JP7607291B2 - Blood Glucose Control System - Google Patents

Description

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT
This invention was made with United States Government support under Contract No. DK120234 awarded by the National Institutes of Health. The United States Government has certain rights in this invention.
INCORPORATION BY REFERENCE OF PRIORITY APPLICATIONS This application was filed on the same October 2, 2020 date as International Application No. PCT/US2020/054025, entitled "BLOOD GLUCOSE CONTROL SYSTEM," which is expressly incorporated herein by reference in its entirety for all purposes. All applications in which a foreign or domestic priority claim is identified in the Application Data Sheet filed with this application are incorporated herein by reference under 37 CFR 1.57.

The present disclosure relates to portable medical devices, such as blood glucose control systems, that provide treatment to a subject.

Continuous delivery pump-driven drug infusion devices typically include a delivery cannula that is placed subcutaneously through the patient's skin at the injection site. The pump draws drug fluid from a reservoir and delivers it to the patient through the cannula. The infusion device typically includes a channel that delivers drug from an entry port to the delivery cannula, resulting in delivery to the subcutaneous tissue layer where the delivery cannula terminates. Some infusion devices are configured to deliver a single drug to a patient, while others are configured to deliver multiple drugs to a patient.

The systems, methods, and devices of the present disclosure each have several innovative aspects, no single aspect of which is solely responsible for all of the desirable attributes disclosed herein. Details of one or more embodiments of the subject matter described herein are set forth in the accompanying drawings and the description below.

Certain embodiments disclosed herein relate to a computer-implemented method for generating an indication of total carbohydrate therapy in a subject over a period of time using a drug pump configured to deliver at least insulin therapy to the subject. The method may be executed by a hardware processor configured to generate a dose control signal for the drug pump configured to deliver at least insulin therapy to the subject. The method may include receiving a glucose level of the subject and determining, at least in part based on the glucose level, that a trigger event has occurred that increases the blood glucose level of the subject. The trigger event may include determining that an imminent risk of hypoglycemia exists in the subject or that an episode of hypoglycemia exists in the subject. The method may further include determining an amount of a counter regulator to respond to the imminent risk of hypoglycemia or the episode of hypoglycemia. Additionally, the method may include determining a dose of carbohydrate therapy based at least in part on the amount of counter regulator. Additionally, the method may include tracking the determined dose of carbohydrate therapy over a period of time that includes a plurality of hypoglycemia risk events or episodes of hypoglycemia to generate an indication of total carbohydrate therapy over the period of time. The method may include outputting an indication of a total carbohydrate regimen.

An additional embodiment of the present disclosure relates to an automated blood glucose control system configured to generate an indication of total carbohydrate therapy over a period of time in a subject. The automated blood glucose control system can include a drug delivery interface configured to operably connect to a drug pump configured to infuse a drug into the subject. The drug can include at least insulin. The automated blood glucose control system may further include a memory configured to store certain computer-executable instructions, and a hardware processor in communication with the memory and configured to execute the certain computer-executable instructions to at least receive a glucose level of the subject, determine that a trigger event has occurred to increase the subject's blood glucose level based at least in part on the glucose level, the trigger event including an imminent risk of hypoglycemia being present in the subject or an episode of hypoglycemia being present in the subject, determine an amount of a counter regulator to respond to the imminent risk of hypoglycemia or the episode of hypoglycemia, determine a dose of carbohydrate therapy based at least in part on the amount of counter regulator, track the determined dose of carbohydrate therapy over a period of time that includes a plurality of hypoglycemic risk events or episodes of hypoglycemia to generate an indication of total carbohydrate therapy over the period of time, and output the indication of total carbohydrate therapy.

Certain embodiments of the present disclosure relate to an automated blood glucose control system configured to generate a backup therapy protocol including insulin therapy instructions derived from autonomously determined doses of insulin. The automated blood glucose control system may include a drug delivery interface configured to operably connect to a drug pump for infusing a drug to a subject. The automated blood glucose control system may further include a memory configured to store certain computer-executable instructions, and a control algorithm configured to receive at least a glucose level signal from a sensor operatively configured to determine a glucose level of the subject, generate a dose control signal using a control algorithm configured to autonomously determine a dose of insulin infused to the subject for purposes of controlling blood glucose of the subject based at least in part on the glucose level signal, track an insulin therapy administered to the subject by the automated blood glucose control system over a tracking period comprising at least one day, and tracking the insulin therapy may include a basal insulin, a correction insulin, and a basal insulin. and a hardware processor configured to store an indication of an autonomously determined dose of insulin delivered to the subject as a bolus or as a mealtime bolus of insulin, generate at least one of a backup infusion therapy protocol or a backup pump therapy protocol including an indication of insulin therapy based at least in part on the insulin therapy administered to the subject over a follow-up period, and output at least one of the backup infusion therapy protocol or the backup pump therapy protocol on a display when the automated blood glucose control system is not providing therapy to the subject to enable maintenance of therapy at a rate determined by the automated blood glucose control system.

An additional embodiment of the present disclosure relates to a computer-implemented method of generating a backup therapy protocol including insulin therapy instructions derived from an autonomously determined dose of insulin determined by an automated blood glucose control system. The method may be executed by a hardware processor of the automated blood glucose control system. The method may include receiving a glucose level signal from a sensor operatively configured to determine a glucose level of the subject, and generating a dose control signal using a control algorithm configured to autonomously determine a dose of insulin to be infused to the subject for controlling blood glucose of the subject based at least in part on the glucose level signal. Further, the method may include tracking an insulin therapy administered to the subject by the automated blood glucose control system over a tracking period including at least one day. Tracking the insulin therapy may include storing an indication of the autonomously determined dose of insulin delivered to the subject. Further, the method may include generating at least one of a backup infusion therapy protocol or a backup pump therapy protocol including insulin therapy instructions based at least in part on the insulin therapy administered to the subject over the tracking period. Additionally, the method may include a step of outputting at least one of a backup infusion therapy protocol or a backup pump therapy protocol on the display when the automatic blood glucose control system is not providing therapy to the subject to enable maintenance of therapy at a rate determined by the automatic blood glucose control system.

Some embodiments of the present disclosure relate to an automated blood glucose control system configured to generate a report of therapy protocol modifications made by a user of the automated blood glucose control system. The automated blood glucose control system may include a drug delivery interface configured to operably connect to a drug pump for infusing a drug to a subject. Additionally, the automated blood glucose control system may include a memory configured to store certain computer executable instructions, stored control parameter values, and a treatment log. The automated blood glucose control system may further include a hardware processor in communication with the memory and configured to execute certain computer-executable instructions to at least receive a glucose level signal from a sensor configured to operate to determine a glucose level of the subject, generate a dose control signal using a control algorithm configured to autonomously determine a dose of insulin to be infused to the subject for controlling blood glucose of the subject based at least in part on the glucose level signal and a control parameter modifiable by user interaction with a control parameter selection interface element, track user modifications to the control parameters over a tracking period including at least one day, tracking the user modifications includes storing in a treatment log whether each of the user modifications includes an increase or decrease in the control parameter from a stored control parameter value and the time at which each of the user modifications occurred, and generate a report of the user modifications to the control parameters, the report including a measure of the frequency of increases and decreases from the stored control parameter values.

Certain embodiments of the present disclosure relate to a computer-implemented method of modifying a therapy provided to a subject using a blood glucose control system. The method may be executed by a hardware processor configured to generate a dose control signal for the blood glucose control system. Additionally, the method may include receiving a glucose level signal from a glucose level sensor operably connected to the subject. Additionally, the method may include causing a first therapy to be delivered to the subject by the blood glucose control system during a first treatment period, the first therapy being delivered based at least in part on a first value of a control parameter used by a control algorithm to generate the dose control signal. The control parameter may be used by the control algorithm to account for insulin accumulation in the subject, thereby controlling the insulin dosing response of the control algorithm to the subject's blood glucose fluctuations as indicated by the glucose level signal. Additionally, the method may include determining a first effect at least in part corresponding to the first therapy. The determining the first effect may include analyzing glycemic control of the subject's blood glucose as indicated by the glucose level signal. Additionally, the method may include autonomously generating a second value of the control parameter. The autonomously generated second value may be determined as a function based on the first value and the first effect. Additionally, the method may include modifying the control parameter from the first value to a second value and causing the blood glucose control system to deliver a second therapy to the subject during the second therapy period. The second therapy may be delivered based at least in part on the second value of the control parameter. Additionally, changing the control parameter may modify the therapy provided to the subject.

An additional embodiment of the present disclosure relates to a computer-implemented method of modifying a therapy provided to a subject using a blood glucose control system. The method may be executed by a hardware processor configured to generate a dose control signal for the blood glucose control system. The method may include causing a first therapy to be delivered to the subject by the blood glucose control system during a first treatment period. The first therapy may be delivered based at least in part on a first value of a control parameter used by a control algorithm to generate the dose control signal. The method may further include determining a first effect corresponding at least in part to the first therapy. The determining the first effect may include receiving a glucose level signal from a glucose level sensor operably connected to the subject. Additionally, the method may include autonomously generating a second value of the control parameter based at least in part on a baseline value of the control parameter and an output of a function defined based on the glycemic control of the subject. The glucose level signal may include an indication of the glycemic control of the subject during the first treatment period. The method may further include modifying the control parameter from a first value to a second value and causing the blood glucose control system to deliver a second therapy to the subject during a second therapy period. The second therapy may be delivered based at least in part on the second value of the control parameter. Changing the control parameter may include modifying the therapy provided to the subject.

Some embodiments of the present disclosure relate to a computer-implemented method of modifying a therapy provided to a subject using a blood glucose control system. The method may be implemented by a hardware processor configured to generate a dose control signal for the blood glucose control system. The method may include causing a first therapy to be delivered to the subject by the blood glucose control system during a first therapy period. The first therapy may be delivered based at least in part on a first value of a control parameter used by a control algorithm to generate the dose control signal. The method may further include determining a first effect corresponding at least in part to the first therapy. The determining the first effect may include receiving a glucose level signal from a glucose level sensor operably connected to the subject. Additionally, the method may include autonomously generating a second value of the control parameter. The autonomously generated second value may be determined as a function based at least in part on a baseline value. Additionally, the method may include modifying the control parameter from a first value to a second value. The method may further include causing a second therapy to be delivered to the subject by the blood glucose control system during a second therapy period. The second therapy may be delivered based at least in part on the second value of the control parameter. Additionally, altering the control parameter may include modifying the therapy provided to the subject. The method may further include determining a second effect corresponding at least in part to the second therapy, and autonomously performing the comparison between the first effect and the second effect without human action. Additionally, the method may include selecting one of the first value of the control parameter or the second value of the control parameter as an active control parameter value based at least in part on the comparison between the first effect and the second effect. Additionally, the method may include configuring the blood glucose control system to provide a therapy to the subject during a third treatment period based at least in part on the active control parameter value. The selection of the active control parameter value may modify the therapy provided to the subject.

The above-described embodiments may also be combined. For example, a single automated blood glucose control system may be configured to implement one or more of the above-described embodiments.

Throughout the drawings, reference numbers are reused to indicate correspondence between referenced elements. The drawings are provided to illustrate certain aspects of the subject matter described herein and are not intended to limit the scope thereof.

1 is an exemplary blood glucose control system that provides blood glucose control via an ambulatory drug pump.

2 is another example of a blood glucose control system that provides blood glucose control via an ambulatory drug pump.

1 is a further example of a blood glucose control system that provides blood glucose control via an ambulatory drug pump.

FIG. 1 is a block diagram of an exemplary blood glucose control system.

FIG. 2 is a block diagram of another exemplary blood glucose control system.

FIG. 2 is a block diagram of another exemplary blood glucose control system.

FIG. 2 is a block diagram of another exemplary blood glucose control system.

FIG. 1 is a schematic diagram of an exemplary glucose control system including an electronic communication interface.

FIG. 1 is a block diagram of an exemplary blood glucose control system in an online mode of operation.

FIG. 1 is a block diagram of an exemplary blood glucose control system in an offline mode of operation.

FIG. 1 is a block diagram of a glucose control system in accordance with certain embodiments.

FIG. 1 is a block diagram illustrating a computer system in accordance with various embodiments.

1 is a flow chart of an exemplary carbohydrate therapy equivalence tracking process in accordance with certain embodiments.

1 is a flow chart of an exemplary back-up therapy protocol generation process in accordance with certain embodiments.

5 is a flowchart of an exemplary control parameter modification tracking process in accordance with certain embodiments.

1 is an exemplary back-up therapy protocol according to certain embodiments.

11 is an exemplary control parameter modification report in accordance with certain embodiments.

12 is an exemplary meal selection report that may be included as part of some implementations of the control parameter modification report of FIG. 11 , according to certain embodiments.

1 is a flow chart of an exemplary automated blood glucose control improvement process in accordance with certain embodiments.

Simulation of subject blood glucose control with Tmax set at 65 minutes.

Simulation of subject blood glucose control with Tmax set at 15 minutes.

Simulation of subject blood glucose control with Tmax set at 130 minutes.

FIG. 2 shows an example of a blood glucose level signal (CGM trace) using a blood glucose control system and some of the parameters associated with glycemic control.

1 is a flowchart of an exemplary automatic blood glucose control improvement process based on an adjustment function, in accordance with certain embodiments.

5A-5C are some examples of statistics that may be generated and utilized by the blood glucose control system as part of a statistical analysis.

1 is a flow chart of an exemplary automated blood glucose control improvement process in accordance with certain embodiments.

Some embodiments described herein relate to drug infusion systems for one or more medications, and components of such systems (e.g., infusion pumps, drug cartridges, cartridge connectors, lumen assemblies, infusion connectors, infusion sets, etc.). Some embodiments relate to methods of manufacturing the infusion systems and their components. Some embodiments relate to methods of using any of the aforementioned systems or components for infusing one or more medications (e.g., medicines, hormones, etc.) into a patient. As an exemplary illustration, the infusion system can include an infusion pump, which can include one or more medication cartridges or can have an integrated reservoir of medication. The infusion system can include a medication cartridge and a cartridge connector, but not a pump. The infusion system can include a cartridge connector and an infusion pump, but not a medication cartridge. The infusion system can include an infusion connector, a lumen assembly, a cartridge connector, an infusion pump, but not a medication cartridge or an infusion set. The blood glucose control system can operate in conjunction with the infusion system to infuse one or more medications, including at least one blood glucose control agent, into a subject. Any feature, structure, component, material, step, or method described and/or illustrated in any embodiment herein may be used in conjunction with or in place of any feature, structure, component, material, step, or method described and/or illustrated in any other embodiment herein. In addition, any feature, structure, component, material, step, or method described and/or illustrated in one embodiment may not be present in another embodiment.

Overview of Blood Glucose Control Systems Blood glucose control systems are used to control the blood glucose level of a subject. The blood glucose control system can include a controller configured to generate a dose control signal for one or more glucose control agents that can be infused into the subject. Glucose control agents include regulators that tend to lower blood glucose levels, such as insulin and insulin analogs, and counter regulators that tend to raise blood glucose levels, such as glucagon or dextrose. A blood glucose control system configured for use with two or more glucose control agents can generate a dose control signal for each of the agents. In some embodiments, the blood glucose control system can generate a dose control signal for a drug even if the drug is not available for dispensing via a drug pump connected to the subject.

The glucose control agent may be delivered to the subject by subcutaneous injection, intravenous injection, or another suitable delivery method. For blood glucose control therapy with a portable drug pump, subcutaneous injection is most common. The portable drug pump 100 is a type of portable medical device, sometimes referred to herein as a portable device, portable drug device, mobile portable device, or AMD. Portable medical devices include portable drug pumps and the like that are configured to be carried by the subject and deliver therapy to the subject.

In some examples, the ambulatory medical device (AMD) is an electrical stimulation device and the delivery of therapy includes providing electrical stimulation to the subject. One example of an electrical stimulation device is a cardiac pacemaker. A cardiac pacemaker generates electrical stimulation of the myocardium to control the rhythm of the heart. Another example of an electrical stimulation device is a deep brain stimulator for treating Parkinson's disease or movement disorders.

FIGS. 1A-1C show an example of a blood glucose control system that provides blood glucose control via an ambulatory drug pump connected to a subject. In FIG. 1A, a drug pump 100 is connected to an infusion site 102 using an infusion set 104. The drug pump has an integrated pump control 106a that allows a user to view pump data and change treatment settings via user interaction with the pump control 106a. A glucose level sensor 110 generates a glucose level signal that is received by the blood glucose control system.

In FIG. 1B, drug pump 100 communicates with external electronics 108 (e.g., a smartphone, etc.) via a wireless data connection. At least some of pump controls 106a and 106b can be operated via user interaction with user interface elements of external electronics 108. Glucose level sensor 110 can also communicate with drug pump 100 via a wireless data connection.

In FIG. 1C, drug pump 100 includes an integrated cannula for insertion into infusion site 102 without a separate infusion set. At least some of pump controls 106b can be operated via user interaction with user interface elements of external electronics 108. In some cases, pump controls can be operated via user interaction with user interface elements generated by a remote computing environment (not shown), such as, for example, a cloud computing service, that connects to drug pump 100 via a direct or indirect electronic data connection.

A glucose control system typically includes a user interface configured to provide one or more of treatment information, glucose level information, and/or treatment control elements that allow the treatment settings to be changed through user interaction with interface controls. The user interface can be implemented via electronics including a display and one or more buttons, switches, dials, capacitive touch interfaces, or touch screen interfaces. In some embodiments, at least a portion of the user interface is integrated with a portable drug pump that can be tethered to the subject's body via an infusion set configured to facilitate subcutaneous injection of one or more glucose control agents. In certain embodiments, at least a portion of the user interface is implemented via electronics that are separate from the portable drug pump, such as a smartphone.

2A-2D show block diagrams illustrating an exemplary configuration of a glucose control system 200. As shown in FIG. 2A, the glucose control system 200a can include a controller 202a having an electronic processor 204a and a memory 210a storing instructions 208a executable by the processor 204a. The controller 202a and the pump 212 can be integrated into an ambulatory medical device (AMD) 100. The AMD 100 can include a transceiver 214a for wireless digital data communication with external electronics. When the instructions 208a stored in the memory 210a are executed by the electronic processor 204a, the controller 202a can implement at least a portion of a control algorithm that generates a dose control signal for one or more glucose control agents based on the subject's time-varying glucose level and one or more control parameters. The dose control signal, when delivered to the pump 212, results in a dosing action that controls the subject's blood glucose.

As shown in FIG. 2B, the glucose control system 200b can operate at least in part through the execution of instructions 208b by an electronic processor 204b of an electronic device 108 separate from the portable medical device 100. The electronic device 108 can include a transceiver 214b capable of establishing a wireless digital data connection to the AMD 100, and the controller 202b can implement at least a portion of a control algorithm through the execution of instructions 208b stored in memory 210b. When the instructions 208b stored in memory 210b are executed by the electronic processor 204b, the controller 202b can implement at least a portion of a control algorithm that generates a dose control signal for one or more glucose control agents based on the subject's time-varying glucose level and one or more control parameters. The dose control signal, when delivered to the pump 212, results in a dosing action that controls the subject's blood glucose. In some embodiments, the dose control signal is transmitted from the device transceiver 214b to the AMD transceiver 214a via a short-range wireless data connection 216. The AMD 100 receives dose control signals and passes these signals to the pump 212 for dosing operations.

As shown in FIG. 2C, the glucose control system 200c can operate at least in part through the execution of instructions 208c on an electronic processor 204c integrated with a remote computer 206, such as a cloud service. When the instructions 208c stored in the memory 210c are executed by the electronic processor 204c, the controller 202c can implement at least a portion of a control algorithm that generates dose control signals for one or more glucose control agents based on the subject's time-varying glucose level and one or more control parameters. The dose control signals, when delivered to the pump 212, result in a dosing action that controls the subject's blood glucose. In some embodiments, the dose control signals are transmitted from the remote computer WAN connection interface 220c to the AMD WAN connection interface 220a via the end-to-end wireless data connection 218. The AMD 100 receives the dose control signals and passes them to the pump 212 for dosing action.

As shown in FIG. 2D, the glucose control system 200d can have two or more controllers 202a, 202b, 202c that cooperate to generate a dose control signal for dosing operation by the pump 212. The remote computer 206 can transmit or receive data or instructions passed to the WAN connection interface 220c via the WAN wireless data connection 218 to the WAN connection interface 220b of the electronics 108. The electronics 108 can transmit or receive data or instructions passed to the transceiver 214b via the short-range wireless data connection 216 to the transceiver 214a of the AMD 100. In some embodiments, the electronics can be omitted and the controllers 202a, 202c of the AMD 100 and the remote computer 206 cooperate to generate the dose control signal that is passed to the pump 212. In such embodiments, the AMD 100 may have its own WAN connection interface 220a to support a direct end-to-end wireless data connection to the remote computer 206.

As shown in FIG. 3, in some embodiments, the glucose control system 200 includes circuitry implementing an electronic communications interface (ECI) 302 configured to transmit and receive electronic data from one or more electronic devices. The ECI includes a sensor interface 304 configured to receive a glucose level signal from a sensor 110, such as a continuous glucose monitor (CGM). Some CGMs generate a glucose level signal at fixed measurement intervals, such as 5-minute intervals. The sensor 110 can be operatively connected to a subject to generate a glucose level signal corresponding to the subject's blood glucose estimate or measurement. The glucose level signal can be used by the controller 202 to generate a dose control signal. The dose control signal can be provided to the pump 212 via a pump interface 306. In some embodiments, the sensor interface 304 connects to the sensor 110 via a short-range wireless connection 308. In some embodiments, the pump interface 306 connects to the pump 212 via a short-range wireless connection 310. In other embodiments, the pump interface 306 connects to the pump 212 via a local data bus, such as when the controller 202, ECI 306, and pump 212 are integrated into the AMD 100.

The controller can be configured to generate the dose control signal using a control algorithm that generates at least one of the basal dose, the correction dose, and/or the meal dose. Examples of control algorithms that can be used to generate these doses are disclosed in U.S. Patent Application Publication Nos. 2008/0208113, 2013/0245547, 2016/0331898, and 2018/0220942 (referred to herein as "Controller Disclosures"), the entire contents of which are incorporated herein by reference and made a part of this specification. The correction dose can include a regulator or counter-regulator and can be generated using a model predictive control (MPC) algorithm such as those disclosed in the Controller Disclosures. The basal dose can include a regulator and can be generated using a basal control algorithm such as those disclosed in the Controller Disclosures. The meal dose may include a modifier and may be generated using a meal control algorithm as disclosed in the Controller Disclosures. Additional aspects and improvements to at least some of these controllers are disclosed herein. The dose control signal may be transmitted to the infusion motor 306 via the ECI 302, or may be transmitted to the infusion motor 306 via electrical conductors if the controller 202a is integrated into the same housing as the infusion motor 306.

As shown in FIG. 4A, the controller 400 can be configured to operate in an "online mode" during times when the controller receives a glucose level signal 402 from the sensor 110. In the on-line mode, the control algorithm generates a dose control signal 404 that implements a regular correction dose based on the value of the glucose level signal 402 and the control parameters of the control algorithm. The pump 212 is configured to deliver at least the correction dose and the basal dose to the subject without substantial user intervention while the controller 400 remains in the on-line mode.

As shown in FIG. 4B, the controller 400 can be configured to operate in an “offline mode” during times when the controller does not receive a glucose level signal 402 from the sensor 110, at least during times when a glucose level signal 402 is expected but not received. In the offline mode, the control algorithm generates a dose control signal 404 that implements a correction dose in response to an isolated glucose measurement 406 (e.g., a measurement obtained from the subject using a glucose test strip) and based on control parameters of the control algorithm. The pump 212 is configured to deliver a basal dose to the subject without substantial user intervention and can deliver a correction dose to the subject in response to the isolated glucose measurement 406 while the controller 400 remains in the offline mode.

5 shows an automatic glucose control system 510 for regulating blood glucose levels in an animal subject (subject) 512, which may be a human. The automatic glucose control system 510 is an example of a drug infusion system and may include any of the embodiments described above for the drug infusion system.

The subject 512 may receive a dose of insulin from one or more delivery devices 514, e.g., an infusion pump connected by a catheter to the subcutaneous space of the subject 512. As described below, the delivery device 514 may also deliver a counterregulator or hyperglycemic agent, such as glucagon or dextrose, to control blood glucose levels under certain circumstances. For delivery of both insulin and a counterregulator (e.g., glucagon), the delivery device 514 may be a mechanically driven infusion mechanism having dual cartridges for insulin and a counterregulator, respectively. Although specific reference is made herein to glucagon, it should be understood that this is for convenience only and that other counterregulators (e.g., dextrose) may be used. Similarly, the term "insulin" herein should be understood to encompass all forms of insulin-like substances, including natural human or animal insulin, as well as any of the various forms of synthetic insulin (commonly referred to as "insulin analogs").

For online or autonomous operation, the glucose sensor 516 is operably coupled to the subject 512 and continuously samples the glucose level of the subject 512. In some cases, the glucose sensor 516 may be referred to as a continuous glucose monitoring (CGM) sensor, which may continuously or periodically measure or sense the blood glucose level of the subject 512 at least over a period of time. Sensing may be accomplished in a variety of ways, and generally involves some form of physical coupling 521 between the subject 512 and the glucose sensor 516. The controller 518 may control operation of the delivery device 514 in response to a glucose level signal 519 from the glucose sensor 516 and according to programmed input parameters (PARAMS) 520, which may be provided by a user, such as the subject 512, a parent or guardian of the subject 512, or a health care provider (e.g., a clinician or physician). One input parameter for automatic operation may include the weight of the subject 512. In some cases, the glucose control system 510 can provide effective automatic control without receiving explicit information regarding either meals that the subject 512 has consumed or any other "feed-forward" information, which is achieved in part by adaptive aspects to the operation of the controller 518. In other cases, the glucose control system 510 can use received information regarding either meals that the subject has consumed or will consume, or other "feed-forward" information, to modify blood glucose control and/or insulin or counter-regulator delivery.

The controller 518 is an electrical device having control circuitry that provides the operational functions described herein. In one embodiment, the controller 518 may be realized as a computerized device (e.g., a hardware processor) having computer instruction processing circuitry that executes one or more computer programs, each of which includes a respective set of computer instructions. In some cases, the processing circuitry generally includes one or more processors 530, along with memory 540 and input/output circuitry 532 coupled to or in communication with the processor 530, where the memory 540 stores computer program instructions and data, and the input/output circuitry 532 can provide an interface to external devices, such as the glucose sensor 516 and the delivery device 514. In some cases, the input/output circuitry 532 can provide a user interface or operate with one or more processors (e.g., the controller 518 or a separate processor 530 included in the glucose control system 510 or a separate computing system, such as a smartphone, laptop computer, desktop computer, smartwatch, etc.) to provide a user interface to a user (e.g., the subject 512, a parent or guardian, or a clinician). In some cases, the input/output circuitry 532 may include a touch screen and/or a touch screen controller 538 configured to control a touch screen (not shown).

In some cases, the controller 518 may perform all of the functions of the glucose level control system 510. In such cases, the processor 530 may be optional or omitted. In other cases, the controller 518 may perform at least the automated blood glucose control of the subject 512, and one or more separate processors 530 may perform one or more additional operations of the blood glucose control system 510 (or drug pump), such as tracking the occurrence of hyperglycemic or hypoglycemic events or risk events, outputting data to a user, controlling or initiating communication with another computing system, regulating access to the glucose level control system 510, or other operations unrelated to the operation of the drug pump or delivery device 514.

The input/output circuitry 532 can control communications with one or more other computing systems and/or users. In some cases, the input/output circuitry 532 can include one or more separate interface circuits or controllers to facilitate user interaction and/or communication. For example, the input/output circuitry 532 can include a user interface circuitry 534, a network interface circuitry 536, and/or a touch screen controller 538.

The user interface circuitry 534 may include any circuitry or processor capable of outputting a user interface to a user and/or receiving user input from a user via a user interface. The user interface circuitry 534 may receive one or more signals from the processor 530 corresponding to a user interface. The user interface circuitry 534 may control a display to present a user interface to a user based on one or more signals received from the processor 530. Additionally, the user interface circuitry 534 may include any circuitry capable of receiving signals corresponding to a user's interaction with the user interface and providing the signals to the processor 530 and/or the controller 518 for further processing. In some cases, the user interface circuitry may be replaced by a touchscreen controller 538 capable of controlling a touchscreen interface. In other cases, the touchscreen controller 538 may be added to the user interface circuitry 534.

The network interface circuitry 536 may include any circuitry that enables communication with a wired or wireless network. The network interface circuitry 536 may include one or more network interface cards and/or radios (e.g., a Bluetooth radio, a Bluetooth Low Energy (BLE) radio, a 4g LTE radio, a 5G radio, a ND-LTE radio, etc.).

Memory 540 may include non-volatile memory and/or volatile memory. Non-volatile memory may include flash memory or solid state memory.

The control system 510 can also operate in an offline manner, used to provide delivery of insulin (and potentially glucagon) independent of or without receipt of a glucose level reported by the sensor 516. For example, if the sensor 516 needs replacement, is not properly connected to the subject 512, or is defective, the glucose control system 510 can operate in an offline manner without input from the sensor 516. Thus, the overall operation may be divided into online periods, each including a series of sampling intervals, when the glucose signal (level) 519 is available, and offline periods, each including a series of sampling intervals, when the glucose signal (level) 519 is not available completely or intermittently. The following description uses the terms "online" and "offline" for these periods. Also, offline operation may be selected by the user for any reason, even if the glucose level signal 519 is available for use.

User control inputs (USER CNTLs 523) may be provided via some type of local or remote user interface. In some embodiments, the user interface may resemble that of a conventional insulin pump or similar device, for example, by including control buttons for commanding the delivery of a bolus and perhaps a small display. In other embodiments, the system may have a wired or wireless interface to a remote device that may incorporate a more fully featured user interface, such as a smartphone, a smartwatch, a laptop computer, a desktop computer, a cloud computing service, or other wearable or computing device. In some cases, the wireless interface may provide access to a local area network, such as a personal home network, an enterprise network, etc. Alternatively, or in addition, the wireless interface may provide a direct connection between local devices available to the user (e.g., via Bluetooth or other short-range communication technology). In some cases, the wireless interface may provide access to a wide area network, such as, but not limited to, the Internet. For example, the wireless interface may include a cellular interface that allows access to a network via a 4G or 5G cellular connection. In some cases, the cellular interface may be a low-power interface, such as narrowband LTE or other Internet of Things (IoT) interfaces.

In the offline mode, the glucose sensor 516 may be absent, non-functional, or not coupled to the subject 512. Thus, in the offline mode, the blood glucose signal 519 may not be available to control automated operation. In some cases, a user may provide one or more blood glucose measurements to the control system 510 to facilitate automated operation of the control system 510. These measurements may be provided for a particular time period. Alternatively, or in addition, the glucose control system 510 may use treatment history and/or a history of previous blood glucose control measurements to facilitate automated operation of the control system 510, at least for a particular time period.

The description herein refers to a "user" as a source of user control input 523. A "user" as used herein may be the subject 512, a parent or guardian of the subject 512, a health care provider (e.g., a clinician, physician, or other person who may provide health care to the subject), or any other user who may be authorized to help manage the treatment of the subject 512. In certain embodiments, the glucose level control system 510 is a personal device worn by the subject 512 for continuous glucose control. In some such embodiments, the user and the subject 512 may be the same person. In other embodiments, there may be another person involved in the care of the subject 512 and providing control input, and in such embodiments, the other person has the role of a user.

Exemplary Controller for a Blood Glucose Control System FIG. 6 illustrates an exemplary structure of a controller 518 according to certain embodiments. The controller 518 illustrated in FIG. 6 may represent a physical structure having different controllers or processors, or a logical structure implemented by one or more physical processors. In other words, a single processor may be used to implement each of the controllers illustrated in FIG. 6, each controller may be implemented by its own processor, or a particular processor may implement multiple, but not necessarily all, of the controllers illustrated in FIG. 6 as part of the controller 518. Additionally, although the controllers in FIG. 6 are illustrated as part of the controller 518, in some implementations one or more of the controllers may be separate from the controller 518.

The controller 518 may include four separate controllers: a glucagon (or counter regulator) controller 622, a basal insulin controller 624, a correction insulin controller 626, and a priming insulin controller 628. The basal insulin controller 624 includes a nominal rate controller 630 and a modulation controller 632. As shown, the glucagon controller 622 generates a glucagon dose control signal 634 that is provided to the glucagon delivery device 514-1. The respective outputs 636-640 from the controllers 624-628 may be combined to form an overall insulin dose control signal 642 that is provided to the insulin delivery device 514-2. As shown, the output signal 636 from the basal insulin controller 624 may be formed by a combination of the respective outputs of the nominal rate controller 630 and the modulation controller 632. The insulin delivery devices 514-2 may include devices tailored to deliver different types and/or amounts of insulin, and the exact configuration may be known to and/or under the control of the controllers 624-628. For ease of explanation, a collection of one or more insulin delivery devices 514-2 will be referred to hereinafter in the singular as the insulin delivery device 514-2.

6 also shows various controller input/output signals, including glucose level signal 519, parameters 520, and user input 523, as well as a series of inter-controller signals 644. Inter-controller signals 644 allow communication of information from one controller where the information is created or generated to another controller where the information can be used in the control function of that controller.

The controllers 622-628 can operate in either an online/automatic mode or an offline mode. In the automatic mode, the correction controller 626 performs the correction process described in U.S. Pat. No. 7,806,854, the contents of which are incorporated herein by reference in their entirety.
The basal controller 624 and the priming insulin controller 628 can perform adaptive automatic control as described in International Patent Application Publication No. 2012/058694 A2, the contents of which are incorporated herein by reference in their entirety. The controllers 622-628 generally use a control method or algorithm that includes control parameters that are mathematically combined with the reported glucose values to generate an output value that is converted (directly or through additional conditioning) into a dose control signal 634, 642. For example, the control scheme described in U.S. Pat. No. 7,806,854 includes a generalized predictive control (GPC) method that incorporates various control parameters. The control algorithms are generally adaptive, meaning that the control parameters are dynamically adjusted during operation to reflect changing operating conditions and "learning"aspects; by monitoring its own operation, the algorithm adjusts its operation to more specifically suit the individual user, increasing the effectiveness of the algorithm and reducing or avoiding the need for additional explicit input information about the user. It should be noted that the input parameters 520 may form part of the control parameters used by the control algorithm, other control parameters being internal parameters according to the algorithm's specifications, selected ones of which are dynamically adjusted to achieve adaptation of the control algorithm.

One feature of operation is that the controller can learn from online operation from the most recent past period and use that learning during offline operation. U.S. Pat. No. 10,543,313, the contents of which are incorporated herein by reference in their entirety, describes two methods that can be used independently or together in offline operation. The first method automatically calculates the correct size of a correction bolus of insulin upon receipt of an isolated glucose measurement, which is then administered by the system in response to a user control input. The second method automatically calculates the correct size of a meal bolus of insulin and administers it in response to a user control input. Both methods utilize information gained from past periods of online operation to automatically calculate the correct value, eliminating the need for the user to perform calculations or provide correction factors.

Carbohydrate Therapy Equivalence Tracking Hyperglycemia is a condition that occurs when the level of sugar or glucose in the blood exceeds a certain level (e.g., 180 mg/dL). This condition can occur in diabetic patients. To help reduce the occurrence of hyperglycemia, a subject can use an automated blood glucose control system that can automatically provide insulin to the subject using a drug pump. The administered insulin can help control the subject's blood glucose level by consuming the subject's glucose.

Hypoglycemia is a condition that occurs when the level of sugar or glucose in the blood is below a certain level (e.g., 70 mg/dL). This condition can have adverse consequences including loss of consciousness, seizures, and death. The levels of blood glucose that result in hyperglycemia and hypoglycemia can vary from patient to patient. To reduce the risk of hypoglycemia, subjects may consume carbohydrates to raise blood glucose. Due to the serious consequences associated with hypoglycemic events, subjects typically consume carbohydrates that are quickly metabolized. These carbohydrates are often unhealthy, but favor the occurrence of hypoglycemic events. For example, carbohydrates may include candy bars that are high in refined sugars.

A bihormonal glucose control system can reduce the risk of hypoglycemia by including, in addition to insulin, a counterregulator (e.g., glucagon) that can be administered to the subject if blood glucose levels are too low (e.g., below 50 mg/dL). For subjects without a bihormonal glucose control system, it may be useful to understand the reduction in carbohydrate therapy or carbohydrate intake to address hypoglycemic events or potential hypoglycemic events, which can be achieved by switching to a bihormonal glucose control system. Additionally, it may be useful for subjects with a bihormonal glucose control system to understand the reduction in carbohydrate therapy that is obtained by having a bihormonal glucose control system. For example, understanding the intake or avoidance of carbohydrate therapy may be important in monitoring the subject's nutritional intake. Monitoring nutrition during intake is important for all people, but is particularly important for diabetics, as they must balance eating healthily with ensuring that blood glucose is maintained within a certain range to avoid both hyperglycemia and hypoglycemia.

The present disclosure relates to a system capable of executing a computer-implemented method for generating an indication of total carbohydrate therapy over a period of time in a subject using a drug pump configured to deliver at least insulin therapy to the subject. The system may be an automated blood glucose control system (e.g., glucose level control system 510) that includes a hardware processor (e.g., controller 518) for determining a dose control signal to provide a drug pump (e.g., delivery device 514). In some cases, the drug pump may be configured to deliver both insulin therapy and counterregulator (e.g., glucagon) therapy. Alternatively, the system may be separate from the blood glucose control system but may receive blood glucose information from the blood glucose control system. For example, the system may be a personal computing system or a cloud computing system that may receive blood glucose information from the blood glucose control system.

The system may receive or determine a glucose level of a subject (e.g., subject 512). The glucose level of the subject may be determined based on a signal (e.g., a glucose level signal) received from a continuous glucose monitoring (CGM) sensor (e.g., glucose sensor 516) corresponding to the glucose level of the subject. In some cases, the glucose level may be determined from an isolated glucose measurement, such as may be obtained using a glucose measurement kit and/or glucose paper.

Using at least the subject's glucose level, the system can determine whether a trigger event has occurred that will increase the subject's blood glucose level. The trigger event can include the occurrence of a hypoglycemic event that exceeds a risk threshold within a particular time period, or a blood glucose level that indicates a risk of a hypoglycemic event occurring. If the subject's glucose level falls below a glucose threshold, the risk of a hypoglycemic event may be determined. This glucose threshold may vary from subject to subject, and in some cases may be specified by the subject or a caregiver (e.g., a healthcare provider, parent, or guardian). Thus, in some cases, different trigger events may be defined based on the subject's risk tolerance for the occurrence of hypoglycemia, or possible different preferences for the amount of blood glucose present in the subject. Different subjects may prefer to maintain or attempt to maintain blood glucose at different levels, for example, due to differences in activity levels or metabolism by different subjects. Determining the risk of occurrence of a hypoglycemic event can include receiving an indication of the risk of hypoglycemia from the glucose sensor or a prediction of the glucose level at a future time. For example, a determination that the risk of hypoglycemia is imminent may include a determination that the subject's blood glucose level is expected to fall below 60 mg/dl within the next 5-15 minutes.

In response to a trigger event, the system can determine an amount of counter regulator to administer, or an amount of counter regulator that would be administered if the blood glucose control system included the capability to administer a counter regulator. In some cases, the counter regulator is administered, for example, by the automated blood glucose control system. In other cases, the counter regulator is not administered. For example, the automated blood glucose control system may not be able to deliver a counter regulator. As another example, the automated blood glucose control system may be able to deliver a counter regulator but may not have a dose of counter regulator available.

The system can determine a corresponding amount of carbohydrate using an indication of the counterregulator that has been administered or will be administered. The corresponding amount of carbohydrate can indicate an amount of carbohydrate ingested to prevent a hypoglycemic event, to reduce the risk of a hypoglycemic event, or in response to the occurrence of a hypoglycemic event. Alternatively, or in addition, the corresponding amount of carbohydrate can indicate an amount of carbohydrate that would have been ingested if the counterregulator was not available.

The corresponding amount of carbohydrate can be obtained from a mapping between the amount of counterregulator and the amount of carbohydrate. In some cases, the mapping may be based on a measured equivalence between carbohydrate and counterregulator. Alternatively, or in addition, it may be a mapping between a determined amount of counterregulator and an amount of carbohydrate that the subject would normally ingest if it was determined that a hypoglycemic event was likely to occur.

The mapping may be performed by a look-up table that maps different amounts of counter regulator to different corresponding amounts of carbohydrate. In some cases, a single amount of counter regulator may be mapped to different amounts of carbohydrate depending on the type of carbohydrate ingested (e.g., simple carbohydrate vs. complex carbohydrate, or the type of candy bar ingested, etc.). Alternatively, the mapping may be based on a formula that converts the amount of counter regulator to the amount of carbohydrate based on a correspondence between the amount of counter regulator and the amount of carbohydrate. The determination of the relationship between the counter regulator and the carbohydrate may be based on a clinical test that compares the carbohydrate to the counter regulator (e.g., glucagon, dextrose, etc.). Additionally, the mapping may be based at least in part on the subject's preferred carbohydrate source and/or the subject's characteristics (e.g., body weight).

In some cases, the system may track the number of hypoglycemic events or the occurrence of triggers indicating an imminent risk of a hypoglycemic event within a particular time period. The time period may be a day, week, month, year, or any other time period in which it is desirable to determine the relationship between carbohydrates ingested or avoided based on the unavailability or availability of counterregulators. In some cases, carbohydrate therapy tracking may be based on the number of hypoglycemic events or hypoglycemic risk events instead of or in addition to the time period.

For each occurrence of a hypoglycemic event or a trigger indicating an imminent risk of a hypoglycemic event, the system can determine an estimate of carbohydrate therapy saved, or an estimate of what would have been saved by accessing a counterregulator. The system can generate a report for the time period showing the total carbohydrate saved, or the time period that would have been saved by accessing a counterregulator. The report may include an aggregate or total carbohydrate therapy required or saved during the time period. The time period may be a day, week, month, year, or since a particular time (e.g., since the subject began using the system). Additionally, the report may indicate the type of carbohydrate typically consumed by the subject in response to a hypoglycemic event or risk of an imminent hypoglycemic event. The report may be presented to the subject, a health care provider, and/or the subject's parent or guardian. The health care provider can use the report to assist in the care of the subject. For example, the health care provider can use the report to generate a nutrition plan for the subject that takes into account carbohydrates consumed to maintain blood glucose levels within a desired or set range.

The report may include the extent of carbohydrate therapy that may be avoided or ingested to address the risk of a hypoglycemic event. Additionally, the report may include the amount of calories saved or not ingested, the amount of sugars avoided, the amount of food not ingested, the weight gain that may have been avoided, etc. based on the use of counterregulators to replace carbohydrate therapy.

Carbohydrate Therapy Equivalence Tracking Process FIG. 7 presents a flowchart of an exemplary carbohydrate therapy equivalence tracking process 700, according to certain embodiments. Process 700 may be performed by any system capable of tracking a subject's glucose levels over time and identifying a hypoglycemic event or occurrence when the risk of a hypoglycemic event meets a threshold (e.g., when the risk of a hypoglycemic event meets or exceeds a certain probability). For example, process 700 may be performed by one or more elements of glucose level control system 510. In some cases, at least certain operations of process 700 may be performed by a separate computing system that receives an indication of the subject's 512 blood glucose level and/or an indication of a hypoglycemic event (or identified as a hypoglycemic risk event that exceeds a threshold) from glucose level control system 510. While one or more different systems can perform one or more operations of process 700, for ease of explanation and not to limit the present disclosure, process 700 will be described with respect to a particular system.

The process 700 begins at block 702, where the glucose level control system 510 receives a glucose level of the subject 512. Receiving the glucose level may include receiving a glucose level signal corresponding to the glucose level of the subject. The glucose level signal may be received from a glucose sensor 516 (e.g., a CGM sensor). Alternatively, or in addition, the glucose level may be received from a user providing the glucose level to the glucose level control system 510 via a user interface, such as a user interface generated by the processor 530 that may be output on a touchscreen by the touchscreen controller 538. The glucose level received from the user may be a glucose level measured using an alternative sensor or measurement mechanism (e.g., a diabetic measurement strip) that may be used in place of the glucose sensor 516.

At block 704, the glucose level control system 510 determines that a trigger event has occurred that will increase the blood glucose level of the subject 512 based at least in part on the glucose level. The trigger event may include a determination that a hypoglycemic event or hypoglycemic episode exists or is occurring in the subject 512. Alternatively or in addition, the trigger event may include a determination that there is an imminent risk of hypoglycemia in the subject 512 or an imminent risk that a hypoglycemic event will occur in the subject 512 within a particular time period. The determination of a hypoglycemic event or risk of occurrence of a hypoglycemic event may be determined by comparing the glucose level of the subject to a glucose threshold value. Alternatively or in addition, the determination of a hypoglycemic event or risk of occurrence of a hypoglycemic event may be determined by comparing a trend and/or rate of change (e.g., rate of decrease) of the glucose level to a threshold value. In some cases, a particular blood glucose level and a trend of blood glucose levels may be combined to determine the risk of hypoglycemia. For example, if the glucose level is low (e.g., below a particular threshold such as 60 mg/dL), but the determined trend of the glucose level is upward, the risk of hypoglycemia may be lower than if the glucose level is above the threshold, but the determined trend of the glucose level is downward toward the threshold. In some cases, the threshold used to determine whether a hypoglycemic event is occurring or to determine that there is a risk above the threshold of hypoglycemia occurring may vary based on physiological characteristics of the subject 512. The physiological characteristics may be based on physiological characteristics associated or shared among a group of patients (e.g., gender, age, weight), or may be specific to a particular subject 512. For example, the threshold associated with the risk of hypoglycemia may be determined based on the glucose level of the subject 512 determined during a previous occurrence of hypoglycemia as determined by the glucose level control system 510, or based on clinical data specific to the subject 512.

In response to the trigger event at block 704, the glucose level control system 510 determines the amount of counter regulator at block 706. The glucose level control system 510 can determine the amount of counter regulator based at least in part on the blood glucose level of the subject 512, the amount or percentage of risk of hypoglycemia occurring (e.g., a 99% risk or probability of hypoglycemia may induce a greater counter regulator dose than a 75% risk or probability of hypoglycemia), physiological characteristics of the subject 512, the tendency of the blood glucose level of the subject 512, or the type of counter regulator.

In some cases, the glucose level control system 510 can deliver a determined amount of a counter regulator to the subject 512 using the delivery device 514-1. The counter regulator may be delivered to the subject 512 in response to an imminent risk of hypoglycemia or an episode of hypoglycemia and/or in response to the glucose level meeting or falling below a threshold glucose level. The determination of whether to deliver the threshold glucose level or a counter regulator may be based on the physiological characteristics of the subject 512 and/or the subject's 512's risk tolerance for a hypoglycemic event. It should be understood that in this context, risk tolerance does not generally refer to a user's subjective propensity for risk. Instead, risk tolerance is typically an objective determination of the likelihood that the subject 512 will have a hypoglycemic event or experience symptoms of hypoglycemia when the subject's 512 blood glucose level is at a particular level. This risk tolerance may be determined based on the subject's 512 history of hypoglycemia, or lack thereof, at a particular blood glucose level and/or based on clinical data obtained about the subject 512.

In other cases, the glucose level control system 510 may not deliver a counter regulator to the subject 512, for example, because the glucose control system 510 may not be able to deliver the counter regulator, or because a cartridge holding the counter regulator may be empty or may have less than a threshold amount of counter regulator remaining.

At block 708, the glucose level control system 510 determines a dose of carbohydrate therapy based at least in part on the counterregulator. Carbohydrate therapy can refer to carbohydrates ingested to prevent or respond to the occurrence of hypoglycemia. Carbohydrates can include any type of carbohydrate that the subject 512 may ingest to prevent or respond to the occurrence of hypoglycemia, typically including fast acting carbohydrates, which may include carbohydrate foods that are easily converted to sugar in the human body. For example, carbohydrates may be candy bars, sodas, fruit juices, or other foods that may have a lot of sugar or refined sugars.

Determining the dose of carbohydrate therapy may include accessing a mapping between counter regulators and carbohydrates. This mapping may be stored in and accessed from memory 540 and/or from another computing device. The glucose level control system 510 may determine the dose of carbohydrate therapy based at least in part on the mapping and the amount of counter regulator. In some cases, the mapping may vary based on the type of counter regulator and/or the type of carbohydrate. The type of counter regulator may be identified by the user or may be automatically determined based on a drug cartridge installed or inserted into the glucose level control system 510. Additionally, the type of carbohydrate may be specified by the user and may include an identification of the type of carbohydrate typically consumed by the subject 512 when responding to the occurrence or risk of occurrence of hypoglycemia. For example, when responding to the occurrence or risk of occurrence of hypoglycemia, the user may specify via the user interface whether the subject typically consumes a candy bar or fruit juice or the size of the carbohydrate typically consumed.

In some cases, the mapping between counterregulators and carbohydrates may be generated based on a clinical comparison of the counterregulator to carbohydrates. Alternatively, or in addition, the mapping may be based at least in part on physiological characteristics of the subject 512.

The mapping may be stored in a look-up table or other data structure that can store the relationship between different carbohydrates and counter regulators. The mapping may be between different amounts and/or types of carbohydrates and different amounts and/or types of counter regulators. Alternatively, or in addition, the mapping may be a formula that relates carbohydrates to counter regulators, or vice versa. For example, the glucose level control system 510 may use the determined amount of counter regulator as an index into a look-up table to determine the corresponding amount of carbohydrate. Alternatively, the glucose control system 510 may apply the determined amount of counter regulator to a formula to calculate the corresponding amount of carbohydrate. The formula may be generated based on the type of counter regulator and/or carbohydrate, the physiological characteristics of the user, and/or clinical data.

In some cases, the mapping may vary based on the glucose level control system 510. For example, the glucose level control system 510 may include a first mapping if the glucose level control system 510 (or its drug pump) is a bi-hormonal pump configured to deliver insulin and counter-regulator therapy to the subject, and may include a second mapping if the glucose level control system 510 is not configured to deliver counter-regulator therapy to the subject 512. In some cases, the glucose level control system 510 may store both mappings in the memory 540. For example, the glucose level control system 510 may use the first mapping if a counter-regulator is available, and may use the second mapping if a counter-regulator is not available. The mapping may vary for several reasons, including that the bi-hormonal glucose level control system 510 may more precisely control the occurrence of hypoglycemic events due to the availability of a counter-regulator, and therefore may change the frequency and type of carbohydrates that the subject may ingest.

At block 710, the glucose level control system 510 outputs an indication of the carbohydrate therapy dosage. Outputting the indication of the carbohydrate therapy dosage may include outputting the indication of the carbohydrate therapy dosage on a display for presentation to a user. Additionally, the indication of the carbohydrate therapy dosage may be transmitted to another computing system for displaying or aggregating other therapy data associated with the subject 512, such as therapy data used by a clinician to assist in managing the care of the subject 512. In some cases, the indication of the carbohydrate therapy dosage may be included in a report corresponding to the care of the subject 512.

In certain embodiments, the operations of process 700 are performed or repeated over a time period. For example, the operations associated with blocks 702-708 may be repeated one or more times over the time period. In such a case, the determined carbohydrate therapy dose may be aggregated for the time period to determine a total carbohydrate therapy for the time period. Additionally, block 710 may include outputting an indication of the carbohydrate therapy dose for each individual time period for which the carbohydrate therapy dose is determined and/or an aggregate of the determined doses of carbohydrate therapy for the time period. The time period may be any time period. For example, the time period may be a day, week, month, year, or any other time period since the subject 512 began using the glucose level control system 510 and since the user gained or ceased to gain access to a counter regulator. In some cases, the time period is defined by the occurrence of a hypoglycemic event or the occurrence of a risk of hypoglycemia that meets a threshold. For example, the time period may be a time period associated with the occurrence of 5, 10, 15, 100, or any other number of hypoglycemic events or a risk of hypoglycemia that meets a threshold.

The indicator of total carbohydrate therapy can correspond, for example, to a reduction in carbohydrates ingested by the subject 512 due to the availability of a counter regulator to the glucose level control system 510 and thus to the subject 514. Thus, the indicator of total carbohydrate therapy can correspond to a reduction in carbohydrates achievable due to the availability of a counter regulator to the subject 512. Additionally, the indicator of total carbohydrate therapy can correspond to an amount of counter regulator that is or can be provided to the subject as a replacement for carbohydrates.

The specific carbohydrates consumed by each subject, or the amount of carbohydrates consumed or the amount of carbohydrates consumed during each hypoglycemic event may vary. For example, the subject 512 may consume a particular candy bar if the subject's 512 measured blood glucose level is too low or if the subject senses that the blood glucose level is likely low (e.g., begins to feel some hypoglycemic effects). The subject may consume the whole candy bar or a portion of it. Part of the candy bar may be lost by the subject (e.g., falls to the ground). In other cases, the subject may take different candy bars, or other refined sugar sources, available during different hypoglycemic events. Thus, even though there may be an objective mapping between carbohydrates and counterregulators, the amount of carbohydrates consumed or avoided due to the availability of counterregulators may vary from hypoglycemic event to hypoglycemic event. Thus, an indication of total carbohydrate therapy that is avoided or may be avoided if a counterregulator is available may indicate the range of carbohydrates that may potentially be replaced by the availability of a counterregulator.

In some cases, the indication of carbohydrate therapy or total carbohydrate therapy may include one or more of an indication of calories attributable to carbohydrate therapy, an indication of carbohydrates, an indication of a sugar measure, an indication of an amount of food, or an indication of the subject's weight. The indication may be related to what is ingested due to the absence of a counterregulator or what is avoided based on the availability of a counterregulator. For example, the indication of calories may be the number of calories not ingested due to the presence of a counterregulator. Advantageously, the availability of therapy information regarding carbohydrate therapy or avoided carbohydrate therapy may assist in patient care. For example, a subject may reduce their intake of refined sugars, which may have several health effects. Additionally, a health care provider may better assist a subject in controlling their weight based on the carbohydrate information.

The carbohydrate therapy indicator can be presented to the user in any presentable form. For example, the carbohydrate therapy indicator can be presented as a table, chart, graph, histogram, or other data presentation tool to show the carbohydrate reduction over time achieved by the presence or use of a counter regulator for a particular subject 512. It should be understood that the carbohydrate therapy data indicator can vary from user to user due to differences in the physiological characteristics of the users, differences in the diabetes of each user, differences in the lifestyle of each user, among other factors. Advantageously, the glucose level control system 510 can be used to personalize the management of the blood glucose level of the subject 512 by tracking the carbohydrate therapy of the subject 512 or by determining avoided or avoidable carbohydrate therapy associated with a counter regulator.

Additional Embodiments of Carbohydrate Therapy Equivalence Tracking People with diabetes often take oral carbohydrates to treat or prevent hypoglycemia. Such excess carbohydrates can have unhealthy consequences, one of which is contributing to weight gain. By having a bihormonal glucose control system that injects a counterregulator (e.g., glucagon) to reduce the frequency, severity, and duration of hypoglycemia, the amount of oral carbohydrate needed "medically" to treat or prevent hypoglycemia can be greatly reduced.

Certain embodiments of the present disclosure relate to a method for converting an amount of an online counterregulatory dose (e.g., glucagon) calculated by an autonomous glucose control system into an amount of carbohydrates that the user would have been expected to have spared thanks to the counterregulatory dose, or would have been expected to have spared if the user had access to a counterregulator. In a bi-hormonal autonomous glucose control system that injects both insulin and a counterregulator/hormone, the method can include a mapping between the online counterregulatory dose delivered to treat or prevent the low glucose level and the oral carbohydrates that would have been expected to have been required otherwise to achieve an equivalent safe control situation (if the counterregulatory dose had not been delivered). In an insulin-only autonomous glucose control system where a dose of counterregulator/hormone is not delivered but is still calculated online, the method can include a mapping between the calculated online counterregulatory dose and an estimated amount of oral carbohydrates that the subject would have likely been spared from having to take to treat or prevent the low glucose level if a counterregulator was available and a dose of the counterregulator had actually been delivered.

Without loss of generality, embodiments disclosed herein include an autonomous glucose control system in which the counter regulator is glucagon. However, other drugs and/or counter regulators may be utilized. The method may include relating the calculated online glucagon dosing to oral carbohydrates taken for the treatment or prevention of low glucose levels ("therapeutic carbohydrates") as observed in actual use (e.g., during clinical studies) in an insulin-only configuration, and relating the relationship between the counter regulator and carbohydrates to a similar relationship between the delivered online glucagon dose (or other counter regulator) and similarly taken oral carbohydrates in a bihormonal (insulin-glucagon) configuration.

Using data collected from actual use (e.g., clinical studies), the relationship between the ingested therapeutic carbohydrates _Cio and the online calculated (but not delivered) glucagon dosing Gc in an insulin-only configuration can be described by the relationship _Cio = _Rio (x) * Gc, where _Rio (x) may be a relational factor that may be a function of several dependencies contained in the vector x. Such dependencies may include the particular insulin and/or glucagon being used (e.g., their clinical characteristics) and/or the pharmacokinetic settings assumed by the control system in relation to the insulin and/or glucagon. The dependencies may also include the user's weight and the glucose target used by the glucose control system. In some embodiments, for systems that show limited variation in the relationship between _Cio and _Gc (e.g., due to limited effect or limited or no variation in the associated dependencies), Rio(x) may be a constant, or Rio(x) ≡ Rio.

As with the insulin-only configuration, from actual usage data, the relationship between therapeutic carbohydrates ingested C _bh and glucagon dosage delivered online G _d in the bihormonal (insulin-glucagon) configuration can be described by the relationship C _bh =R _bh (x) *G _d , where R _bh (x) can be described similarly to R _io (x) above. In some cases, the quantities C _io , G _c , C _bh , and G _d can refer to daily amounts averaged over a period of use (e.g., one week). In some cases, the quantities C _io , G _c , C _bh , and G _d can refer to average daily amounts per user's body weight, in which case the dependency on body weight can be eliminated from x.

If _Gc is calculated but glucagon is not actually delivered in an insulin-only system, it is generally expected that _Gc will have no effect on glucose insofar as treating or preventing low glucose levels, resulting in further calculated glucagon dosing (e.g., moving in the direction of increasing the magnitude of _Gd for a given situation). In contrast, since _Gd is delivered in a bihormonal system, it is generally expected that it will have an effect of preventing or reducing the frequency, extent, or duration of low glucose levels, resulting in limiting the overall magnitude of glucagon dosing (e.g., limiting _Gd for a given situation). Thus, for a given set of dependencies, it is generally expected that _Gc > _Gd between the two system configurations. Similarly, since _Gc has no effect against low glucose levels, but _Gd does, the therapeutic carbohydrate is expected to be _Cio > _Cbh when comparing the two system configurations.

If, for a given actual use case of an insulin-only system, one could ideally correlate Gc with what the corresponding _Cio would have been in the same actual scenario if the calculated online glucagon dose had actually been delivered as _Gd , one could predict an inference that the user would have required (e.g., saved) " _Cio - _Cbh " less therapeutic carbohydrates if they had instead used a bi-hormonal system (with the same insulin controller) in which glucagon was delivered. Conversely, if, for a given actual use case of a bi-hormonal system, one could ideally correlate _Gd with what the corresponding _Cbh would have been in the same actual use scenario if the delivered online glucagon dose had not been delivered, but only been calculated as _Gc , one could predict an inference that the user would have actually avoided the need to take " _Cio - _Cbh " additional therapeutic carbohydrates if they had instead used an insulin-only system (with the same insulin controller) in which glucagon was not delivered. It should be understood that in practice it is not possible to re-run past actual usage scenarios to obtain such ideal relationships, so the above calculations are estimates in ideal circumstances.

は、「本当のＧ_ｄ」であったであろうものと比較して、ほぼ間違いなく大きさが誇張されている可能性がある。上述したように、事前の分析に基づいて、Ｇ_ｃは、インスリン単独構成において対応する量のＣ_ｉｏにマッピングすることができ、

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にマッピングすることができる。したがって、シミュレーション結果は、ユーザがバイホルモンシステムを使用していたとしたら節約したであろう治療炭水化物の推定値

に対する低減量

をマッピングすることができる。推定値は、保守的な推定値であってもよい。実際に観察されるＧ_ｃの範囲に及ぶ様々な実際の使用事例にわたってシミュレーション分析を繰り返すことにより、これらの推定値と、バイホルモンシステムを使用していたとしたらユーザが摂取する必要がなかった可能性が高い治療炭水化物の（場合によっては、保守的な）推定値

の関連付けられた範囲との間の全体的なマッピングが提供される。逆に、このマッピングは、バイホルモンシステムが使用されている場合に利用することができ、観察された投薬Ｇ_ｄが擬似的な計算グルカゴン

にマッピングし戻され、得られた関連付けられた差

は、ユーザがバイホルモンシステムを使用することによって節約できた可能性が高い治療炭水化物の（場合によっては、保守的な）推定値を提供する。 For practical implementation, one can re-simulate the actual use case where an insulin-only system is used, assuming that a bihormonal system is available in which glucagon is assumed to be delivered. The simulated glucagon dosing can show a reduction compared to the original _Gc of the insulin-only system, since the control system can take the delivered dose into account when issuing subsequent nearby glucagon doses. If the glucose profile remains unchanged in the simulation, the simulation may not reflect the glucose fluctuations that occur in response to the assumed delivered glucagon dosing. Then, the simulation may not reflect the complete reduction in glucagon dosing to _Gc , which would be observed if the glucose fluctuations benefited from the actual delivered glucagon. Thus, the reduced glucagon dosing observed in the simulation, i.e., the pseudo-delivered glucagon, is not a good indicator of the effect of the reduced glucagon dosing.

is arguably exaggerated in magnitude compared to what would have been the "real _Gd ." As noted above, based on prior analysis, _Gc can be mapped to a corresponding amount of _Cio in the insulin-only configuration,

corresponds to the amount in the bihormonal composition

The simulation results can therefore be used to estimate the therapeutic carbohydrate savings that a user would have made had they used the bihormonal system.

Reduction in

The estimates may be conservative estimates. By repeating the simulation analysis over a variety of real-world use cases spanning the range of _Gc observed in practice, these estimates, along with (possibly conservative) estimates of the therapeutic carbohydrates that a user would likely not have needed to ingest if they were using a bihormonal system, can be used to map these estimates.

Conversely, this mapping can be utilized when a bihormonal system is in use, where observed dose _Gd is approximated by a calculated glucagon dose.

and the associated difference

provides a (sometimes conservative) estimate of the therapeutic carbohydrates a user is likely to have saved by using a bihormone system.

Certain embodiments include a system comprising a controller for automatic control of a subject's blood glucose level. The controller may be operable to generate an insulin dose control signal based on the subject's time-varying glucose level as represented by a glucose level signal over time. The glucose level signal may be generated by a glucose sensor operable to continuously sense the subject's glucose level. The insulin dose control signal may control delivery of insulin doses by a delivery device. Additionally, the controller may operate at a regular frequency to generate an insulin dose control signal for regulating the subject's glucose level. During online operation, the controller may employ a control algorithm to generate a glucagon dosing signal that may be mapped to an associated amount of oral carbohydrate.

Oral carbohydrate may be associated with the prevention or treatment of low glucose levels. Additionally, the mapping between glucagon dosing signal and oral carbohydrate may be derived from an analysis of clinical data. A glucagon dosing signal may be calculated but not delivered in an insulin-only system configuration. In contrast, a glucagon dosing signal may be calculated and glucagon may be delivered in an insulin-glucagon system configuration. The calculated glucagon dosing in an insulin-only system configuration may be mapped to the amount of oral carbohydrate that would be estimated to have been saved if the glucagon dosing had been delivered if an insulin-glucagon system configuration had been used instead. The delivered glucagon dosing in an insulin-glucagon system configuration may be mapped to the amount of oral carbohydrate that would be estimated to have been saved if an insulin-only system configuration had been used instead. The mapping may depend on the clinical characteristics of the insulin and glucagon used, as well as the settings of the control system related to the action and effect of insulin and glucagon. Additionally, the mapping may depend on the subject's weight.

Backup Therapy Protocol Generation Portable drug devices, such as blood glucose control systems (e.g., insulin pumps or combination insulin and counterregulator (e.g., glucagon) pumps), can provide personalized therapy to subjects. In other words, portable drug devices can provide medication specific to the subject's physiology, condition, activity, and the like. In addition, some portable drug devices monitor the subject's condition to determine when to provide therapy, what type of therapy to provide (e.g., insulin or counterregulator therapy), and/or how much therapy to provide. The therapy provided by portable drug devices can be ongoing and, in some cases, life-saving. It is therefore important that portable drug devices function uninterrupted.

Despite best efforts, treatment with the portable medication device may be interrupted. For example, the portable medication device may be damaged, the subject may run out of or be unable to obtain necessary disposable items (e.g., replacement insulin cartridges, site kits for changing the site of the portable medication device, replacement batteries, etc.), or the subject may forget to charge the batteries in the portable medication device or may not be in a location where a power source is available to charge the portable medication device. Thus, the portable medication device may not be available or may require replacement.

When a portable medication device is not available or when a replacement (temporary or permanent) portable medication device is being used, it may be desirable to have an indication of the treatment setting from the portable medication device. For example, if a user (e.g., a subject, a healthcare provider, a parent, or a guardian) is using a portable medication device to provide an alternative therapy (e.g., an infusion therapy), they may need to know the amount of therapy to provide under certain circumstances or at a particular time.

In some cases, the healthcare provider may have access to treatment information, which may have been previously determined, for example, via clinical testing. This treatment information may include any type of information that may be used to determine the treatment to provide to the subject at a particular time or under particular conditions. For example, the treatment information may indicate the subject's set point insulin range, the amount of insulin to provide to the user to adjust glucose levels, the time at which the subject's insulin will reach a maximum concentration, or any other information that may affect the timing or amount of medication dosing.

In some cases, the medical provider may have insufficient treatment information available. For example, the subject may not be able to contact the medical provider to obtain treatment information at the time the information is needed. Furthermore, in some cases, the information may be out of date, for example, because the portable drug device may have improved the therapy over time. If the improvement was recent, the medical provider's old values may be sufficient until a replacement portable drug device can repeat the improvement process of the original portable drug device. In other cases, the old treatment information may be insufficient, for example, because the improvement was substantial, or because the subject may have had physiological changes (e.g., weight gain or loss, or metabolic changes) since the clinical test was last performed. Using the outdated treatment information may be less effective and may cause discomfort or harm to the subject.

Certain embodiments of the systems disclosed herein can generate backup therapy data. With the backup therapy data, a subject (or user) can administer an infusion therapy or configure a replacement portable medication device in the event that the subject's current device fails. By using the backup therapy data, the subject can maintain a level of therapeutic care that matches or more closely matches that which was being provided by the portable medication device than clinical data, even if available, which may be out of date.

The system may include an automated blood glucose control system (e.g., glucose level control system 510) configured to generate a backup therapy protocol including insulin therapy instructions derived from an autonomously determined dose of insulin. During normal operation, the system may receive a glucose level signal from a sensor operatively configured to determine a glucose level of a subject. The sensor may include any type of sensor capable of determining a glucose level. For example, the sensor may be a Continuous Glucose Monitoring (CGM) sensor.

Using the determined glucose level, the system can autonomously determine and/or generate a dose control signal using a control algorithm. The determination and/or generation of the dose control signal can be performed without any user action or interaction with the blood glucose control signal. In some cases, the absence of user action or interaction with the blood glucose control system may refer to a conscious action and preclude sensor measurements of the subject's physiological characteristics. The control algorithm can autonomously determine a dose of insulin to be infused to the subject for purposes of controlling the subject's blood glucose based at least in part on the glucose level signal. The control algorithm can include any type of control algorithm.

によって表すことができ、式中、Ｕ_０は、（Ｕ）を単位とする皮下用量であり、Ｋは、
スケーリング定数であり、α_１およびα_２は、時定数である。 For example, the control algorithm may be a bi-exponential pharmacokinetic (PK) model that models the accumulation of an insulin dose in the subject's plasma. The automated blood glucose system may control the delivery or administration of insulin or a counterregulator based on the bi-exponential PK model and one or more blood glucose measurements of the subject. The bi-exponential PK model may model the absorption of subcutaneously administered insulin into the blood and/or the rate of glucose decrease in the blood. The bi-exponential PK model over time is based on the following equation:

where _U is the subcutaneous dose in (U) and K is
is a scaling constant, and α ₁ and α ₂ are time constants.

As an alternative, the control algorithm may include a linear algorithm that models the subject's glucose decline or glucose accumulation based on a linear rate of decline. For example, the control algorithm may determine that a particular dose D of insulin should be administered to the subject. The control algorithm may then estimate that 0.25*D of insulin will be absorbed into the plasma per hour for four hours. Similarly, the control algorithm may estimate that once insulin reaches a maximum concentration in the plasma, insulin will decline at a rate of 0.33*D per hour for three hours.

Regardless of the control algorithm used, the automated blood glucose control system may administer insulin and possibly a counterregulator one or more times over a particular time period. There may be multiple reasons and/or triggers that cause the automated blood glucose control system to deliver insulin. For example, the automated blood glucose control system may provide a basal insulin dose periodically in an attempt to maintain a stable blood glucose level in the subject's plasma. As another example, the automated blood glucose control system may deliver a bolus of insulin at mealtimes, taking into account the expected amount of glucose to be ingested as part of a meal. The bolus at mealtimes may be an amount specified by the user, or may be an amount of insulin administered depending on an indication of a meal size by the subject. This indication of meal size may be subjective. In some cases, the amount of insulin bolus for a specified meal size may be a fixed or constant value. In some other cases, the amount of insulin bolus for a specified meal size may change over time as the automated blood glucose control system learns or refines the amount of insulin to administer to the subject to maintain the subject's blood glucose within a target set point. The automated blood glucose control system can learn or refine the optimal insulin to administer based on a comparison of expected and actual blood glucose level measurements when the subject (or other user) performs a subjective specification of meal size. In addition to basal and mealtime boluses of insulin, the automated blood glucose control system can also deliver correction doses of insulin to the subject based on the glucose level signal. Correction doses of insulin may be delivered in response to a model predictive controller (MPC) determining or estimating, based on blood glucose level readings, that the user's insulin level is expected to fall below a threshold at some time in the future. The MPC can execute a control algorithm that can regulate the glucose concentration to a reference setpoint while minimizing both the aggressiveness of the control signal and local insulin accumulation. A mathematical formulation describing the subcutaneous accumulation of administered insulin can be derived based on nominal time values for the pharmacokinetics of insulin for the subject. The mathematical formulation may relate to insulin absorption rate, peak insulin absorption time, and/or overall duration of action of insulin (or another drug). An example of an MPC controller that may be used with embodiments of the present disclosure is described in U.S. Patent No. 7,806,854, issued October 5, 2010, the disclosure of which is incorporated herein by reference in its entirety for all purposes.

The automated blood glucose control system may track insulin therapy administered to the subject over a tracking period. The tracking period is not limited in length and may generally be any time period, but typically the tracking period is at least a minimum time period sufficient for the automated blood glucose control system to learn or refine the amount of medication (e.g., insulin) administered to the subject under particular conditions (e.g., when a particular blood glucose level is detected or a particular meal size is identified). For example, the automated blood glucose control system may initially administer 6 units of insulin at lunch and 10 units of insulin at dinner. These initial values may be set by a healthcare provider and/or the subject, for example, based on the subject's clinical data. However, over time (e.g., 3-5 days), the automated blood glucose control system may determine that providing 7 units of insulin at lunch and 8 units of insulin at dinner will maintain the subject's blood glucose level closer to the median of the set range than the initial configuration. Although not limited in this manner, typically, each unit of insulin is 1/100 of a milliliter of insulin.

As shown, the tracking period can be any length of time. For example, the tracking period can be 1 day, 3 days, 5 days, 7 days, anything in between, or longer. Typically, the tracking period is at least long enough to provide sufficient time to learn or refine the initial settings of the subject's automated blood glucose control system. In some cases, the tracking period can be 1 or 2 days. In other cases, the tracking period can be from a particular time period to the current time period. For example, the tracking period can be from the start of treatment to the current time. In other cases, the tracking period can be a moving or shifting window. For example, the tracking period can be as short as one week, two weeks, one month, or one year. Additionally, for non-blood glucose systems, the tracking period can vary based on the amount of time sufficient to determine or refine the subject's medication management values. In some cases, the tracking period can be a window of a particular length. The window can be a moving window. For example, the window can be the past seven days. As time passes, the window moves to continue to encompass the previous seven days.

Tracking insulin therapy may include storing autonomously determined doses of insulin delivered to the subject. These autonomously determined doses of insulin may include one or more of a basal insulin dose, a prandial insulin bolus, or a correction insulin dose. Additionally, tracking insulin therapy may include tracking the type of insulin used. The type of insulin may include any type of insulin, such as fast-acting insulin (e.g., Lispro, Aspro, or Glulisin), regular or short-acting insulin (e.g., Humulin R, Novolin R, or Velosulin R), intermediate-acting insulin (e.g., Humulin N, Novolin N, ReliOn), long-acting insulin (e.g., detemir (Levemir), and glargine (Basaglar, Lantus)), or very long-acting insulin (e.g., degludec (Tresiba), glargine u-300 (Toujeo)). Additionally, tracking insulin therapy may include tracking counterregulator (e.g., glucagon) therapy.

In some cases, tracking insulin therapy can include calculating an average therapy delivered over a time period (e.g., over a tracking window). For example, tracking insulin therapy can include determining a moving average of the nominal basal dose for the past seven days during each dosing interval. Assuming that basal therapy is delivered every five minutes, the moving average can be calculated based on the last 288 doses (e.g., over a day) or 2016 doses (e.g., over seven days). This calculation can be used to obtain a basal rate profile for the back-up therapy. In some cases, the time period can be divided into different time segments that can be associated with different treatment rates. For example, there can be four basal therapy periods (e.g., 10 p.m. to 4 a.m., 4 a.m. to 10 a.m., 10 a.m. to 4 p.m., and 4 p.m. to 10 p.m.). Thus, a separate moving average can be calculated for each of the basal therapy periods, over a day, or over several other time periods (e.g., seven days). The calculated averages can be used to calculate a back-up basal rate that can be used to program an automatic glucose control system. Additionally, the basal rate profile can include tabulating the doses throughout the day to determine the dose of long-acting insulin that can be used for the infusion therapy.

As with basal therapy, a moving average of correction doses can be calculated to determine a correction bolus of insulin to deliver via pump or infusion therapy. Alternatively, or in addition, the moving average of correction doses combined with measurements of the subject's blood glucose over time may be used to determine the rate of change in blood glucose from one unit of insulin provided during correction therapy.

Mealtime boluses can also be calculated using moving averages. Additionally, separate moving averages can be calculated for the doses for each meal (e.g., breakfast, lunch, and dinner) over a period of time (e.g., 7 days prior to the mealtime). In some cases, each of the moving averages may be calculated using a different window function. For example, a moving average may be calculated using a Hann window or a Hamming window. In some cases, different levels of dosing can be determined for different meal sizes, and different doses can be determined for different meals. In some cases, different meals (e.g., breakfast vs. lunch) may result in different dosages even if similar in size, for example, due to differences between the subject's blood glucose level when the subject wakes up and the subject's blood glucose level when the subject normally eats lunch, or due to differences in the types of food consumed at breakfast and lunch. Additionally, in some cases, differences in the subject's metabolism may result in different mealtime boluses.

The insulin therapy may be stored in a treatment log or any other type of data structure. Additionally, the insulin therapy may be stored in the memory of the automated blood glucose system, on a companion device, on a subject's or user's computing device (e.g., laptop or desktop), in a cloud computing environment, or in any other storage system capable of receiving insulin therapy information from the automated blood glucose control system.

Using the treatment log or tracked insulin data, the automated blood glucose system, or a computing system accessing the treatment log or tracked insulin data, can generate a backup insulin therapy protocol. The backup insulin therapy protocol may include a backup infusion therapy protocol or a backup pump therapy protocol. The backup infusion therapy protocol may include one or more amounts of insulin (or other medication) administered using one or more infusion therapies (e.g., manually delivered injections) to help maintain the subject's condition within a normal or desired physiological range or condition. The backup pump therapy protocol may include data and/or instructions for a replacement medication pump of the same or different type to deliver therapy to the subject. The replacement medication pump may be a permanent or temporary replacement.

The backup pump therapy protocol may be the same as the backup infusion therapy protocol and/or may include the same types of information. Alternatively or additionally, the backup pump therapy protocol may include different values than the backup infusion therapy protocol. For example, the backup pump therapy protocol may include indications of a basal therapy that is provided periodically at relatively short intervals (e.g., every 5 minutes, every 30 minutes, every hour, etc.). Because insulin pumps can automatically administer insulin, it is possible to provide a constant or periodic drip of insulin. Manually administering insulin at similar short intervals may not be practical for subjects using infusion therapy. Alternatively, a user may administer therapy less regularly (e.g., every 5 minutes, every 30 minutes, every hour, etc.).
(e.g., approximately once every 4-5 or 6-8 hours, before mealtimes, after waking, and/or before going to bed, etc.). Thus, the back-up therapy protocols for pumps and infusions may differ. Additionally, the type of insulin used or specified in the back-up protocols may differ. For example, the back-up protocols may call for the use of long-acting insulin (e.g., insulin glargine, etc.) or intermediate-acting insulin (e.g., human recombinant insulin, etc.).

In some cases, the backup pump therapy protocol may be used to manually refine the pump settings of the replacement blood glucose control system used by the subject. In other cases, the replacement blood glucose control system may automatically configure itself based on the backup therapy protocol. For example, the user may provide the backup therapy protocol to the replacement blood glucose control system, and the replacement blood glucose control system may use that information to self-calibrate.

Regardless of whether a back-up protocol is generated or required, collecting and analyzing therapy data for the therapy provided by the automated blood glucose control system may be useful in helping to manage the subject's condition. For example, the therapy data may help determine whether the subject is satisfied with the therapy provided by the automated blood glucose control system, or whether the blood glucose control system is configured to best fit the subject's lifestyle or therapy preferences (subjective or otherwise). One way of determining whether the blood glucose control system is providing the desired therapy, or therapy at a desired rate, is to determine the frequency and/or magnitude of modifications made to the therapy provided by the automated blood glucose control system by the subject, or by another user, that may help manage the subject's therapy.

The automated blood glucose control system disclosed herein can track user modifications to the control parameters over a tracking period. The tracking period can include any of the time periods described above for tracking therapy to generate a backup protocol. Additionally, the control parameters can include any type of control parameter that can affect the administration of therapy. For example, the control parameters can relate to the amount of therapy, the timing of the therapy delivered, the rate at which the therapy is delivered, or triggers for when or whether to deliver therapy, among other control parameters. Additionally, the control parameters can directly affect the delivery of therapy (e.g., specify the time to deliver a drug or the amount of drug to deliver) or indirectly affect the therapy (e.g., adjust set point ranges for maintaining a subject's blood glucose or insulin accumulation rate, which can be used to modify a control algorithm for administering the therapy).

User modifications may include any changes to the control parameters or settings of the automated blood glucose control system. For example, the automated blood glucose control system may track the rate or percentage of each instance and/or number of times the user decreases or increases a control parameter (e.g., the amount of insulin administered). Furthermore, tracking changes to control parameters may include tracking how often the user discontinues treatment or temporarily adjusts the target blood glucose range or other control parameters. In addition, tracking changes to control parameters may include tracking when the user makes changes to the control parameters. For example, a user may typically modify control parameters overnight but leave daytime parameters unchanged, or vice versa. In some cases, the automated blood glucose control system may track the subject's weight over time. Weight may be provided by the user and may affect blood glucose control (e.g., the amount of insulin administered may be related to the subject's weight).

The automated blood glucose control system can generate reports that track user modifications to control parameters. The reports may include measures of the frequency of increases and decreases from stored control parameter values. Additionally, the reports may include an indication of the percentage of times the user modified the control parameter higher or lower than the automated blood glucose control system's stored control parameter value over the tracking period. In some cases, the reports indicate the number of times insulin infusion was paused over the tracking period, or the rate (e.g., aggressiveness) at which insulin was delivered to the subject.

Using this report, the clinician or other health care provider can determine whether modifications should be made to the control parameters to better manage the subject's treatment. For example, if it is determined that the subject has elevated blood glucose target levels in the evening or at night 4-5 times per week, the clinician can determine that the evening target settings should be adjusted to reduce the number of occurrences where the user manually adjusts the treatment provided by the automated blood glucose control system or the control parameter settings for the treatment. In some cases, the subject may have treatment adjusted based on subjective reasons. In such cases, the treatment report may enable the clinician or health care provider to train the subject in controlling the subject's illness. In other cases, the clinician may determine that the subject has a different tolerance to blood glucose than was originally determined or from the average subject, and can adjust one or more control parameters of the automated blood glucose control system accordingly.

In some implementations, the automated blood glucose control system can automatically adjust one or more control parameters over time based on the reports. For example, if the automated blood glucose control system determines that over a month, the subject has adjusted their daytime target glucose range low 20 out of 30 days, the automated blood glucose control system can modify the control parameters to have a lower set point range. In some cases, the automated blood glucose control system can communicate the changes to a user, such as the subject, a parent or guardian, or a healthcare provider.

Exemplary Backup Therapy Protocol Generation Process FIG. 8 presents a flowchart of an exemplary backup therapy protocol generation process 800 according to certain embodiments. Process 800 may be performed by any system capable of tracking drug therapy (e.g., insulin therapy) provided to a subject over time and generating a backup therapy protocol that may be used in the event that glucose level control system 510 becomes unavailable. For example, process 800 may be performed by one or more elements of glucose level control system 510. In some cases, at least some operations of process 800 may be performed by a separate computing system that receives an indication of drug therapy provided to subject 512 from glucose level control system 510. While one or more different systems may perform one or more operations of process 800, for ease of explanation and not to limit the present disclosure, process 800 is described with respect to a particular system.

Process 800 begins at block 802 with the glucose level control system 510 receiving a glucose level of a subject 512. Receiving the glucose level may include receiving and/or determining a glucose level signal corresponding to the glucose level of the subject. The glucose level signal may be received from a glucose sensor 516 (e.g., a CGM sensor). Alternatively, or in addition, the glucose level may be determined from:
It may be received from a user who provides the glucose level to the glucose level control system 510 via a user interface, such as a user interface generated by the processor 530 that may be output on a touchscreen by the touchscreen controller 538. The glucose level received from the user may be a glucose level measured using an alternative sensor or measurement mechanism (e.g., diabetic measurement strips) that may be used in place of the glucose sensor 516.

At block 804, the glucose level control system 510 generates an insulin dose control signal based at least in part on the glucose level signal. In some cases, the insulin dose control signal may be a drug control signal configured to control a drug pump to administer a drug (e.g., insulin, a counterregulator, or other drug) to the subject 512. The dose control signal may be generated using a control algorithm configured to autonomously determine a dose of insulin to be administered or infused to the subject to control the subject's blood glucose based at least in part on the glucose level determined at block 802 or the glucose level signal.

In block 806, the glucose level control system 510 tracks the insulin therapy administered to the subject 512 over a tracking period. The tracking period is typically at least one day, but may be longer. For example, the tracking period may be one day, two days, one week, one month, several months, one year, any length of time between the aforementioned examples, or even longer. In some cases, the tracking period may be continuous from the time that tracking begins. For example, the tracking period may encompass the entire life of use of the glucose level control system 510 by the subject 512. If the tracking period is set to a defined time period (which may be modified for different iterations of the process 800), the process 800 may be repeated periodically, on demand or in response to a trigger event, using a new tracking period of equal or different length. The trigger event may include any event that can potentially cause a pre-generated backup therapy protocol to expire. For example, the triggering event may include a change in medication type (e.g., a different insulin or counterregulator combination), a change in a physiological characteristic of the subject 512 (e.g., a change in weight or a different glucose level or sensitivity to a medication), or a change in the average activity level of the subject 512.

The tracking period is typically at least one day to allow the glucose level control system 510 to determine a backup protocol based on data from a full cycle of use of the glucose level control system 510 (e.g., wake-up and sleep times), although in some cases the tracking period may be less than one day, at least initially. For example, an initial backup therapy protocol may be generated after a half-day of activity. This initial backup therapy protocol may be updated as more data becomes available throughout a day's use (and possibly the next day's use) of the glucose level control system 510.

In some cases, the tracking period may be defined by or based on a specific number of insulin administration events. For example, the tracking period may be defined by at least 10 instances of generating an insulin dose based on the glucose level signal. As another example, the tracking period may be defined by a minimum number of meal events, correction dose events, and/or basal dose events. For example, the tracking period may require three meals, or three meals with each meal type occurring, two correction doses, and/or 100 basal doses. It should be understood that the aforementioned number of doses is merely an example, and the tracking period may include more or fewer doses. Additionally, the tracking period may be defined or specified as a combination of time and occurrence of a specific number of insulin doses.

In some cases, the tracking period may be variable. For example, if the glucose level control system 510 determines that the insulin dose therapy is inconsistent or irregular over the tracking period (e.g., due to inconsistent exercise or eating habits), the tracking period may be extended.

Tracking the insulin therapy may include storing an insulin dose control signal generated at block 804 based at least in part on the glucose level signal. Alternatively or additionally, tracking the insulin therapy may include storing an indication of an amount of insulin (or another medication) corresponding to the insulin (or another medication) dose control signal. The insulin dose control signal and/or the indication of the amount of insulin may correspond to an insulin dose delivered to the subject 512 as a basal insulin dose, a correction bolus of insulin, and/or a prandial bolus of insulin.

Storing the insulin dose control signal and/or the indication of the amount of insulin may include storing the insulin dose control signal and/or the indication of the amount of insulin in a treatment log or any other type of data structure in the memory 540 of the glucose level control system 510. Alternatively or in addition, the glucose level control system 510 may store the insulin dose control signal and/or the indication of the amount of insulin in a remote data storage device. This remote data storage device may be a local computing system with which the glucose level control system 510 can communicate (e.g., the subject 512 or user's laptop, desktop, smartphone, or other computing device). The glucose control system 510 may provide the insulin dose control signal data or the indication of the amount of insulin to the local computing system via Bluetooth or other short-range communication services or via a local network. Alternatively or in addition, the remote data storage device may be a remote computing system with which the glucose level control system 510 can communicate via a wide area network, such as a wireless area network, a cellular network using IoT-based communication technology, a cellular communication technology, or any other communication network. In some cases, the wide area network may include the Internet. The glucose level control system 510 may include a wireless communicator that allows it to communicate with local or remote computing systems. Additionally, the remote computing systems may be computing systems in a data center or cloud computing environment.

Whether a local or remote computing system, the glucose level control system 510 can establish a communication channel with the computing system. This communication channel may be an encrypted channel. Additionally, the communication channel may be a direct end-to-end connection between the glucose level control system 510 and the computing system. Once the communication channel is established, the glucose level control system 510 can transmit insulin dose control signal data or an indication of the amount of insulin to the computing system.

Generally, the operations associated with blocks 802-806 may be repeated multiple times throughout the course of a tracking period. For example, in some cases, an insulin dose control signal associated with a basal insulin may be generated up to 288 times per day. Thus, tracking insulin therapy may include storing insulin dose control signals and/or corresponding indications of the amount of insulin for multiple autonomously determined insulin doses infused to the subject 512 throughout the tracking period.

Generally, counter regulator therapy involves administering a counter regulator (e.g., glucagon) when there is a risk or occurrence of hypoglycemia. Typically, counter regulators are not provided on a regular or daily basis. However, it may be useful to understand the amount and frequency at which counter regulators are administered to the subject 512. For example, this may help a healthcare professional or user guide or tailor the care of the subject 512. Additionally, tracking counter regulator usage may help determine a minimum amount of counter regulator that should be accessible to the subject 512 for use in a bihormonal pump or in an infusion therapy. In some cases, block 806 may include tracking the counter regulator administered during the tracking period. Tracking counter regulator therapy may include storing an indication of an autonomously determined dose of counter regulator delivered to the subject 512 in response to the glucose level signal obtained in block 802.

At block 808, the glucose level control system 510 generates a backup therapy protocol based at least in part on the tracked insulin therapy. The backup therapy protocol may be determined based on an average amount or rate of insulin administered to the user over the tracking period, over different portions of the tracking period (e.g., breakfast, lunch, and dinner, or wake and sleep times, etc.), or in response to specific events (e.g., mealtimes, when blood glucose levels exceed a threshold level, etc.). The backup therapy protocol may include a backup infusion protocol and/or a backup pump therapy protocol. The backup infusion protocol may provide the user (e.g., the subject 512, a parent or guardian, or other caregiver of the subject 512) with an amount of insulin that may be administered to the subject 512 via infusion. Additionally, the backup infusion therapy may indicate times at which insulin may be administered. For example, the backup infusion therapy may indicate an amount of insulin to be administered at a particular mealtime. Additionally, the backup infusion therapy can indicate the effect that units of insulin may have on the subject 512 so that the user can calculate how much insulin to administer to the subject 512 if the blood glucose measurements indicate that the subject's 512 glucose level is too high (e.g., above a desired setpoint range).

Similar to the backup infusion therapy protocol, the backup pump therapy protocol can provide a user (e.g., the subject 512, a parent or guardian, or other caregiver of the subject 512) with an amount of insulin that can be administered to the subject 512 via a drug pump. Using the backup pump therapy protocol, the user can configure the drug pump to administer a specified amount of insulin. The backup pump therapy protocol may be used to configure the drug pump when access to a CGM sensor is unavailable (e.g., the subject 512 does not have a CGM sensor, or the drug pump or CGM sensor is malfunctioning, etc.). Additionally, the backup pump therapy protocol may be useful to provide an initial configuration for a replacement glucose level control system.

In some cases, the backup infusion therapy protocol and the backup pump therapy protocol may be the same. However, at least the recommended basal therapy settings are often different. It is generally not practical to administer insulin to the subject 512 via infusion therapy more than a few times a day. Thus, the backup infusion therapy protocol may specify a long-acting insulin unit or dose that may be administered on a limited basis (e.g., once or twice a day). However, a drug pump may more easily administer insulin on a limited basis (e.g., every hour, every 30 minutes, every 5 minutes, etc.). Thus, the backup pump therapy protocol may specify a basal rate of insulin that may be administered once per hourly unit (e.g., once per hour, or once per 15 minutes, or once per 5 minutes), or continuously at a particular rate (e.g., 0.5 or 0.6 units) per hourly unit (e.g., every hour). Additionally, the backup pump therapy protocol may specify different rates for different parts of the day (e.g., for a subject, one rate for each half of the day, one rate for each quarter of the day, or one rate during typical waking hours and one rate during typical sleeping hours, etc.).

In some cases, an initial back-up therapy protocol may be generated at block 808. The initial back-up therapy protocol may be updated over time as additional insulin therapy data is obtained.

Generating the back-up therapy protocol can include determining a number of long-acting insulin units based at least in part on the average total basal insulin provided to the subject 512 per day over the follow-up period. The average total basal insulin provided per day can be included in the back-up infusion therapy protocol as a single dose of long-acting insulin configured to help maintain the subject's 512 basal insulin levels throughout the day. In some cases, the average total basal insulin provided per day can be included in the back-up infusion therapy protocol as multiple doses of insulin (e.g., two or three doses throughout the day).

Alternatively or in addition, basal insulin may be included in a backup therapy protocol, such as a backup pump therapy protocol, as a dosage rate that may be delivered to the pump to provide a rate of basal insulin throughout the day. Furthermore, in some cases, each day of the tracking period may be divided into multiple subperiods. For example, each day of the tracking period may be divided into two, three, four, or more time periods, or periods of equal or different length. In some such cases, generating the backup therapy protocol may include determining an hourly basal rate for each subperiod of the multiple subperiods. This hourly basal rate may be determined by averaging the corresponding subperiods for each day of the tracking period. For example, if each day of the tracking period is divided into two subperiods (e.g., noon to midnight and midnight to noon), the basal rate delivered during the first subperiod throughout the tracking period may be averaged, and the basal rate delivered during the second subperiod throughout the tracking period may be averaged to determine two basal rates for inclusion in the backup therapy protocol. The basal rate may be determined at an hourly rate or based on any other time period. Alternatively, the basal rate may be determined based on the amount of time that a particular amount (e.g., 1 unit) of insulin is recommended to be administered to the subject 512 as part of the back-up therapy protocol. For example, if the glucose level control system 510 determines that the subject 512 is receiving 1 unit of insulin every 1.125 hours, the back-up therapy protocol may indicate that the basal rate is 1 unit every 1.125 hours. Alternatively, or in addition, the back-up therapy protocol may indicate a basal rate of 0.89 units per hour.

In addition, generating the backup therapy protocol may include determining an average correction bolus provided to the subject per day over the tracking period. The average correction bolus may be determined by adding to each data the total amount of correction doses administered and dividing by the number of days in the tracking period. The average correction bolus may be included in the backup therapy protocol as guidance to the user. However, generally, a correction bolus is provided in response to a determination that the subject's blood glucose level is spiking or has exceeded a threshold, and is not necessarily provided as a daily dose of insulin. Thus, the average correction bolus may be included as part of the backup therapy protocol to facilitate a user's understanding of the amount of insulin likely to be needed during an average day, which may be useful for a user (e.g., a subject) to determine, for example, how much insulin is available for use in an infusion therapy. In some cases, one or more days or time periods of the tracking period may be omitted when determining the average correction bolus, for example, because one or more days or time periods may be determined to be outliers. The outliers may be omitted to provide a more accurate understanding of the average insulin requirement or consumption.

In some implementations, the glucose level control system 510 can determine an average change in blood glucose attributable at least in part to units of insulin provided as correction boluses to the subject during the tracking period. In some cases, the glucose level control system 510 can correlate each correction bolus applied during the tracking period with the change in the blood glucose level of the subject 512.

Generating the backup therapy protocol may include determining an average mealtime bolus of insulin provided to the subject over the tracking period for each of a number of mealtimes per day. In some cases, an average mealtime bolus may be determined for a particular meal (e.g., breakfast, lunch, and dinner), while other food intake periods (e.g., snack or teatime) may be omitted or ignored. Additionally, the average mealtime bolus may be associated with a particular meal size as specified by the user. For example, the glucose level control system 510 may determine an average mealtime bolus for small and large meals, or for small, medium, and large meals. The average mealtime bolus may be determined by averaging the amount of insulin that the glucose level control system 510, using the control algorithm of the glucose level control system 510, has determined should be administered to the subject 512 for each mealtime and the specified meal size.

In some cases, the back-up therapy protocol may include data related to administration of counterregulators. For example, the back-up therapy protocol may include an indication of total and/or daily counterregulators provided to the subject over the follow-up period.

At block 810, the glucose level control system 510 outputs the backup therapy protocol. Outputting the backup therapy protocol may include displaying the backup therapy protocol on a display to enable a user to implement the backup therapy protocol. Alternatively, or in addition, outputting the backup therapy protocol may include transmitting the backup therapy protocol to a user's computing device for display and/or storage. In some cases, the backup therapy protocol may be stored in the glucose level control system 510 and may be accessed in response to user interaction with a user interface of the glucose level control system 510.

In some cases, process 800 can be at least partially combined with process 900 described below. Thus, in some cases, the back-up therapy protocol can further include a record of user modifications to one or more control parameters used by the control algorithm of the glucose level control system 510 to autonomously determine the dose of insulin to be infused or administered to the subject. This record of user modifications can include an identification of instances of user modifications to the control parameters and/or a percentage of the number of times the user modified the control parameters during each day of the tracking period and/or during the entire tracking period.

9 illustrates a flow chart of an exemplary control parameter modification tracking process 900 according to certain embodiments. The process 900 may be performed by any system capable of tracking user interactivity with the glucose level control system 510, and more specifically, the occurrence of a user modifying a control parameter used by the glucose level control system 510 to help control drug delivery to the subject 512. For example, the process 900 may be performed by one or more elements of the glucose level control system 510. In some cases, at least certain operations of the process 900 may be performed by a separate computing system that receives an indication of a change to the control parameter setting of the glucose level control system 510 from the glucose level control system 510 and/or from a user interaction with a user interface at the separate computing system prior to transmitting the modification to the glucose level control system 510. While one or more different systems may perform one or more operations of the process 900, for ease of explanation and not to limit the present disclosure, the process 900 is described with respect to a particular system.

The process 900 begins at block 902, where the glucose level control system 510 receives the glucose level of the subject 512. Block 902 may include one or more of the embodiments described above with respect to block 802.

At block 904, the glucose level control system 510 generates an insulin dose control signal based at least in part on the glucose level signal and the control parameter. The insulin dose control signal may be generated based on a control algorithm that enables the glucose level control system 510 to autonomously determine a dose of insulin to be infused or administered to the subject to control the subject's blood glucose level. The control algorithm may determine the dose of insulin based at least in part on the control parameter. The control parameter may include any parameter that can affect the operation or output of the control algorithm or the operation of the glucose level control system 510 and that is modifiable by a user (e.g., the subject 512 or a user (e.g., a parent or guardian) at least partially responsible for the care of the subject 512). In some cases, the control parameter may be or correspond to a target set point for the glucose level of the subject 512. In other cases, the control parameter may correspond to whether the glucose level control system 510 generates an insulin dose control signal for at least some time period. For example, the control parameter may relate to whether at least some operation of the glucose level control system 510 is paused or active. Block 904 may include one or more of the embodiments described above with respect to block 804.

In block 906, the glucose level control system 510 tracks one or more user modifications to the control parameters over a tracking period. The tracking period may be one day, less than one day, or longer than one day (e.g., two days, three days, one week, one month, etc.). Additionally, the tracking period may include one or more time periods, as previously described with respect to process 800. The user may be the subject 512 or any other user (e.g., a parent or guardian, or a health care provider) who may be authorized to modify the control parameters of the glucose level control system 510.

The user can modify the control parameters using a user interface, which may be generated and/or output by the glucose level control system 510. Alternatively, or in addition, the user interface may be
The user interface may be generated and/or output by a computing system that can communicate with and/or modify the control parameters in the glucose level control system 510. For example, the computing system may be a smartphone, a smartwatch, a laptop, or a desktop computer, or any other type of computing device that may be used to configure the glucose level control system 510. The user interface may be output on a touch screen that the user can interface with to modify the control parameters. The user can interact with control parameter selection elements or other user interface elements to select and/or modify the control parameters. In some cases, the user can provide a control parameter having any value supported by the glucose level control system 510. In other cases, the user may be limited to selecting a particular value of the control parameter, which may be smaller than values that the glucose level control system 510 can support or smaller than values other users are permitted to select. For example, a clinician may be allowed a larger modification range than a parent to modify a control parameter.

Tracking the one or more user modifications may include storing the one or more user modifications in a treatment log, database, or other data structure. Additionally, tracking the one or more user modifications may include tracking or storing whether each of the user modifications includes an increase or decrease in a control parameter. The determination of whether the control parameter has increased or decreased may be determined based on whether the value of the control parameter has increased or decreased relative to a reference value. The reference value may include a current value of the control parameter, a default value, a clinical value provided to the glucose level control system 510, and/or a value determined by the glucose level control system 510. Additionally, tracking the one or more user modifications may include storing a time and/or one or more conditions under which the control parameter was modified. For example, the glucose level control system 510 may store the time of day, the activity level of the subject 512 determined from one or more physiological sensors and/or specified by the user, meals that have been or have not been ingested, etc. Additionally, tracking the insulin therapy may include storing an indication of an autonomously determined dose of insulin delivered or administered to the subject 512.

In some cases, the tracking period may be divided into multiple sub-periods. The sub-periods may correspond to different portions of a day in the tracking period. For example, each day of the tracking period may be divided into two equal parts corresponding to approximately day and night, or into thirds or fourths corresponding to a particular number of hours in the day. The sub-periods may or may not be of equal length. Tracking the one or more user modifications may include tracking occurrences of modifications to the control parameter within a sub-period of the tracking period. Furthermore, occurrences of modifications within a sub-period of one day in the tracking period may be combined with occurrences of modifications within a corresponding sub-period of another day in the tracking period. In other words, each occurrence of a modification of the control parameter in a sub-period defined as 9:00-21:00 may be aggregated over the number of days in the tracking period.

In some cases, a different baseline value may be determined for the control parameter for each subperiod. In some such cases, tracking the one or more user modifications may include tracking modifications of the control parameter value relative to the baseline value for the subperiod.

At block 908, the glucose level control system 510 generates a report of the user modifications to the control parameters. Alternatively, or in addition, the report may be generated by another computing system, such as a cloud computing system or a healthcare provider's computing system, based on data received from the glucose level control system 510 (e.g., the occurrence of user modifications to the control parameter values).

The report may include a measure of the frequency of increases and decreases from the stored control parameter value. Additionally, the report may indicate the number of times operation of one or more features of the glucose level control system 510 was paused or interrupted, or the percentage of the tracking period during which operation of one or more features of the glucose level control system 510 was paused or interrupted. Additionally, the report may indicate the magnitude of the correction to each control parameter, in total and/or on average, for each occurrence. In some cases, the report may indicate the percentage of user corrections above or below the baseline value over the tracking period. Additionally, if the tracking period, or each day of the tracking period, is divided into subperiods, the report may include a measure of the frequency of increases and decreases from the baseline value of the control parameter for each subperiod of the tracking period. In some cases, the report may include an identification of a user activity that occurred at the time of the user correction to the value of the control parameter or within a threshold time period. For example, the report may identify whether the user was exercising (e.g., swimming, running, dancing, etc.) when the user correction to the control parameter value was made.

In some embodiments, block 908 may include storing the generated report in the glucose level control system 510 (e.g., in memory 540) and/or in storage of another computing device. In some cases, the computing device may be a computing device of the subject 512 (or a parent or guardian). Additionally, the computing device may be a computing device of a healthcare provider. In some cases, the computing device may be a computing device of a cloud computing service.

The report may be obtained from the glucose level control system 510 by a wired connection (e.g., a USB cable). Alternatively, or in addition, the report may be obtained via a wireless connection to the glucose level control system 510. For example, the glucose level control system 510 can establish an encrypted connection with a healthcare provider's computing system, and the healthcare provider can receive the report from the glucose level control system 510. Alternatively, or in addition, the glucose level control system 510 can establish an encrypted communication channel with a cloud computing provider, and the cloud computing provider can receive the report from the glucose level control system 510. The report may then be accessed by any authorized user.

Advantageously, in certain embodiments, a healthcare provider can use the reports to help manage the care of the subject 512. For example, if the healthcare provider determines that the user is modifying the control parameters more than a threshold number of times or during a particular time period, the healthcare provider can use this information to modify the care being provided to the subject 512 and/or educate the subject 512 on optimal care. For example, the rate of treatment may need to be modified, or the amount of insulin may be too low for the subject's comfort. For example, in some cases, the subject 512 may have a different tolerance to blood glucose levels than the average user, which may lead the user to modify the range of set points. Understanding this information can help the healthcare provider manage the care of the subject 512 (e.g., adjust the range of default settings or modify the type of insulin prescribed).

Furthermore, as noted above, process 900 may be combined with process 800. In other words, a report may be generated that includes both a backup therapy protocol and a record of the number of times a user may make modifications to one or more control parameters of glucose level control system 510. In other cases, processes 800 and 900 may be triggered and/or executed independently.

Examples of Backup Therapy Reports FIGS. 10-12 show one non-limiting example of a backup therapy report or set of reports that may be generated using one or more of the embodiments disclosed herein. In other words, the reports of FIGS. 10-12 may be part of a single report generated by the glucose level control system 510, or may be separate reports generated simultaneously or based on different data and/or over different tracking periods. The reports may be generated by the automated blood glucose control system 510 or by another computing system that may receive therapy data from the automated blood glucose control system. Additionally, FIGS. 10-12 represent only one non-limiting example of a report or set of reports that may be generated. Other reports that include more or less data may be generated. For example, the backup infusion therapy protocol and backup pump therapy protocol shown in FIG. 10 may be separated into two separate reports that may be generated and/or accessed separately.

FIG. 10 illustrates an exemplary backup therapy protocol report 1000 according to certain embodiments. The amount of insulin recommended under different times and/or conditions may be displayed in units. In some cases, the report 1000 may specify the amount of insulin and/or type of insulin contained in a unit. Additionally, in some cases, the report 1000 may be an interactive report that allows the user to modify the type of insulin or the unit size of insulin. In some such cases, the table 1002 may update the recommended number of units of insulin to administer at a particular time or condition based on the selected type of insulin and/or unit size of insulin.

The report 1000 may identify the length of the tracking period 1006 used to determine the backup therapy protocol. Additionally, the report 1000 may identify the time or date range 1008 in which the tracking period 1006 occurred. Advantageously, knowing the tracking period 1006 may help determine the amount of confidence to be placed in the recommendations included in the backup therapy protocol. The longer the tracking period, the more likely the recommendations are to be accurate. A shorter tracking period may be more susceptible to more inaccurate recommendations, for example, because the tracking period may include more days that are outliers to the subject's typical condition or activity level. For example, a one-day tracking period that occurs on a day when the subject ate a larger meal than usual or exercised significantly more than usual may result in a backup therapy recommendation that does not fit the subject's typical lifestyle. Additionally, knowing when the tracking period occurred may be useful in determining how up-to-date the recommendations are and whether they are a reliable indicator of the amount of insulin the subject should administer. For example, if the date range 1008 of the follow-up period 1006 is a year old and the subject has gained or lost a significant amount of weight over the year, the back-up therapy protocol may no longer be a reliable indicator of the recommended infusion therapy. In such cases, the user may adjust the recommendation and/or trigger a new occurrence of the process 800.

Table 1002 shows an exemplary backup infusion therapy protocol, which may indicate various insulin doses that may be administered to subject 512 at various times or under various conditions using infusion therapy. Table 1002 identifies the amount of insulin that subject 512 may inject when consuming a normal-sized meal for breakfast, lunch, or dinner. A normal-sized meal may refer to the meal size that a particular subject 512 typically consumes or has been advised to consume by a healthcare provider. The specified units of insulin may refer to the amount of insulin that the automated blood glucose control system 510 provides to subject 512 on average when a user consumes the specified normal-sized meal. In some cases, table 1002 may further include recommended insulin doses for different sized meals. For example, each breakfast may indicate three different values (e.g., 5 units, 6 units, and 8 units) corresponding to a light or smaller than normal breakfast, a normal-sized breakfast, and a heavy or larger than normal breakfast.

It should be understood that the amount of insulin delivered may vary over time and/or based on the patient's condition at a particular time. Thus, as shown at the top of report 1000, the recommendations in the back-up therapy protocol are suggested for temporary use for a particular amount of time (e.g., up to 72 hours in the illustrated example). The amount of time the recommendations are valid may vary based on the subject 512, the amount of historical data collected (e.g., the size of the follow-up period), the amount of daily variation in the subject's blood glucose level, or any number of other factors that may affect the amount of time the back-up therapy protocol can be safely followed.

As shown by table 1002, the backup infusion therapy protocol may further specify an amount of long-acting insulin that the subject 512 is recommended to administer each day (or at specific times throughout the day). This long-acting insulin may be used in place of basal insulin that the glucose level control system 510 may provide periodically.

Additionally, table 1002 identifies the drop in glucose level that results from one unit of insulin. For example, as shown in table 1002, the automated blood glucose control system 510 has determined that one unit of insulin (e.g., 1/100 of a milliliter of insulin) can lower the blood glucose level of the subject 512 by 9 mg/dL. Thus, a user administering an infusion therapy can measure the blood glucose level of the subject 512, determine the difference between the measured blood glucose level and a desired setpoint or threshold glucose level, and, in response to determining that a correction dose is warranted (e.g., determining that the blood glucose is outside the desired setpoint range), divide this difference by 9 to determine the number of units of insulin to infuse.

Table 1004 of report 1000 provides an example of a backup pump therapy protocol. As shown, the backup pump therapy protocol can have the same therapy information as the backup infusion therapy protocol for meal times and correction factors. However, since the pump may be capable of providing a periodic basal therapy, the long-acting insulin units of the infusion therapy may be replaced with a basal rate indicating the rate at which the backup or replacement pump should administer insulin to the subject. As shown, the basal rate may vary over time. In the example shown, the basal rate is delivered for four different time periods that make up a 24-hour day. However, the basal rate may be divided into a fewer number of periods (e.g., two 12-hour blocks) or a greater number of periods (e.g., every four hours), with each time period potentially having a different basal rate as determined based on therapy history data provided by the automated blood glucose control system.

In some cases, the report 1000 may include additional data that may be tracked over the tracking period. This additional data may include any data that may facilitate the care of the subject 512 and/or the maintenance of the automatic glucose level control system 510. For example, some non-limiting examples of additional data that may be tracked using the process 800 or 900 and included in the report are shown in chart 1010 of the report 1000. For example, as shown in chart 1010, the report may include the average blood glucose level and/or corresponding estimated A1C percentage of the subject 512 over the tracking period. Additionally, the report 1000 may indicate the amount or percentage of time that the subject's blood glucose level is within and/or above a desired set value range. Similarly, the report 1000 may indicate the amount or percentage of time that the subject's blood glucose level is below a threshold blood glucose level.

Additionally, the report 1000 may indicate the average number of meal announcements per day. As shown in the chart 1010, the subject 512 for whom the exemplary report 1000 was generated made an average of 4.2 meal announcements, indicating that, on average, the subject consumed more than three meals per day. In some cases, the report may further indicate the type of meals announced (e.g., two breakfasts, one lunch, and one dinner). The second breakfast may be a large snack that is roughly equivalent in size to a small breakfast for the subject. Thus, the subject may have made an additional breakfast meal announcement. In some cases, the automatic glucose level control system 510 may support separate snack or other meal announcement options.

The report 1000 may further include the total amount of insulin administered to the subject per day and/or the total amount of counterregulator (e.g., glucagon) administered to the subject per day. Additionally, the report 1000 may indicate the amount of percentage of time that the automatic glucose level control system 510 is able to connect or communicate with the CGM sensor during the tracking period, which may correspond to the amount of time that the automatic glucose level control system 510 functions in an online mode during the tracking period.

FIG. 11 illustrates an exemplary control parameter modification report 1100 according to certain embodiments. As previously described, report 1100 may be a separate report generated, for example, using process 900. Alternatively, report 1100 may be included as a second report within report 1000.

The report 1100 may generally provide an indication of the number or percentage of times that the user modified one or more control parameters of the automated glucose level control system 510 during the tracking period. Additionally, similar to the report 1000, the report 1100 may identify a time or date range 1008 during which the tracking period 1006 occurred. In some cases, the user may interact with the report 1100 to determine the percentage of times that the user modified one or more control parameters during a subset of the tracking period. Similarly, the user may filter or narrow the date range to view other data described herein for the subset of the tracking period (e.g., the selected data range).

The report 1100 may include a graph 1102 showing the subject's blood glucose levels against the desired target set point range over a day during the tracking period. This day may be an average of the values obtained for each day over the tracking period or may show a specific selected day.

Additionally, report 1100 may include a table 1104 showing the percentage of times the user modified the blood glucose goal during a particular time period. In the non-limiting example table 1102, report 1100 shows two time periods: daytime and nighttime. However, it should be understood that table 1104 may show fewer or more time periods. Also, the time periods may show specific times for each time period (e.g., 9:00-21:00, 21:00-9:00).

As shown, table 1104 can show the percentage of times the user increased or decreased the glucose target set point. In addition, the report can show the percentage of times the user did not modify the glucose target set point or continued as usual. The target set points shown in table 1104 can refer to a single target value (e.g., 110 mg/dL, 125 mg/dL, 130 mg/dL, etc.) or can refer to a target set point range (e.g., 70-180 mg/dL).

Additionally, the report 1100 may indicate the number of times the user set a temporary glucose target during the tracking period (Temporary Targets 1106) or a selected date range. The report may also indicate the number of times the user paused therapy during the tracking period (e.g., Pause Insulin Therapy 1108) and/or a selected date range.

A subject's blood glucose may be affected by the subject's weight. Thus, the subject may provide an update on his/her weight to the automated blood glucose control system. In some such cases, the report may indicate the change in weight and when the weight parameter was modified (e.g., weight data 1110). In some cases, the report 1100 may be filtered to separately show data before and after the weight change. The weight data may be useful for a health care provider to determine, for example, whether a weight change may have been at least partially the basis for a user modification to a target glucose level. In general, the automated glucose level control system 510 (e.g., using blood glucose measurements) automatically takes into account the impact that a weight change may have on blood glucose control. However, the subject 512 may feel differently. The ability to collect correction data related to a user's corrections of the automatic glucose level control system 510 and correlate the data with weight change can assist a healthcare provider in better treating the subject 512, for example, by adjusting settings of the automatic glucose level control system 510, modifying an insulin prescription, educating the subject 512, or any other action that may improve the care of the subject 512.

In some cases, the report may omit changes to blood glucose target settings that are below a threshold. In other words, small changes that may be statistical noise may be ignored. Additionally, in some cases, the report may indicate when the control parameters were modified (e.g., with respect to a particular meal, such as bedtime, dinner, etc.). In some cases, the report may also indicate the duration of the changes to the glucose target settings or other control parameters.

12 illustrates an exemplary meal selection report 1200 that may be included as part of some implementations of the control parameter modification report 1100 of FIG. 11, according to certain embodiments. The report 1200 may include a table 1202 that identifies the average number of times per day that a user (e.g., subject 512) announces each meal type. Typically, a user will announce zero or one meal per day. However, in some cases, a user may announce a particular mealtime more than once, for example, to account for larger snacks that may be similar in size to a particular meal. Smaller snacks may often be handled by the control algorithms of the automatic glucose level control system 510 (e.g., by the correction insulin controller 626) without a meal announcement.

Additionally, table 1202 may identify the number of times a particular size meal was announced by the user, over a tracking period, or over a selected time period within the tracking period. For example, table 102 may show the number of times a regular size meal was announced, the number of times a smaller than regular size meal was announced, or the number of times a larger than regular size meal was announced.

Improved Automatic Blood Glucose Control An ambulatory medical device (AMD) may include a control system that autonomously provides a treatment to a subject based on, for example, the subject's health status (e.g., determined based on one or more measured physiological indicators or parameters of the subject). In some examples, the control system may determine the treatment time and/or intensity of the treatment during each treatment delivery based on one or more measured physiological parameters (e.g., using one or more subject sensors such as CGM sensors) and according to a predictive model that may include one or more control parameters. In some examples, the predictive model may be used to estimate the physiological effect of the treatment to tailor the treatment delivery according to the intended physiological effect. It may be desirable to adaptively adjust the value of the control parameter to optimize the treatment delivery to the subject in the presence of time-varying and subject-specific factors that may affect the physiological effect of the treatment delivery to the subject. In some cases, the AMD may be a portable drug device that regulates the level of an analyte in the subject's blood. An example of such a portable medication device is an automatic blood glucose control system (e.g., glucose level control system 510) that can automatically provide insulin and/or counterregulators (e.g., glucagon) to the subject 512 to help control the subject's 512 blood glucose level (BGL). Generally, a control algorithm is implemented by the automatic blood level glucose control system 510 and can determine when to deliver insulin and/or how much insulin to provide to the subject 512. Furthermore, the control algorithm can control both the continuous or periodic delivery of insulin (e.g., basal doses) and correction boluses that can be provided to adjust the subject's blood glucose level within a desired range. The control algorithm can use blood glucose level measurements obtained from a subject sensor (e.g., a sensor that measures one or more physiological parameters of the subject in real time), such as a continuous glucose monitoring (CGM) sensor that obtains automatic blood glucose measurements from the subject. Additionally, in some cases, the control algorithm may deliver a bolus of insulin in response to an indication of a meal to be or being consumed by the subject 512.

Insulin may be administered subcutaneously into the blood of the subject 512. For example, a glucose control system may deliver medications (e.g., insulin, glucagon) subcutaneously via an infusion set connected to a site on the subject's body. There is a delay, often referred to as a pharmacokinetic (PK) delay, between when the insulin is provided and when the amount of insulin in the subject's plasma reaches a particular concentration level, such as a maximum concentration. This amount of time may vary based on the type of insulin and/or the physiology of the particular subject. For example, with a fast-acting insulin, a bolus of insulin may take approximately 65 minutes to reach a maximum concentration in one subject's plasma, but 60, 64, or 70 minutes in another subject. For some other types of insulin, it may take 3-5 hours to reach a maximum concentration in the subject's plasma. Additionally, there may be a delay, called a pharmacodynamic (PD) delay, between the fluctuation in the amount of insulin in the subject's plasma and the resulting fluctuation in the glucose level in the subject's blood. In some examples, the pharmacodynamic (PD) delay value can be used to estimate the BGL based on the estimated concentration of insulin in the patient's blood.

In some cases, the blood glucose control system may implement a predictive algorithm based on a pharmacokinetic (PK) model to estimate the accumulation of insulin in the subject's plasma over time following subcutaneous administration of insulin to the subject. In some examples, the PK delay may be subject specific and/or may vary over time. Thus, in these examples, the PK model may include one or more parameters, referred to as control parameters, that may be subject specific and/or may vary over time. Examples of factors and parameters that may affect the PK delay and/or the control parameters of the PK model may include the type of insulin, blood glucose level (e.g., at the time of insulin administration), physiological characteristics of the subject, the health status of the subject, one or more physiological parameters of the subject, the time of administration, where the infusion set is placed, the amount of insulin administered, etc. Physiological characteristics may include characteristics shared among a large portion of the population (e.g., weight, sex, age, etc.), as well as characteristics that may be unique or specific to the subject or that may be shared among a small number of people (e.g., characteristics related to genetics). Differences between subjects' physiology may result in different optimal blood glucose ranges for each subject or for some subset of subjects. Additionally, physiological differences may also affect the absorption of insulin into the plasma. In other words, different subjects have different physiologies, which may result in different times for insulin absorption. Thus, the maximum concentration of glucose in the plasma may occur 65 minutes after delivery of a bolus of fast-acting insulin for one subject, but 60 or 70 minutes for another.

Thus, in some such examples, the blood glucose level control system 510 (e.g., the blood glucose control system of the AMD) can implement a method for adaptively changing one or more control parameters of the PK model used in its control algorithm to modify its predictions in order to maintain the BGL within a desired range. For example, the blood glucose control system can modify one or more control parameters using readings from one or more subject sensors (e.g., CGM) and/or information received from the subject (e.g., using a user interface of the AMD).

式中、Ｕ_０は単位（Ｕ）での皮下用量であり、Ｋはスケーリング定数であり、α_１およびα_２はモデルの制御パラメータとして使用することができる時定数である。いくつかの例では、皮下用量（Ｕ_０）が投与された時点から始まるインスリン吸収のピーク時間は、Ｔｍａｘと呼ばれることがあり、以下の式に基づいて決定することができる。

いくつかの例では、α_１とα_２は、（例えば、α_２＝１．５α_１などの式、または任意の他の線形もしくは非線形の数学的関係によって）関連付けることができる。いくつかのこのような例では、Ｔｍａｘのみを二指数ＰＫモデルの制御パラメータとして使用することができる。場合によっては、Ｔｍａｘは、被検者の血液中のインスリンの濃度が最大レベルに達する時間（例えば、皮下用量が投与される時間から開始する）を指してもよい。いくつかの他の例では、二指数ＰＫモデルを使用して、被検者の血液中の逆調節剤またはホルモン（例えば、グルカゴン）の蓄積を推定または決定することができる。皮下投与されたインスリンの蓄積量の保留効果は、総面積（用量Ｕ_０に起因して吸収され得るホルモン（例えば、インスリン）の総量の尺度を表すことができる

）と、ある時点でのＵ_０の消費部分の尺度を表すことができる

との差と見なすことができるため、式２を用いて計算することができる。 As indicated above, a blood glucose system such as the automated blood glucose level control system 510 can control the delivery or administration of insulin or a counterregulator based on a PK model and one or more blood glucose level measurements of a subject. In some examples, the PK model can be a bi-exponential PK model that can be used to estimate or determine the absorption or accumulation of subcutaneously administered insulin into the blood and/or the decay rate of insulin levels in the blood of a subject for a given value of a delivered dose of insulin. In some examples, the absorption of insulin over time according to a bi-exponential PK model can be represented by the following equation:

where _U0 is the subcutaneous dose in units (U), K is a scaling constant, and _α1 and _α2 are time constants that can be used as control parameters for the model. In some examples, the time of peak insulin absorption starting from the time the subcutaneous dose ( _U0 ) is administered may be referred to as Tmax and can be determined based on the following formula:

In some examples, α ₁ and α ₂ can be related (e.g., by an equation such as α ₂ =1.5α ₁ , or any other linear or non-linear mathematical relationship). In some such examples, only Tmax can be used as the control parameter of the biexponential PK model. In some cases, Tmax may refer to the time at which the concentration of insulin in the subject's blood reaches a maximum level (e.g., starting from the time the subcutaneous dose is administered). In some other examples, the biexponential PK model can be used to estimate or determine the accumulation of a counterregulator or hormone (e.g., glucagon) in the subject's blood. The retention effect of the accumulated amount of subcutaneously administered insulin can represent a measure of the total amount of hormone (e.g., insulin) that can be absorbed due to the total area (dose U _{0 )} .

) can be used to represent a measure of the consumed portion of _U0 at a given time.

Since it can be regarded as the difference between

In many cases, blood glucose control systems are configured to maintain a subject's blood glucose within a particular range (e.g., normal range). When blood glucose rises or falls, the blood glucose control system can administer a particular amount of insulin or counter regulator to the subject to bring the subject's blood glucose level back within a desired range or closer to a desired set point. As explained above, it may take very little time for the drug to be absorbed into the subject's bloodstream. Thus, a PK model (e.g., a bi-exponential PK model) can be used to determine the amount of insulin or counter regulator to provide to the subject and maintain the subject's blood glucose within a particular range. In some examples, the PK model (e.g., a bi-exponential PK model) can be used to predict the subject's concentration of insulin, blood glucose level over time as insulin or counter regulator is administered. In some cases, the control parameter values of the PK model may be set by a healthcare provider based on default values obtained through clinical testing, and/or based on the subject's individualized treatment plan, which may be determined based on the subject's clinical testing, and/or based on the subject's healthcare provider's evaluation, which may be determined based on the subject's testing.

However, as previously indicated, the pharmacokinetic delay and control parameters of the PK model may be subject specific and/or may change over time due to various factors. Thus, although clinical data may determine optimal or recommended values of control parameters for an average subject through one or more tests, the determined data may not be optimal for a particular subject. Furthermore, individualized treatment plans are typically based on measurements at specific time points. While these measurements at specific time points may provide good guidelines for treatment, the optimal values of control parameters for a subject may change at different times of the day due to different activities, due to changes in the subject over the course of their life, or for several other reasons.

The glucose level control system 510 of the present disclosure can implement a method or process of autonomously and/or automatically modifying one or more control parameters of a control algorithm or a model used by a control algorithm to modify a therapy provided to a subject using the glucose level control system 510. The method may be executed by a hardware processor 530 and/or a controller 518 that controls the administration of the therapy. The system can provide a therapy (e.g., insulin) to a subject in response to a determination of the subject's blood glucose level. The blood glucose level may be determined based at least in part on a glucose level signal obtained from a glucose level sensor operably connected to the subject. The determination of the therapy (e.g., amount of insulin or counter regulator) can be based at least in part on the blood glucose level and/or the bi-exponential model. Additionally, the determination of the therapy can be based at least in part on values or settings of one or more control parameters of the blood glucose control system. The one or more control parameters may be, or may correspond to, one or more parameters of a bi-exponential PK model or any other model or control algorithm used to control the administration of a therapy to a subject.

As described above, the system 510 can provide therapy based on the value or setting of one or more control parameters. The value or setting of the one or more control parameters can be based on an initial setting of the blood glucose control system 510 by a healthcare provider, subject, or other user. Additionally, the initial setting may be based on clinical or acquired data specific to the subject. In some cases, the control parameter may be a time constant (e.g., Tmax in a biexponential PK model) used by the control algorithm of the blood glucose control system. The time constant may be used in the calculation of the accumulated amount of insulin in the subject by the control algorithm. Additionally, the control parameter may be used to control the insulin dosing response of the control algorithm to the blood glucose fluctuations of the subject as indicated by the glucose level signal obtained from the glucose level sensor. In some cases, the control parameter may be or be related to Tmax (e.g., as defined by Equation 2). For example, the control parameter may be an estimate of Tmax or a fraction of Tmax (e.g., 0.5). As explained above, Tmax may be the peak time of insulin absorption, or the amount of time from insulin administration until the concentration of insulin reaches its maximum concentration in the subject's blood.

Additionally, the control parameter may be associated with a set point or target blood glucose level, or blood glucose range. For example, the control parameter may relate to the time when an estimate of "insulin on board" (e.g., the amount of insulin in the subject, as determined by a model of insulin accumulation and/or utilization in the subject) falls below a threshold. As another example, the control parameter may be the clearance time of an insulin bolus (e.g., an estimate of the amount of time an administered bolus of insulin is utilized by the subject). In some cases, the control parameter may relate to T1/2, which corresponds to the time at which the concentration of insulin in plasma reaches half of its maximum concentration in plasma. In some cases, the control parameter may be a parameter that can be used to calculate Tmax or T1/2.

In some examples, the system 510 can determine the effect of the delivered therapy (referred to herein as therapeutic effect or effect). For example, the therapeutic effect may be determined by analyzing the glycemic control of blood glucose in the subject's blood (e.g., variation in BGL or delivered therapy over a measurement period) as indicated by a glucose level signal received from a glucose sensor (e.g., a CGM sensor). In some cases, the control system can measure or determine the effect of the delivered therapy over time. In some such cases, the therapeutic effect may be determined based on the variation in BGL and/or the amount of therapy delivered over time. Further, in some cases, the system may continue to deliver therapy to the subject over several therapy delivery times or instances, and may average or otherwise aggregate the measured or determined effect of the therapy over several therapy delivery times or instances. In some other examples, the system 510 can determine the therapeutic effect based at least in part on input received from the subject. The input received from the subject may include subjective or objective effects. The input received from the subject may include, for example, manual blood glucose level measurements obtained using a test strip. Another example of an input may be an indication of lightheadedness, shortness of breath, headache, or any other objective or subjective effect identified by the subject.

Based at least in part on the provided treatment and the measured or determined effect of the treatment (e.g., change in blood glucose level resulting from the treatment), the control system 510 can autonomously determine a modification to one or more control parameters. For example, the control system can modify the Tmax value used by the control algorithm (or a PK model used in the control algorithm) to, for example, improve the effect of a subsequent treatment that may be provided to the subject. Thus, the directional modification (e.g., increase or decrease) of the control parameter value may depend on the measured or determined effect of the provided treatment based on the initial or prior value of the control parameter. Additionally, the directional modification of the control parameter value may depend on the difference between the determined or measured effect of the blood glucose treatment and the expected effect of the blood glucose treatment (e.g., calculated based on a PK model). In some examples, the directional modification of the control parameter can be determined based on the amount of the provided treatment dose and/or the measured BGL of the subject during and during one or more previous treatment deliveries.

In some examples, the pharmacodynamic delay of the subject may be a known value. In these examples, the amount of insulin absorbed in the subject's blood may be estimated based on the measurement of BGL received from the glucose sensor. In some such examples, the directional correction may depend on the difference between a calculated value of insulin absorbed based on a PK model (e.g., a biexponential PK model) having a selected value of Tmax and an estimate of insulin absorbed based on the measurement of BGL received from the glucose sensor.

Using the modified control parameters, the system 510 can determine a treatment to deliver to the subject 512 at the treatment delivery time. Similar to the initial control parameters, a treatment can be delivered during one or more treatment delivery times based on the modified control parameters. The system can determine the effect of a treatment delivered based on the modified control parameters using one or more of the embodiments described above with respect to a treatment delivered using the initial control parameters.

In some examples, the control system can compare a measured, determined, or reported effect (e.g., physiological effect) of a treatment delivered using an initial value of the control parameter to an effect of a treatment delivered using a modified value of the control parameter. Based on this comparison, the control system can determine which value of the control parameter is preferred for the subject. In some examples, the comparison may be performed in real time or substantially real time. Additionally, the comparison may be performed by the system 510 without user interaction. The comparison may be performed based on one or more comparison criteria using a comparison method.

The comparison method may be based on a finite number of treatment effects determined or measured at discrete times, or may be based on a continuous temporal variation of the effect over a period of time. In some examples, the comparison method may involve a statistical analysis of the measured or determined effects resulting from the use of initial and modified values of the control parameter. The comparison criteria may be based on the effect, or on the temporal variation of the effect over a period of time. For example, the preferred control parameter value may be a value that causes the subject's blood glucose level to remain within a desired range or closer to the subject's set point level. Thus, the system may set or maintain the control parameter to have a value that produced a blood glucose level closer to the desired range or set point for the subject for subsequent treatments.

In some cases, the system 510 can repeat the process for different control parameter values, allowing the system to improve blood glucose control for the subject over time. In subsequent runs of the process, the initial control parameter values may not be the initial values, but may be the most recently selected values for the control parameters based on the determined effect of the control parameters.

In some cases, the determination of the second or revised value of the control parameter or the modification of the control parameter may be triggered based on the subject's glucose level not meeting a threshold value. Alternatively, or in addition, the process of modifying the control parameter value may be triggered based on the difference between the subject's predicted glucose value and the subject's predicted glucose value after administering the treatment exceeding a threshold value.

Using the embodiments described herein, the values of the control parameters may be autonomously modified without subject or user interaction with the blood glucose control system. In other words, the blood glucose control system may automatically adjust and/or refine the control parameters used by the control algorithm for the subject's glycemic control.

As previously discussed, the blood glucose control system may provide both insulin therapy and counter regulator therapy to the subject. In some cases, the blood glucose control system may provide only insulin therapy. In some such cases, the blood glucose control system may output an indication of the amount of counter regulator that may or should be administered to the subject based on the detected subject condition.

The active control parameter value used by the control parameter may remain active until the next time a therapy modification process occurs. In some cases, the execution of the therapy modification process is performed continuously by modifying the control parameter value based at least in part on the determined effect of the previous control parameter value. In other cases, the therapy modification process is performed until the determined therapy effect meets a desired threshold (e.g., when the detected blood glucose level is within a set value or a median set value threshold). In some cases, the therapy modification process is performed a set number of times, and the control parameter value that produces the best results (e.g., close to the desired blood glucose level) is set as the active control parameter for the subsequent therapy. In some cases, providing therapy to different parts of the subject's body (e.g., back, stomach, legs, or arms) may result in different blood glucose absorption rates (associated with different PK delays). Thus, in some such cases, the therapy modification process may be performed each time the infusion set used to deliver therapy is moved to a different part of the subject.

Example of an Automatic Blood Glucose Control Improvement Process FIG. 13 presents a flow chart of an exemplary automatic blood glucose control improvement process according to certain embodiments. Process 1300 may be performed by any system capable of autonomously and/or automatically modifying a control algorithm and/or control parameters affecting the execution of the control algorithm based on feedback (e.g., from a blood glucose signal) regarding a treatment administered to subject 512. For example, process 1300 may be performed by one or more elements of glucose level control system 510. In some cases, at least certain operations of process 1300 may be performed by a separate computing system that receives blood glucose data from glucose level control system 510. While one or more different systems can perform one or more operations of process 1300, for ease of explanation and not to limit the present disclosure, process 1300 is described with respect to a particular system.

Process 1300 may be performed automatically and without user interaction. In some cases, a user may trigger process 1300 via a command or interaction with a user interface. However, once process 1300 is triggered, process 1300 may be performed automatically. Additionally, process 1300 may be performed continuously, periodically, or in response to a trigger. The trigger may be time-based and/or based on a measurement of the subject's glucose level. For example, the trigger may correspond to a determination that the subject's glucose level differs by more than a threshold value from a predicted glucose level predicted by the glucose level control algorithm based on administration of a medication. Additionally, the trigger may be based on activation or initial use of glucose level control system 510 by subject 512.

Process 1300 begins at block 1302, where the glucose level control system 510 receives a glucose level signal corresponding to the glucose level of the subject 512. The glucose level signal may be received from a glucose sensor capable of measuring the glucose level in the subject's blood. For example, the sensor may be a continuous glucose monitoring (CGM) sensor. Block 1302 may include one or more of the embodiments described above with respect to blocks 802 or 902.

In block 1304, the glucose level control system 510 provides a first treatment to the subject 512 during a first treatment period. The first treatment can be based at least in part on the glucose level signal and a first value of a control parameter. The control parameter can include any control parameter that affects the operation of the glucose level control system 510 and/or the execution of a control algorithm of the glucose level control system 510. The control algorithm can include any control algorithm used to determine a dose of a medication (e.g., insulin) to administer to the subject 512. In other words, the controller 518 or processor 530 can use the control algorithm to generate a dose control signal based at least in part on the value of the control parameter (e.g., the first value of block 1304) to cause the delivery device 514 to administer a dose of insulin or other medication.

In some cases, the control algorithm may be based on a PK model (Equation 2). Additionally, in some cases, the control parameter may be Tmax, which may be calculated using Equation 3. In other cases, the control parameter may be T1 _/2 , which may relate to the amount of time for the dose of insulin in the bloodstream to drop to 1/2 of the maximum concentration in the blood resulting from the dose administered to the subject 512. In some cases, the control parameter corresponds to the time for insulin in the subject's plasma to reach a particular concentration level after administration of the insulin dose. Additionally, in some cases, the control parameter may be the time constants α1 and α
2. In some embodiments, the control parameters may be used by the control algorithm to account for and/or determine the accumulation of insulin (or other medication) in the subject 512 and/or the rate of depletion of insulin (or other medication) in the subject 512. In some cases, the control parameters may be used to control the insulin dosing response of the control algorithm to blood glucose fluctuations of the subject as indicated by the glucose level signal received in block 1302.

In some instances, the control parameter may relate to at least one time constant used in the control algorithm's calculation of insulin accumulation in the subject, such as one or more of the time constants _α1 and _α2 that may be used in the calculation of Tmax. In some cases, the control parameter may correspond to a rate of insulin decline in the subject 512. In some cases, the control parameter may relate to a target set point or target set point range for maintaining or attempting to maintain the blood glucose level of the subject 512.

The first therapy may correspond to a single dose of insulin to the subject 512. This single dose of insulin may be any type of insulin administered for any reason. For example, the insulin dose may be a basal insulin dose, a priming dose, a dose delivered in response to a meal notification, or a correction dose of insulin. Additionally, the first therapy may be a drug other than insulin, such as a counterregulator (e.g., glucagon). In some cases, the first therapy may be multiple drug (e.g., insulin and/or counterregulator) doses delivered or administered to the subject 512 over the first therapy period. Additionally, the multiple drug doses may include various types of drug doses, such as one or more basal doses, one or more meal doses associated with one or more meal notifications, one or more correction doses, etc.

The first treatment period may be a time period corresponding to a single drug dose. Alternatively, the first treatment period may be a time period encompassing multiple drug doses. Furthermore, the first treatment period may be a time period associated with a defined length of time. Alternatively, or in addition, the first treatment period may be defined based on a number of drug delivery periods. In other words, the time period may vary based on the amount of time it takes to deliver or administer a specified number of drug doses (of any type or of a particular type).

The first value may be selected based on prior treatment or prior execution of the process 1300. In some cases, the first value is selected based on a baseline value. The baseline value may be associated with clinical data or may be determined based on an initial operation of the glucose level control system 510 at some time prior to execution of the process 1300. Alternatively or in addition, the first value may be selected based on clinical data or a particular prescription of the subject 512. In some cases, the first value may be based on clinical data of an average user or an average user who shares particular physiological data with the subject 512. In some cases, the first value is determined based on a healthcare provider's evaluation of the subject 512. Additionally, the first value may be determined based on an injection site of the glucose level control system 510 (e.g., back, stomach, leg, etc.). In some cases, the first value may be selected based on demographics or characteristics of the subject 512. For example, the first value may be based on the gender, weight, body mass, or age of the subject 512.

In block 1306, the glucose level control system 510 determines a first effect that corresponds at least in part to or is attributable to the first treatment. Determining the first effect may include receiving a glucose level signal from a glucose level sensor operably connected to the subject. The glucose level signal may be a subsequent or updated glucose reading that is newer than the glucose level signal received in block 1302. The glucose level signal received in block 1302 may be used to determine a treatment to administer to the subject 512, and the glucose level signal received in block 1306 may be used to determine the results of the administered treatment. It should be appreciated that the glucose level signal may be received continuously or periodically and may be used to both determine a treatment to administer and to determine the effect of the administered treatment.

In some cases, determining the first effect can include analyzing the glycemic control of the subject's blood glucose as indicated by the glucose level signal. Analyzing the glycemic control of the subject's blood glucose can include tracking the blood glucose levels of the subject 512 over time. Additionally, analyzing the glycemic control of the subject's blood glucose can include comparing the blood glucose levels of the subject 512 over time to a predicted blood glucose level of the subject 512 over time as predicted based on the PK model used in the control algorithm with the values selected for the control parameters. As mentioned above, in some examples, the blood glucose levels of the subject 512 measured over time can be used to calculate the accumulation and/or decrease of insulin levels in the subject's blood. In these examples, analyzing the glycemic control of the subject's blood glucose can include determining whether or to what extent the insulin (or other drug) accumulation and/or reduction calculated using a PK model (e.g., a biexponential PK model) and control parameter values used in the control algorithm matches the insulin (or other drug) accumulation or reduction estimated based on the measured blood glucose level (e.g., obtained from a CGM sensor). In some cases, the first effect may be determined at least in part by analyzing one or more signals received from one or more subject sensors measuring one or more physiological parameters of the subject (e.g., heart rate, body temperature, etc.).

In yet other examples, the first effect may be determined based on input received from the subject (e.g., using a user interface of the AMD). In some cases, the first effect may be determined based at least in part on an assessment or input provided by the subject 512 (e.g., using a user interface) regarding the first value or the first effect. For example, if the subject 512 experiences lightheadedness, dizziness, headache, nausea, or other discomfort during the first treatment period, the subject 512 may indicate, for example, via a touch screen display of the AMD, how the subject 512 is feeling.

At block 1308, the glucose level control system 510 obtains a second value of the control parameter. This second value may be determined autonomously. Further, in some cases, the second value may be determined automatically. In some cases, the second value is determined at least in part based on a user triggering the blood glucose control improvement process 1300. In some such cases, the control system may determine the second value and present it to the user via the user interface 534 of the control system 510.

In some other examples, the second value may be obtained from a user interface 534 of the blood glucose control system 510 (e.g., in response to a user interaction with the user interface). In some examples, the second value may be obtained from a computing system connected to or otherwise in communication with the glucose control system. The communication connection may be a wired connection or a wireless connection. Furthermore, the wireless connection may be a direct connection (e.g., via Bluetooth or other short-range communication technology) or a connection over a network (e.g., a local area network, a wide area network, a cellular network, etc.).

The second value may be an increase or decrease of the control parameter compared to the first value. The second value may be limited to a certain maximum change from the first value. Furthermore, the second value may be selected at least in part based on the first effect. For example, if the first effect corresponding to the first value causes blood glucose to be near the upper end of the set value range, the second value may be selected to bring the blood glucose level closer to the middle of the set value range. Furthermore, the second value may be selected at least in part based on characteristics of the subject 512, such as age, weight, sex, or any other characteristics that may affect blood glucose management. In some examples, the second value may be selected at least in part based on the first effect, which is determined based on an assessment provided by the subject 512 in an attempt to reduce symptoms felt by the subject 512.

In some cases, the second value of the control parameter may be generated based at least in part on the output of a function defined based on the baseline value of the control parameter and the glycemic control of the subject. In some examples, the glycemic control of the subject may include a measurement of the glucose level in the subject's blood (e.g., provided by the CGM) and/or an amount of treatment (e.g., a dose of insulin or counter-regulatory hormone) provided during the first treatment period. The baseline value of the control parameter may correspond to the first value used to provide treatment in block 1304. This baseline value may be the last known optimal value for the subject prior to any changes in the subject (e.g., weight change, insulin type, or metabolic changes, etc.). Alternatively or additionally, the baseline value may be a value determined by a healthcare provider. In some cases, the second value of the control parameter is based at least in part on the glycemic control indicated by the glucose level signal.

In some cases, the second value may be a correction to Tmax or T1 _/2 . It should be understood that Tmax and/or T1 _/ 2 may be based, at least in part, on the physiology or biochemistry of the subject 512. Thus, setting either Tmax or T1/ ₂ to the setting of the first and second values may refer to setting a parameter of the control algorithm or a PK model used by the control algorithm that represents or corresponds to Tmax and/or T1/ ₂ . For example, setting the first and second values may include setting one or more control parameters that may be used to determine or estimate the Tmax and/or T1 _/2 of the subject 512. However, the set values may differ from the actual values of Tmax and/or T1 _/2 of the subject 512. Furthermore, since Tmax and/or T1 _/2 may vary from subject to subject, it may not always be possible to explicitly set or determine the Tmax and/or T1 _/2 of a subject. Alternatively, Tmax and/or T1 _/2 may be estimated or determined by comparing the effects and/or blood glucose levels determined for different control parameter values that correspond at least in part to Tmax and/or T1 _/2 . Using process 1300, the control parameters may be iteratively approached to the actual Tmax and/or T1 _/2 of the subject 512, or may be approached to within a threshold of the actual Tmax and/or T1/2 of the subject 512. Alternatively, using process 1300, the control parameters (such as one or more of the time constants _α1 and _α2 ) may be iteratively approached to a value corresponding to the actual _Tmax and/or T1 _/2 of the subject 512.

At block 1310, the glucose level control system 510 changes the control parameter to a second value. Changing the control parameter to a second value changes the operation or execution of the control algorithm. This change in the execution of the control algorithm may result in a change in one or more factors associated with providing therapy to the subject 512. For example, the change in the execution of the control algorithm may result in a change in the amount of drug delivered, the timing of delivery of the drug, the rate at which a dose of the drug is delivered to the subject 512, a target set point or range for the subject's blood glucose, a threshold used in determining whether to deliver a drug (e.g., a difference between the threshold and a target set point), or any other factor that may affect the therapy delivered to the subject 512.

In block 1312, the glucose level control system 510 provides a second therapy to the subject 512 during a second therapy period. The second therapy is based at least in part on the updated control parameter, which is updated to a second value in block 1310. Like the first therapy, the second therapy can refer to one or more drug doses. Additionally, the second therapy period can refer to a particular amount of time, an amount of time for delivering a particular number of drug doses, or a particular number of drug doses. In some cases, block 1312 can include one or more of the embodiments described with respect to block 1304, but using a second value of the control parameter for the second therapy period. In some examples, the duration of the second therapy period may be equal to the duration of the first period. In some other examples, the number of therapies delivered during the second therapy period may be equal to the number of therapies delivered during the first second therapy period.

In block 1314, the glucose level control system 510 determines a second effect that corresponds at least in part to the second treatment. Block 1314 may include one or more of the embodiments described with respect to block 1306 with respect to the second treatment.

In block 1316, the glucose level control system 510 selects one of the first value or the second value based at least in part on the comparison of the first effect and the second effect. The comparison of the first effect and the second effect may be performed autonomously without any action by the user. The glucose level control system 510 may select one of the first value or the second value as the current or active value of the control parameter based on whether the first effect or the second effect leads to improved care for the subject 512 (e.g., closer to the desired set point over a longer time period, or less variability in blood glucose values, or any other factor that a healthcare provider may use to evaluate the success of diabetes management). In some cases, the glucose level control system 510 selects a third value for the current or active value of the control parameter. The third value may be selected based on the comparison of the first effect and the second effect. For example, if the first effect is determined to be more favorable than the second effect, the third value may be selected based on a change to the first value in the opposite direction to the change made to the first value to obtain the second value. For example, if in a prior example where a first effect was determined to be preferable to a second effect, the first value was selected to correspond to a Tmax of 60 minutes and the second value was selected to correspond to a Tmax of a longer time period (e.g., 65 or 70 minutes), then the third value may be selected to correspond to a Tmax of a shorter time period (e.g., 50 or 55 minutes).

Comparing the first effect to the second effect can include determining whether the first value or the second value caused the glucose level of the subject 512 to be closer to a target set value and/or maintained the glucose level of the subject 512 within a target range for a longer time period. In some cases, comparing the first effect to the second effect can include determining whether the first value or the second value resulted in a more stable blood glucose level of the subject 512 or resulted in less variability in the blood glucose level of the subject 512. In some cases, comparing the first effect to the second effect can include determining whether the first value or the second value resulted in more and/or greater deviations of the blood glucose level of the subject 512 from the target blood glucose range.

The comparison of the first effect to the second effect may be performed in real time or substantially in real time, taking into account the processing speed of the hardware processor 530 or the glucose level control system 510. Thus, in some cases, the comparison of the first effect to the second effect may be performed at the time of determining the second effect.

In some embodiments, the comparison of the first effect with the second effect can include a statistical comparison or analysis of the first effect with the second effect. In some cases, the comparison of the first effect with the second effect can include determining whether the second treatment resulted in a statistically significant improvement in treatment (e.g., glycemic control) compared to the first treatment. The statistically significant improvement can vary depending on the subject or the subject's condition. The comparison can also include determining whether there was a statistically significant increase in a risk factor (e.g., hypoglycemia) during the second treatment period compared to the first treatment period. In some embodiments, the statistically significant improvement can be an improvement determined based on a first statistical analysis of a set of data associated with the first effect and a second statistical analysis associated with a second set of data associated with the second effect. For example, the first and second statistical analyses can include calculating the mean and variance of blood glucose levels measured during the first and second treatment periods, respectively. In some examples, the improvement can be determined by comparing the mean and variance of blood glucose levels measured during the first and second treatment periods. In some examples, the improvement may be determined by comparing the mean and variance of the blood glucose levels measured during the first and second treatment periods to one or more reference values. The reference values may be values provided by a healthcare provider or a user and may be stored in the memory 540 of the glucose level control system 510. In some examples, the first and second treatment periods may be long enough to include multiple treatment deliveries (e.g., infusions of glucose and/or glucagon) during each period. In some embodiments, the improvement may be determined by comparison to other statistics calculated at least in part based on the blood glucose levels measured during the first and second treatment periods. In some such embodiments, the statistics may be specific statistics defined for comparing the effectiveness of treatments (e.g., drug delivery to control the subject's blood glucose level).

In some cases, the first and/or second may be output to a user (e.g., a subject or a parent) via a user interface of the glucose control system and/or a computing system (e.g., a smartphone, laptop, personal computer, etc.). In some examples, the user can use the determined effect to adjust the value of a control parameter.

In some cases, values that better manage the subject's 512 blood glucose can be output to a user (e.g., subject or parent). The user can then configure the glucose level control system 510 based on the selected control parameter values. Alternatively, or in addition, the glucose level control system 510 can automatically modify the values of the control parameters. In some cases, the user may be provided with an opportunity to confirm the modification. In other cases, the modification may be made automatically without confirmation. However, the modification may be presented to the user (e.g., subject or healthcare provider) and/or recorded in a treatment log.

In some cases, the comparison is performed by another computing system in communication with the glucose level control system 510. For example, the glucose level control system 510 can transmit the glucose level signal, data determined from the glucose level signal, and/or the evaluation received from the subject, indicative of the effect of blood glucose control, to another computing system, such as a local computing system, a smartphone, or a cloud-based computing system. Additionally, the glucose level control system 510 can transmit data associated with the control parameter values and the administration of medication to the subject 512 to the computing system. The computing system can determine values of the control parameters that better manage the blood glucose level of the subject 512. The computing system can configure the glucose level control system 510 with the selected values. Alternatively or additionally, the selected values may be output to a user, who can configure the glucose level control system 510 with the selected values.

In block 1318, the glucose level control system 510 provides therapy to the subject 512 based on the selected values of the control parameters selected in block 1316. The therapy provided in block 1318 may be provided during a third treatment period that is some time after the first and second treatment periods. Thus, during the first two time periods, the first and second values may be used for the control parameters, respectively, to determine values that result in a better outcome or improved care for the subject 512. In subsequent time periods, the values that resulted in a better outcome for the subject 512 may be used to provide future care for the subject 512. Alternatively, new values that are neither the first nor the second values may be used to provide subsequent care in an attempt to find values of the control parameters that may provide a better or improved level of care for the subject 512 (e.g., closer to the desired target glucose level for a longer time period).

In some examples, providing therapy to the subject can include generating a dose control signal to a delivery device 514 (e.g., an infusion pump coupled by a catheter to the subcutaneous space of the subject 512) that delivers an amount of medication (e.g., insulin or a counterregulator) to the subject, the amount of which can be determined by the dose signal.

Providing treatment to the subject 512 based on the selected value may include configuring the glucose level control system 510 to provide treatment to the subject 512 during a third treatment period based at least in part on the active control parameter value. In some cases, configuring the glucose level control system 510 to provide treatment to the subject 512 based at least in part on the active control parameter value may terminate the process 1300. In other cases, the process 1300 may be repeated. Repeating the process 1300 may include using the selected value (e.g., the first or second value from a previous iteration of the process 1300) as the first value when performing the operation associated with block 1304. The second value generated in block 1308 may be a new value that was not used during the previous iteration of the process 1300.

Process 1300 may be repeated until the difference between the first effect and the second effect is less than the threshold difference. Alternatively, or in addition, process 1300 may be repeated periodically, for a particular number of iterations, in response to a command, or in response to determining that the blood glucose of subject 512 does not meet a particular threshold for a particular amount of time.

In some examples, the process 1300 may be used to modify two or more control parameters of the glucose system (or the control algorithm used by the control system). In some such examples, the process 1300 may be used to adjust a first control parameter during a first correction period beginning at block 1302 and ending at block 1318, and to adjust a second control parameter during a second correction period beginning again at block 1302 and ending at block 1318. The second correction period may be immediately after the first correction period or may be delayed a particular amount of time. In some examples, the control system may determine when the second control parameter should be modified following the modification of the first parameter. In some examples, the delay may be determined based at least in part on the glycemic control measured based on the glucose signal (e.g., received from a CGM sensor). In some other examples, the delay may be determined based on input received from a user. In some examples, the modification of the second control parameter may be determined based at least in part on the determined modification of the first control parameter.

In some examples, after adjusting the first and second control parameters, a third control parameter may be adjusted during a third time period. The adjustment of the third control parameter may occur immediately after the adjustment of the second control parameter, or may occur with a delay. The delay may be determined at least in part based on the subject's glycemic control after the second control parameter is adjusted. In some examples, the glucose control system may be configured to sequentially adjust the first and second, or the first, second and third control parameters, when the subject's glycemic control meets one or more threshold conditions. In some examples, the duration of the time period during which the control parameters are adjusted may be delayed from the duration of the other parameters.

In some embodiments, a modified version of process 1300 may be used to determine a value (e.g., an optimal value) of the control parameter. In some such examples, after determining the second effect in block 1314, the control system may skip blocks 1316 and 1318 and instead obtain a third value of the control parameter. In some examples, this third value may be determined based at least in part on the second effect determined in block 1314. In some examples, this third value may be determined autonomously. Further, in some cases, the third value may be determined automatically. In some cases, the third value is determined at least in part based on a user triggering the blood glucose control improvement process 1300. In some such cases, the control system may determine the third value and present it to the user via the user interface 534 of the control system 510. In some examples, the third value may be provided by the user via the user interface 534 of the control system 510. In some examples, after obtaining the third value, the system may provide a treatment to the subject based on the third value. This modified version of process 1300 may be repeated several times. In some examples, this modified version may be repeated until the difference between the last two subsequent effects is less than a threshold difference. Alternatively, or in addition, the modified version of process 1300 may be repeated periodically for a particular number of iterations, in response to a command, or in response to determining that the blood glucose of subject 512 does not meet a particular threshold for a particular time.

As described, process 1300 may be used to modify one or more control parameters that affect the delivery of insulin. However, process 1300 is not so limited and may be used to modify one or more control parameters that affect the delivery of other agents, such as counter regulators (e.g., glucagon, dextrose, etc.). In some cases, process 1300 may be used to recommend changes in insulin and/or counter regulator delivery without modifying the delivery. This may be advantageous for generating counter regulator recommendations in single hormone glucose level control systems 510 that do not support counter regulators or that support the use of counter regulators but where counter regulators are not available.

Furthermore, when process 1300 is used to modify multiple control parameters, at least two or more of the control parameters may be related to each other. For example, when the control parameters include time constants α1 and α2, a relationship may exist between _α1 and _α2 such that modifying α1 modifies α2. For example, _α2 may be equal to 1.5 times _α1 .

The value of the control parameter (e.g., the first value or the second value) set as the active parameter in block 1316 may be used by the control algorithm to provide therapy to the subject 512 for a particular time period or until the process 1300 is repeated. As previously described, in some cases, the process 1300 is repeated periodically and/or in response to a trigger, such as a blood glucose value or an average blood glucose value over a time period, or an indication of a site change for connecting the glucose level control system 510 to the subject 512 (e.g., a change in the location of an infusion set used to provide a subcutaneous dose).

Hypothetical Example As previously mentioned, the peak time of absorption of insulin may be referred to as Tmax. Different insulin types may have different times to peak and different subjects to absorb into the blood of a subject. For example, in one hypothetical example, the overall Tmax among subjects for fast-acting insulin lispro and insulin aspart may be determined to be about 65 minutes, while the overall Tmax among subjects using ultrafast-acting insulin, such as insulin aspart injections marketed under the Fiasp brand, which has a formulation that shortens the time to peak absorption, may be determined to be about 40 minutes. When using an automated blood glucose level control system (such as glucose level control system 510) with control parameters corresponding to a Tmax set at 65 minutes, there may be no statistically significant improvement in average glucose levels or frequency of hypoglycemia when using ultrafast-acting insulin compared to using fast-acting insulin. In this comparison, Tmax is held constant while changing the type of insulin used.

When adjusting the values of the control parameters of an automated blood glucose level control system to use different Tmax settings, in a hypothetical example, lowering the Tmax when using a fast-acting insulin would result in lower average glucose. In this example, three cohorts of subjects employ a control algorithm that uses modified Tmax values when using a blood glucose control system with a fast-acting insulin such as Fiasp. The first cohort uses a blood glucose level control system configured with a Tmax of 65 minutes in the first week of treatment and a lower Tmax (e.g., 50 minutes, etc.) in subsequent weeks of treatment. The second cohort uses a blood glucose level control system configured with a Tmax of 65 minutes in the first week of treatment and an even lower Tmax (e.g., 40 minutes, etc.) in subsequent weeks of treatment. The third cohort uses a blood glucose level control system configured with a Tmax of 65 minutes in the first week of treatment and a significantly lower Tmax (e.g., 30 minutes, etc.) in subsequent weeks of treatment. Comparison of changes in Tmax within and between cohorts demonstrated that lower Tmax was associated with lower mean glucose levels, with no statistically significant increase or decrease in hypoglycemia.

If Tmax is shorter than the physiological insulin absorption peak time, the blood glucose level control system may overlap or administer multiple insulin doses within a certain window of time, increasing the risk of hypoglycemia. This may occur because if Tmax is set below the actual peak insulin absorption time, the blood glucose level control system may erroneously recognize a lower blood glucose concentration as the maximum blood glucose level concentration.

By comparing the effects of different Tmax settings using the process 1300, it is possible to optimize the Tmax setting for a subject and/or a particular type of insulin. In some examples, the comparison may be based on one or more statistical methods. For example, using glucose concentration data collected during a treatment period (e.g., using a CGM sensor), the control system may determine whether there is a statistically significant difference in the average glucose level during the later period using a different Tmax value compared to a previous evaluation period. If a subsequent or newer value used for Tmax results in an improved effect, Tmax or a control parameter of the blood glucose level control system 510 corresponding to Tmax may be set to the newer value, and the change in the control parameter value may be made automatically upon determination of a statistically significant improvement or after generating a notice of potential improvement and receiving a confirmation that a change in the control parameter value should be made. After collecting glucose signals for the subject 512 for a period of time at the default or previous value of Tmax, the value of Tmax may be lowered by a significant amount from the initial Tmax. For example, the control algorithm may automatically modify the Tmax or associated time constant to reflect a decrease in Tmax of at least 10 minutes, at least 5 minutes, at least 2 minutes, 15 minutes or less, 20 minutes or less, 30 minutes or less, or by a change within a range spanning any two of the aforementioned values in this text (the aforementioned values are included in the range). The system may perform a statistical analysis between a previous data set associated with a higher Tmax and a current data set associated with a lower Tmax. If the controller of the blood glucose level control system determines that there is a significant or statistically significant improvement (e.g., improvement above a threshold) in the subject's average glucose level with little or no increase in hypoglycemic or risk events, the system may adopt or recommend the lower Tmax value as the preferred Tmax. This process may be repeated using further decreased values of Tmax. In some cases, each decreased value of Tmax may be less than the previous decreased value. Furthermore, if it is determined that there is no improvement in the subject's average glucose level and/or there is an increase in hypoglycemia or hypoglycemia risk events, the system may use the previous Tmax or may select a Tmax between the new Tmax and the previous Tmax. Thus, using process 1300, the system can iteratively modify Tmax to find an optimal value for the subject and/or the selected insulin type.

Furthermore, real-time analysis and optimization of one or more control parameters may result in faster and more accurate improvement in the subject's diabetes maintenance compared to delayed analysis that may occur during clinical testing, which may be less accurate because physiological changes in the subject may not be captured in real time.

In some cases, the real-time process and statistical analysis described above can be used to analyze other types of biomedical data obtained by one or more subject sensors (e.g., measuring one or more physiological parameters). In some such cases, the additional biomedical data, such as data, can be received from a smartwatch (e.g., blood pressure, heart rate), from a weight sensor, or from any other type of biomedical sensor. By adapting process 1300 to perform statistical analysis of the additional biomedical data, a quantitative objective analysis of the biodata can be performed that can be used by a healthcare provider to care for the subject.

Additionally, the results of the comparative analysis described above can be used to provide additional recommendations to the subject. For example, if the actual Tmax of a particular type of insulin is determined to be higher than the subject expected, it may be recommended that the subject modify their diet in a particular manner while using that particular type of insulin.

Exemplary Simulations Embodiments of an automatic glucose level control system 510 that may be adapted for use with embodiments of the present disclosure are described in International Publication No. WO 2010/116524, published August 6, 2015, U.S. Pat. No. 9,833,570, issued December 5, 2015, and U.S. Pat. No. 7,806,854, issued October 5, 2017, the disclosures of each of which are incorporated by reference in their entirety herein for all purposes.

The automatic glucose level control system 510 can administer insulin doses autonomously and account for the on-line accumulation of insulin doses ("insulin on board") due to the finite availability of insulin. The rate of insulin absorption, and therefore the rate of accumulation, of an insulin dose can be modeled by a pharmacokinetic (PK) model (e.g., a biexponential PK model represented by Equation 2 with preset values of time constants α1 and α2). Of clinical importance in relation to the PK model is the time it takes for an insulin dose (e.g., administered subcutaneously) to be absorbed into the subject's blood. In some examples, the peak time of insulin absorption into the blood is referred to as Tmax. In some other examples. In some other examples, Tmax may be the time at which the concentration of insulin reaches its maximum value after delivery of a particular dose of insulin. In some such examples, Tmax may be measured from the time insulin is provided to the subject (e.g., subcutaneously using an infusion set).

In some examples, setting the time constants of the PK model (e.g., _α1 and _α2 in Equation 2) may be equivalent to setting the Tmax inherently assumed by the model, and conversely, setting the Tmax may be setting the time constants of the PK model. The value of the time constants may be used to determine the on-line calculation of insulin accumulation by the control system, and thus may control the insulin dosing response of the control system to a given blood glucose level fluctuation. Thus, varying the Tmax or the time constants associated with Tmax controls the aggressiveness of the insulin dose of the control system.

In certain embodiments, the control system implements a method for adapting the Tmax (and therefore the time constant) setting of the control system's PK model online. This method can be performed by the control system periodically performing online evaluations and calculations and generating recommendations for modifying Tmax, or by the control system autonomously adjusting Tmax online. In either case, the calculation can be based on the execution of the control system over a period of time. In some cases, the online adaptation to Tmax, whether performed autonomously or issued as a recommendation, may be based on the execution of glucose control by the control system over a time interval, including trends in glucose levels, average glucose levels, or the extent and/or duration of occurrences of low glucose levels (hypoglycemia) and/or high glucose levels (hyperglycemia). Alternatively, the calculation can be based on the amount of counter regulator used, other intended usage if the counter regulator was available (e.g., in an insulin-only system or when the counter regulator or its delivery channel is temporarily unavailable). The method can impose upper and/or lower limits (static or dynamic) within which Tmax can vary. The suitability of Tmax for a given situation may vary depending, for example, on the particular insulin being administered by the control system.

In certain embodiments, the described methods may be applicable whether the continuous glucose monitor (which can provide an input glucose signal to the control system) is online or offline. For example, the methods disclosed herein can be applied to the system described in WO 2015/116524. Furthermore, the described methods can coexist with other aspects of the system whether it is activated or not, such as, but not limited to, having a glucose target that is automatically adapted by the system, such as, for example, the system described in WO 2017/027459, published February 16, 2017, which is incorporated herein by reference for all purposes.

）と、Ｕ０の消費された部分の尺度を表すことができる

との差であると見なすことができるためである。血液へのインスリン用量の吸収のピーク時間Ｔｍａｘは、式３によって与えることができる。したがって、Ｔｍａｘを設定することは、所与のグルコースプロファイルに対する制御システムのオンラインインスリン投薬応答の大きさ（例えば、積極的または保守的）を直接支配することができるＰＫモデル時定数を設定することであってもよい。限定されるものではないが、簡単にするために、α_１とα_２が、例えば、α_２＝１．５α_１の関係があると仮定する。 As previously mentioned, the absorption of subcutaneously administered insulin into the blood may be governed by a biexponential PK model of Equation 2. Setting the time constant in the PK model may be a measure of the retention effect of the accumulated amount of insulin in a subcutaneously administered dose, which may represent a measure of the total effect over time due to the total area (U0)

) can be used to represent a measure of the consumed portion of U0.

The peak time of absorption of an insulin dose into the blood, Tmax, can be given by Equation 3. Setting Tmax may therefore be a matter of setting a PK model time constant that can directly govern the magnitude (e.g., aggressive or conservative) of the online insulin dosing response of the control system to a given glucose profile. For simplicity and without limitation, it is assumed that _α1 and _α2 are related, e.g., _α2 = _1.5α1 .

A biexponential PK model can be used to simulate the relationship between the glucose profile and the drug (e.g., insulin or glucagon) dose delivered to a subject. Figures 14A-14C show simulations that demonstrate the effect that increasing or decreasing the Tmax setting or the value of the control parameter corresponding to Tmax can have on the online insulin and glucagon dosing response of the glucose level control system 510 for a given glucose profile (e.g., temporal variation in blood glucose levels over a 24-hour period).

FIG. 14A shows a simulation of a subject's blood glucose control with Tmax set at 65 minutes. Graph 1402 shows the variation in the subject's blood glucose level (BGL) over a 24 hour period. Range 1404 shows a desired target setpoint range for the subject's blood glucose level (e.g., 70-120 mg/dL). Additionally, range 1406 shows a range of the subject's glucose levels associated with hypoglycemia or risk of hypoglycemia (e.g., less than 60 mg/dL). Graph 1410A shows administering medication (insulin or glucagon) to the subject over the same 24 hour period as graph 1402, based at least in part on the blood glucose level variation shown in graph 1402.

14B shows a simulation of blood glucose control for a subject with Tmax set at 15 minutes. Graph 1410B corresponds to graph 1410A, but with Tmax set at 15 minutes instead of 65 minutes. As shown by comparing graph 1410B with 1410A, decreasing Tmax to 15 minutes may increase the insulin dosage required to maintain a given glucose profile 1400.

Figure 14C shows a simulation of blood glucose control for a subject with Tmax set at 130 minutes. Graph 1410C corresponds to graph 1410A, but with Tmax set at 130 minutes instead of 65 minutes. As shown by comparing graph 1410C with 1410A, increasing Tmax to 130 minutes may reduce the insulin dosage required to maintain a given glucose profile 1400.

Even if the subject's glucose profile does not change, increasing or decreasing insulin (or counterregulator) dosage can impact the care of the subject 512. For example, subjects may differ in the degree to which they experience symptoms (e.g., dizziness, nausea, etc.) resulting from the maintenance of their diabetes. Advantageously, autonomous optimization of one or more control parameters of the glucose control system can reduce the drug dosage and/or frequency of administration required to maintain a normal glucose profile.

The simulations shown in Figures 14A-14C show one non-limiting example of the impact of modifying the control parameters of the glucose control system. In some cases, different dosages may subsequently result in different blood glucose fluctuations, which may result in a subsequent change in the determined insulin glucagon dose. Nevertheless, the simulations shown in Figures 14A-14C demonstrate a correlation between Tmax as a control parameter and the drug dose determined by the glucose level control system 510 for each treatment. Furthermore, these simulations demonstrate that the determined treatment dose can be used as feedback to adjust Tmax as described below.

ここで、

はＴｍａｘのベースライン値であり、ｆ（ｙ_ｋ，ｇ_ｋ）は、グルコース信号ｙ_ｋの血糖制御、および／または（送達されているか否かにかかわらず）制御システムによって計算される逆調節投薬量ｇ_ｋに基づいた、パラメータ制御調整関数（本明細書では調整関数と呼ばれる）である。ｆ（ｙ_ｋ，ｇ_ｋ）の評価は、ある時間帯（例えば、１週間、２週間、４週間または他の時間間隔）にわたるものであってもよい。例えば、

である。いくつかの例では、ｋは現在の治療期間を表すことができ、Ｎは、１つまたは複数の治療期間を含むことができる退薬期間を示すことができる。 Example of an Automatic Blood Glucose Control Improvement Process In some embodiments, the value of Tmax can be automatically changed online based on the glycemic control during withdrawal. For example, Tmax can be described using the following formula:

Where:

is the baseline value of Tmax, and f( _yk , _gk ) is a parameter-controlled adjustment function (herein referred to as an adjustment function) based on the glycemic control of the glucose signal _yk and/or the counter-regulatory dosage _gk (whether delivered or not) calculated by the control system. Evaluation of f( _yk , _gk ) may be over a period of time (e.g., 1 week, 2 weeks, 4 weeks, or other time interval). For example,

In some examples, k can represent a current treatment period and N can represent a withdrawal period, which can include one or more treatment periods.

に対して増加させることができ、逆に、退薬期間にわたって、グルコース信号ｙ_ｋの血糖制御において高血糖症が増加する（重篤度および／または持続期間において）場合に、Ｔ_ｍａｘ（ｋ）を

に対して減少させることができる。さらに、ｆ（ｙ_ｋ，ｇ_ｋ）は、退薬期間にわたって、逆調節投薬量ｇ_ｋの増減に対して、Ｔ_ｍａｘ（ｋ）を

に対してそれぞれ増減させることができる。ｆ（ｙ_ｋ，ｇ_ｋ）に対する調整Ｔ_ｍａｘ（ｋ）は、ユーザコマンドに応答して、または被検者の生理学的測定に基づいて、予定された周期的な間隔（例えば、２４時間ごとに１回、３日ごとに１回、週に１回など）であってもよい離散的な時間に評価および実行することができる。代替として、または加えて、いくつかのメトリックが、現在の計算ウィンドウ（例えば、１週間、１ヶ月など）内でしきい値を満たすか、または特定の基準を満たすと、調整をオンラインで評価および実行することができる。この基準は、ｙ_ｋにおける低血糖症が特定のしきい値に達するかもしくはそれを超える場合、またはｇ_ｋにおける逆調節投薬のレベルが特定のしきい値に達するかもしくはそれを超える場合を含むことができる。代替として、または加えて、調整は、現在の計算ウィンドウにおけるグルコース信号ｙ_ｋ（例えば、平均値）に関する何らかの評価が、前の計算ウィンドウ（例えば、前の週）におけるその評価と統計的に有意な差を達成した後に実行することができる。これらの説明された実施態様は、Ｔ_ｍａｘ（ｋ）がいつ調整されるか、および／または調整される大きさに関して、オンラインで数学的に決定される動的インスタンスを有することを可能にする。 The parameter control adjustment function f(y _k ,g _k ) is adapted to adjust T max (k) in the event of an increase (in severity and/or duration) or impending increase in hypoglycemia in the glycemic _control of glucose signal y _k over the withdrawal period (which may include one or more treatment periods).

and conversely, if hyperglycemia increases (in severity and/or duration) in the glycemic control of the glucose signal y _k over the withdrawal period, T _max (k) can be increased by

Furthermore, f(y _k , g _k ) can be used to determine the T _max (k) for increasing or decreasing the counter-regulatory dose g _k over the withdrawal period.

and g k , respectively. The adjustments T _max (k) to f(y _k ,g _k ) can be evaluated and performed at discrete times, which may be scheduled periodic intervals (e.g., once every 24 hours, once every 3 days, once a week, etc.), in response to a user command or based on physiological measurements of the subject. Alternatively or additionally, adjustments can be evaluated and performed online when some metrics meet a threshold or meet a certain criterion within the current calculation window (e.g., one week, one month, etc.). The criterion may include when hypoglycemia in y _k reaches or exceeds a certain threshold or when the level of counter-regulatory dosing in g _k reaches or exceeds a certain threshold. Alternatively or additionally, adjustments can be performed after some evaluation of the glucose signal y _k (e.g., average value) in the current calculation window achieves a statistically significant difference from that evaluation in the previous calculation window (e.g., the previous week). These described implementations make it possible to have dynamic instances of when T _max (k) is adjusted and/or the magnitude by which it is adjusted, determined mathematically on-line.

に基づいて評価されてもよく、ここで、ｙ_ｎは、ｎ番目の治療期間に測定されたグルコース信号であり、ｇ_ｎは、ｎ番目の治療期間に対する逆調節ホルモンの計算された用量であり、Ｎは、１つまたは複数の治療期間を含むことができる退薬期間を示す。いくつかの例では、Ｎは、ｋ番目の治療期間から後退した治療期間の数であってもよい。 In some examples, the treatment periods can be scheduled regular or periodic time intervals (e.g., 24 hour periods, 2 day periods, 1 week periods, etc.) based on user commands or based on physiological measurements of the subject. In some other examples, the treatment periods may be defined as the time interval between two subsequent treatment deliveries, and each treatment period may be identified based on the treatment delivery time that indicates the start of the treatment period. In either case, f( _yk , _gk ) may be the adjustment to _Tmax for the kth treatment period, and f( _yk , _gk ) may be expressed as the formula

where _yn is the glucose signal measured at the nth treatment period, _gn is the calculated dose of counter-regulatory hormone for the nth treatment period, and N indicates a withdrawal period, which may include one or more treatment periods. In some examples, N may be the number of treatment periods withdrawn from the kth treatment period.

15 shows an example of a blood glucose level signal G(t) 1502 (e.g., a CGM trace received from a CGM sensor) over a treatment period (starting at _tS 1504 and ending at _tE 1506) during which one or several doses of insulin and/or a counter regulator (e.g., glucagon) are determined and/or administered by a glucose control system 510. For example, an insulin dose of _Ui 1508 units may be provided at time _tu,i 1510 (where i varies from 1 to the number of insulin deliveries between _tS 1504 and _tE 1506) at a measured glucose level of _Gu,i 1512. Similarly, the control system may calculate a dose of C _j 1514 units that may or may not have been administered, a glucose level G _c,j 1518 at which glucagon may be delivered, and a time t _c,j 1516 at which glucagon may be delivered (where j varies from 1 to the number of glucagon deliveries between t _S 1504 and t _E 1506). The control system may be configured to provide therapy to maintain the BGL within a normal range defined by an upper limit G _max 1520 and a lower limit G _min 1522, and toward a set value G _set 1524. In some examples, a glucose level above G _max 1520 may indicate hyperglycemia, and a glucose level below G _min 1522 may be considered hypoglycemia. For example, during the treatment period shown in FIG. 15, two instances of hyperglycemia 1526 and two instances of hypoglycemia 1528 may be identified by the control system. In some examples, during each treatment period, the control system may store G(t) 1502, t _u,i 1510, t _c,j 1516, U _i 1508, and C _j 1514 for all treatment deliveries (all values of i and j). In some examples, the values of one or more control parameters (e.g., Tmax, G _set ) may not change during the treatment period between t _S 1504 and t _E 1506.

16 presents a flow chart of an exemplary automatic blood glucose refinement process that can use the above-described correction methods to control Tmax and/or other control parameters of a glucose control system. Process 1600 may be performed by any system that can autonomously and/or automatically modify a control algorithm and/or control parameters that affect the execution of the control algorithm based on feedback (e.g., from a blood glucose signal) regarding the treatment administered to subject 512. For example, process 1600 may be performed by one or more elements of glucose level control system 510. In some cases, at least certain operations of process 1600 may be performed by a separate computing system that receives blood glucose data from glucose level control system 510. While one or more different systems can perform one or more operations of process 1600, for ease of explanation and not to limit the present disclosure, process 1600 is described with respect to a particular system.

Process 1600 may be executed automatically and without user interaction. In some cases, a user may trigger process 1600 via a command or interaction with a user interface. However, once process 1600 is triggered, process 1600 may be executed automatically. Additionally, process 1600 may be executed continuously, periodically, or in response to a trigger. The trigger may be time-based and/or based on a measurement of the subject's glucose level. For example, the trigger may correspond to a determination that the subject's glucose level differs by more than a threshold value from a predicted glucose level predicted by the glucose level control algorithm based on administration of a medication. Additionally, the trigger may be based on activation or initial use of glucose level control system 510 by subject 512.

Process 1600 begins at block 1602 where a first value of a control parameter (e.g., a control parameter that may be adaptively modified) of the glucose control system 510 is selected. For example, the control parameter may be a Tmax value used in a control algorithm of the glucose control system 510. In some examples, the Tmax may be related to one or more parameters in a PK model used by the control algorithm. As another example, the control parameter may be a set point (e.g., _Gset 1524 in FIG. 15) or a target value for a measured blood glucose concentration of the subject 512 (e.g., measured using a CGM sensor).

The first value of the control parameter may be selected based on a baseline value. The baseline value may be associated with clinical data, may be determined based on the operation of the glucose level control system 510 at a time period prior to execution of the process 1600, or may be determined from a previous execution of the process 1600. Alternatively or in addition, the baseline value may be selected based on clinical data or a particular prescription of the subject 512. In some cases, the baseline value may be based on clinical data for an average user, or an average user who shares particular physiological data with the subject 512. In some cases, the baseline value is determined based on a healthcare provider's evaluation of the subject 512. Additionally, the baseline value may be determined based on the injection site of the glucose level control system 510 (e.g., back, stomach, leg, etc.). In some cases, the baseline value may be selected based on demographics or characteristics of the subject 512.

In block 1604, the glucose control system 510 provides therapy to the subject 512 over a time period based at least in part on the first value of the control parameter. Additionally, therapy may be provided at least in part based on one or more glucose signals received during the time period. The glucose signal may be received from a glucose sensor (e.g., CGM) and may correspond to the glucose level of the subject. In some cases, the time period may include one or more treatment periods. In some examples, the number of treatment periods included in the time period may be equal or unequal treatment periods. A treatment period may be a time period corresponding to a single delivered drug dose, which may include instantaneous delivery or delivery of a drug dose over a time period. Alternatively, a treatment period may be a time period encompassing multiple drug dose deliveries. Additionally, a treatment period may be a time period associated with a defined length of time. Alternatively, or in addition, a treatment period may be defined based on several drug periods. In other words, a time period may vary based on the amount of time it takes to deliver or administer a specified number of drug doses (of any type or of a particular type).

In some examples, the delivery times and doses of the multiple therapies may be based at least in part on the glucose level signal and a first value of a control parameter of a control algorithm used by the glucose control system 510. The control parameter may include any control parameter that affects the operation of the glucose level control system 510 and/or the execution of the control algorithm of the glucose level control system 510.

For example, the control parameters may be Tmax, T1 _/2 , a delivery rate of a drug dose, a glucose level set point, a blood glucose range, a blood glucose level threshold (e.g., a maximum or minimum value), etc. The control algorithm may include any control algorithm and/or PK model used to determine a dose of a drug (e.g., insulin) to administer to the subject 512. In other words, the controller 518 or processor 530 may use the control algorithm to generate a dose control signal based at least in part on the value of the control parameter (e.g., the first value selected in block 1602) to cause the delivery device 514 to administer a dose of insulin or other drug.

Each treatment of the multiple treatments provided over a period of time may correspond to a single dose of insulin to the subject 512. This single dose of insulin may be any type of insulin that may be administered for any reason. For example, the insulin dose may be a basal insulin dose, a priming dose, a dose delivered in response to a meal notification, or a correction dose of insulin. Additionally, each treatment provided may be a medication other than insulin, such as a counter regulator (e.g., glucagon). In some cases, each treatment delivery may include multiple medication (e.g., insulin and/or counter regulator) doses delivered or administered to the subject 512 over the treatment period. Additionally, the multiple medication doses may include different types of medication doses, such as one or more basal doses, one or more meal doses associated with one or more meal notifications, one or more correction doses, etc.

In some examples, the value of the control parameter to be adjusted may change from one treatment period to another during the time window. For example, the value of the control parameter may change by a given amount at the beginning of each treatment period or group of treatment periods. In some other examples, the value of the control parameter may change by a given amount after a certain number of treatments. In some examples, the amount by which the control parameter is changed may be determined based on one or more withdrawal treatment periods within the time window. In some cases, block 1604 may include one or more of the embodiments described with respect to process 1304.

In some examples, treatment data may be acquired and/or stored during a treatment period or during one or more of the treatment periods included in a time slot. With reference to FIG. 15, in some examples, the treatment data may include the glucose signal G(t) 1524, the calculated or actual delivery time (t _c,j 1516), and the amount of counterregulatory agent estimated or delivered (C _j 1514). This treatment data may be stored in the memory 540 of the glucose level control system 510. Additionally, the treatment data may include a total amount of counterregulatory hormone administered during the treatment period. Alternatively or in addition, other parameters and data associated with each treatment period may be stored in the memory 540, such as a total amount of insulin administered, an amount of insulin delivered (U _i 1508), delivery time of insulin delivered during each treatment period (t _u,i 1510), data received from other sensors that may measure one or more physiological parameters of the subject, data received from the subject or user (e.g., via a user interface), etc.

In block 1606, the glucose level control system 510 determines a control parameter adjustment to the control parameter. The control parameter adjustment may be based at least in part on the treatment data. In some embodiments, the adjustment may be determined using an adjustment function. For example, the adjustment function may be a function f( _yk , _gk ) to modify Tmax according to Equation 4. In some examples, the control parameter adjustment may be determined by analyzing the glycemic control of the subject's blood glucose as indicated by the glucose level signal (e.g., G(t) 1524 or CGM trace). Analyzing the glycemic control of the subject's blood glucose may include tracking the subject's 512 blood glucose levels over time. Additionally, analyzing the subject's glycemic control of the subject's blood glucose may include comparing the subject's 512 blood glucose levels over time to a predicted blood glucose of the subject's 512 over time that is estimated based on the control parameter values used in the PK model and the control algorithm. In some examples, the value of the adjustment function f( _yk , _gk ) may be calculated at least in part using estimated or actual values of _tc,j 1516, _Cj 1514, and Gc _,j (where j varies from 1 to the number of counter-adjustments provided during a time period). In some other examples, the determination of the adjustment function f( _yk , _gk ) may include statistical analysis based on estimated or actual values of _tc,j 1516, _Cj 1514, and Gc _,j (where j varies from 1 to the number of counter-adjustments provided during a time period). In some such examples, the statistical analysis may be based on statistics and/or analytical tools described below.

In some cases, the adjustment of the control parameters may be determined based on the number of hypoglycemia 1528 and/or hyperglycemia 1526 events and/or the duration of each event. In some examples, the adjustment of the control parameters may be determined based on the difference between the measured glucose level and a set value (G _set 1524). In some examples, the adjustment may be determined based on the time interval that the glucose level remains within a target range (e.g., between G _max 1520 and G _min 1522). In some cases, the adjustment may be determined based on the stability of the blood glucose level measured for the subject 512, or the low variability in the blood glucose level of the subject 512. For example, a statistical analysis may be performed to determine the rate of distribution change of G(t) that exceeds one or more threshold rates.

In some cases, the adjustment of the control parameters may be determined, at least in part, by analyzing one or more signals received from one or more subject sensors measuring one or more physiological parameters of the subject (e.g., heart rate, body temperature, etc.). In yet other examples, the adjustment of the control parameters may be determined based on an assessment or input received from the subject 512 (e.g., using a user interface of the AMD). For example, if the subject 512 experiences lightheadedness, dizziness, headache, nausea, or other discomfort during one or more treatment periods, the subject 512 may indicate, e.g., via a touchscreen user interface or other interface of the AMD, how the subject 512 is feeling.

The adjustments may be determined in real time or substantially in real time, taking into account the processing speed of the hardware processor 530, the glucose level control system 510, or the time it takes the subject to provide an assessment of his or her condition to the glucose level control system 510. In some cases, the adjustments to the control parameters may be determined by a computing system in communication with the glucose level control system 510. For example, the glucose level control system 510 may transmit the treatment data to another computing system, such as a local computing system, a smartphone, or a cloud-based computing system. Additionally, the glucose level control system 510 may transmit data related to the treatment data and the control parameter values to the computing system. The computing system may determine adjustments to better manage the blood glucose levels of the subject 512 for the next time period.

In block 1608, the glucose level control system 510 adjusts the control parameters using the control parameter adjustments determined in block 1606. In some examples, the adjustments may be performed autonomously or automatically. In some other examples, the control parameter adjustments determined in block 1606 may be presented to the subject or other user (e.g., a parent, guardian, clinician, etc.) via a user interface (e.g., a touch screen display). In some such cases, the subject or other user may be able to confirm or modify the control parameter adjustments. In other cases, the indication of the control parameter adjustments may be presented for informational purposes and may not be adjustable by the user. In some cases, the control parameters may be adjusted only after receiving user confirmation (e.g., user interaction with a user interface). In some other examples where the adjustments are determined by a computing system, the adjustment values may be presented to the user via a user interface of the glucose control system or a user interface of the computing system. In some cases, the user may adjust the control parameters of the glucose control system using the adjustment values received from or presented by the computer system.

The adjustments in block 1608 may cause a change in the operation or execution of the control algorithm. This change in the execution of the control algorithm may result in a change in one or more factors associated with providing therapy to the subject 512. For example, the change in the execution of the control algorithm may result in a change in the amount of drug delivered, the timing of delivery of the drug, the rate at which a dose of drug is delivered to the subject 512, the target set point or range for the subject's blood glucose, the threshold used in determining whether to deliver the drug (e.g., a threshold difference from a target set point), or any other factor that may affect the therapy delivered to the subject 512.

In some cases, the adjusted value of the control parameter may be output to a user (e.g., the subject or parent). The user may then configure the glucose level control system 510 based on the selected control parameter value. Alternatively, or in addition, the glucose level control system 510 may automatically adjust the value of the control parameter. In some cases, the user may be provided with an opportunity to confirm the adjustment. In other cases, the adjustment may be made automatically without confirmation. However, the adjustment may be presented to the user (e.g., the subject or healthcare provider) and/or recorded in a treatment log.

At block 1610, the glucose level control system 510 provides therapy based at least in part on the updated control parameters updated at block 1608. The new values of the control parameters may be maintained for a second time period. The second time period may refer to a particular amount of time, an amount of time to deliver a particular number of drug doses, or a particular number of drug doses.

The process 1600 may be repeated during subsequent time periods. In some examples, the process may be repeated periodically (every 24 hours, every two days, weekly, or other time intervals). In some cases, the time periods may be provided by the subject or user. Additionally, the process may be repeated in response to a command. In some cases, the process may be repeated in response to determining that the blood glucose level of the subject 512 does not meet one or more criteria for a particular time period. For example, the process may be repeated if a statistically significant difference between the average measured BGL and the target BGL exceeds a threshold value or if the number of detected hypoglycemia and/or hyperglycemia events exceeds a threshold number for a particular time period.

In some examples, the process 1600 can be used to adjust several control parameters that affect therapy delivery by the glucose control system. In some such examples, the process 1600 can be used to adjust a first control parameter during a time period and a second control parameter during a second time period. The second time period may be immediately after the first time period or may be delayed by a particular time. In some implementations, the control system 510 can determine when to adjust the control parameters. In some examples, the delay between the periods of control parameter adjustments may be determined at least in part based on the glycemic control of the glucose signal. In some cases, the delay may be determined based on input received from a user. Additionally, the adjustment of the second control parameter may be determined at least in part based on the adjustment determined for the first control parameter.

In some embodiments, the third control parameter may be adjusted during a third time period. The adjustment of the third control parameter may occur immediately after the adjustment of the second control parameter, or may occur with a delay. The delay may be determined at least in part based on the subject's glycemic control after the second control parameter is adjusted. In some cases, the glucose control system may be configured to sequentially adjust the first and second, or the first, second, and third control parameters, if the subject's glycemic control meets one or more threshold conditions. In some examples, the duration of the time period during which the control parameters are adjusted may be different from the duration of the first and second control parameters.

As discussed above, process 1600 may be used to adjust one or more control parameters that affect the delivery of insulin. However, process 1600 is not so limited and may be used to modify one or more control parameters that affect the delivery of other medications, such as counter regulators (e.g., glucagon). In some cases, process 1600 may be used to recommend changes in insulin and/or counter regulator delivery without modifying the delivery. This may be advantageous for generating counter regulator recommendations in non-bihormonal glucose level control systems 510 that do not support counter regulators or that support the use of counter regulators but where counter regulators are not available.

Implementing Statistical Analysis in Automated Blood Glucose Control Improvement As described above, values of one or more control parameters (e.g., baseline values or optimal clinical values) of the PK model and/or control algorithm used by the glucose control system 510 may be determined by statistical analysis of treatment data sets (e.g., glycemic control information) collected from multiple cohorts of subjects (e.g., 20, 50, 100, 200 subjects) during a clinical study. In some examples, the control parameters (e.g., Tmax) may be measured directly for subjects in each cohort (e.g., based on the results of blood analysis after manual or automated drug administration). These measurements can be used to determine optimal values of the control parameters (e.g., Tmax) used in the glucose control system. In some cases, the blood glucose levels (BGL) of the subjects may be controlled and recorded over a given period (e.g., one week, two weeks, one month, or other period) using the same or nearly the same glucose control system. Subjects in each cohort may use the same values for the control parameters of the glucose control system, while subjects in different cohorts may use different values for the same control parameters. Then, the measured treatment data sets (e.g., including the glycemic control information measured and/or determined for the subjects) over a given period of time may be compared using statistical analysis to evaluate the optimal values of the control parameters. For example, the measured glycemic control of the subjects in the first cohort in response to setting Tmax to a first value may be compared to the measured glycemic control of the subjects in the second cohort in response to setting Tmax to a second value. Such comparisons may include various statistical analyses that may reveal statistically significant differences between the measured glycemic controls. For example, the mean, variance and/or standard deviation of the measured blood glucose level data obtained from the first and second cohorts may be compared to a set of reference values that may be obtained from a third cohort of subjects with normal blood glucose levels (e.g., non-diabetic subjects). To generate accurate results, such clinical studies often require several cohorts, each of which includes a large number of subjects (e.g., large enough to allow statistical analysis), and therefore a large number of identical glucose control systems. For example, in some studies, 10, 20, 50, or 100 subjects and glucose systems may be required. Thus, determining the optimal value of one or more control parameters based on clinical studies may be costly and time-consuming. Furthermore, clinical studies typically cannot capture the unique physiological characteristics and real-time physiological changes of subjects (even in studies involving several large cohorts).

A portable glucose control system that monitors BGL in real time and autonomously or automatically provides medication to a subject can collect and store a treatment data set that can include a sufficient number of data points for statistical analysis, similar to those collected in clinical studies. In some examples, the treatment data can include glycemic control information (e.g., received from a CGM sensor), other physiological effects of the treatment (e.g., obtained from a subject sensor or from the subject), the amount and type of medication delivered, medication delivery time, etc. Advantageously, these treatment data sets can be used to determine optimal values of one or more control parameters of the glucose control system, or values of one or more control parameters of the glucose control system that provide improved diabetes management compared to default values, baseline values, or initial clinically determined values. The optimal or improved values may be determined based on a statistical analysis, including the types of statistical analysis that may be used in clinical studies. In some embodiments, the statistical analysis can include calculating one or more statistics, such as the mean, variance, standard deviation, various statistical distributions (e.g., those described below with respect to FIG. 17). On-board and real-time (or near real-time) evaluation of the value of one or more control parameters of the glucose control system based on treatment data collected during one or more treatment periods can eliminate the need for expensive and time-consuming clinical studies and can improve the maintenance of a subject's diabetes, for example, by taking into account the subject's unique physiological characteristics and real-time physiological changes. Furthermore, on-board evaluation of the control parameter values provides faster and more accurate diabetes assessment and management compared to clinical testing. Some of the embodiments described herein may be used to determine optimal values of one or more control parameters that can be used by a user to adjust the control parameters via a user interface of the glucose control system. In some cases, the glucose control system can autonomously adjust one or more control parameters using the determined optical values.

Therapeutic data collected by the glucose control system may include glycemic control information, information regarding drug delivery time, the dose of drug provided, BGL level at the time of drug delivery (e.g., measured based on glucose signals obtained from a CGM sensor), physiological effect of the drug on the subject (e.g., BGL for a period of time after drug delivery, subject assessment, etc.), and any type of data that may be determined from the treatment provided to the subject. In some embodiments, the glucose control system may collect therapeutic data during one or more therapeutic periods. With reference to FIG. 15 , therapeutic data collected and stored during each therapeutic period (e.g., a period starting at t _S 1504 and ending at t _E 1506) may include, but is not limited to, the CGM trace G(t) 1502, the delivered dose (U i 1508) and delivery time (time t _u,i ), the delivered or determined dose (C _i 1514) and delivery time (t _c,i 1516) of a counter regulator (e.g., glucagon), and the like. The treatment data may be stored in the memory of the glucose control system (e.g., a flash drive, solid state drive, hard disk, or any other type of non-volatile memory) as one or more data sets. Each data set may be associated with one or more categories of treatment data or with a particular treatment period during which the treatment data was collected. In some cases, the value of one or more control parameters may change from one treatment period to another. For example, the value of the control parameter may change by a given amount at the beginning of a treatment period or group of treatment periods. The value of the control parameter may be changed automatically by the glucose level control system 510 or by a user via a user interface. In some cases, the control parameter may be changed by a given amount after a certain number of treatment periods. The amount by which the control parameter is changed may be determined based on treatment data collected during one or more preceding treatment periods. Alternatively, or in addition, the amount by which the control parameter is changed may be provided by a user via a user interface. In some cases, the duration of the one or more treatment periods is selected such that the measured or determined data set is large enough for statistical analysis. In some examples, the uncertainty associated with an optimal or improved value of a control parameter determined using statistical analysis may depend on the size of the data set used for the analysis.

In some embodiments, the process 1300 can use statistical analysis to determine the value (e.g., optimal value) of the control parameter. For example, statistical analysis can be used to determine the treatment effect in block 1306, block 1314, or to compare the treatment effects resulting from different control parameter values in step 1316. In some such examples, in block 1308, a second value of the control parameter may be provided by a user (e.g., subject or guardian) based at least in part on the results of the first effect and statistical analysis performed on the treatment data collected and/or stored during the first treatment period (block 1304). In some examples, in step 1316, a statistical analysis may be performed based at least in part on the first effect and the second effect to obtain a comparative evaluation. The comparative evaluation can be used to determine whether one of the pairs or sets of values of the control parameter results in improved glycemic control of the subject compared to the other values used for the control parameter. In some embodiments, the values of the control parameters determined in block 1316 can be provided to the subject, guardian, or health care provider via a user interface of the glucose control system 510 and/or a computing system (e.g., a smartphone, notebook, personal computer, etc.) connected (e.g., via a wireless link) to the glucose control system. In some such embodiments, the subject, guardian, or health care provider can change the value of the corresponding control parameter to the determined value by interaction with the user interface (e.g., in block 1318) before the next treatment session. Alternatively or additionally, the glucose level control system 510 may automatically change the value of the control parameter to the determined value and proceed to block 1318. In some such cases, the user may be provided with an opportunity to confirm the modification. In other cases, the modification may be made automatically without confirmation. However, the modification may be presented to the user (e.g., the subject or health care provider) and/or recorded in a treatment log.

In some examples, the first and second therapies provided to the subject during the first (block 1304) and second (block 1312) treatment periods may include multiple therapy deliveries. During the first (block 1304) and second (block 1312) treatment periods, the first and second first therapy data may be acquired by the control system 510. In some such cases, the therapy data may include glycemic control information including at least a glucose signal received during the corresponding treatment period. Determining the first effect may include calculating statistical characteristics of the therapy data collected during the multiple therapies provided during each period. For example, the control system 510 may calculate a mean value, a deviation from the mean value, and a variance of the measured BGL. In some cases, the control system 510 may calculate one or more quantities (e.g., statistics) to quantify the mean blood glucose level and its deviation from the baseline level. In some embodiments, the control system 510 can determine one or more quantities (e.g., statistical quantities) to assess glycemic control variations and associated risks (e.g., risk of hypoglycemia or hyperglycemia) or to quantify the mean blood glucose level and its deviation from a baseline (e.g., normal) level. In some cases, the duration of the second period may be equal to the duration of the first period. Alternatively, or in addition, the duration of each period may be selected such that each period includes the same number of treatments provided to the subject. In some embodiments, the duration of each period may be selected such that the number of times a treatment is administered during that time period is sufficiently large to allow for a statistically significant evaluation. In some cases, in block 1316, the comparison of the first effect to the second effect can include a statistical analysis of the statistical data generated based on the data collected during the first and second periods.

In some examples, in addition to the optimal values of one or more control parameters, the control system may generate a control parameter optimization report that may include statistics calculated during the optimization process. Additionally, the report may include a graphical display of the treatment data and associated risk assessment. In some such examples, this report may be used by the subject or a healthcare provider to make decisions related to selecting the determined optimal parameter values. Additionally, the control parameter optimization report may include information that may be used by the subject or a healthcare provider to modify the subject's overall strategy for managing glycemic control. For example, modifying meal times, the content or amount of food consumed by the subject, etc.

17 shows some examples of statistics that may be generated and utilized in blocks 1306 and 1314 of process 1300 using treatment data 1705 during the treatment period and known parameters of the control system 1703. In some embodiments, during the treatment period, values of certain control parameters may be fixed and/or selected based on baseline values (e.g., results of previous clinical studies) or previously determined values (e.g., by different control parameter modification and/or optimization processes). With reference to FIG. 15, in the example shown in FIG. 17, G _min 1722 (lower limit of normal BGL), G _max 1720 (upper limit of normal BGL), and G _set 1724 (target BGL) are assumed to be known values provided by the subject, user, healthcare provider, or determined by a computing system based on a clinical data set. For example, G _min 1722 may be between 65 mg/dL and 75 mg/dL, G _max 1720 may be between 175 mg/dL and 185 mg/dL, and G _set 1724 may be between 70 mg/dL and 180 mg/dL. In some examples, G _set 1724 may be a value (e.g., an optimal value) determined by a previous optimization process (e.g., process 1300). G(t) 1702 (CGM curve or measured glycemic control), U _i 1708, t _u,i 1710, C _i 1514, and t _c,i 1716 may be included in the treatment data collected during the treatment period. In some examples, the treatment data 1705 may be used to generate various types of statistics. For example, the treatment data 1705 can be used to generate probability distributions (e.g., discrete or continuous) and/or frequency distributions (e.g., absolute, relative, or cumulative) of particular measurements or determinations, such as distributions associated with glucose concentration 1726 (e.g., the portion of the treatment period during which the glucose signal was within a selected range), glucose rate of change 1728 (e.g., the portion of the treatment period during which the glucose rate of change signal was within a selected range), insulin dose 1730 (percent of insulin doses delivered within a selected dose range), glucagon dose 1732 (percent of glucagon doses delivered within a selected dose range), hyperglycemia 1734 (percent of hyperglycemic events in which the glucose signal was detected to be above _Gmax by an amount within a selected range), hypoglycemia 1736 (percent of hypoglycemic events in which the glucose signal was detected to be below _Gmin by an amount within a selected range), etc. In some examples, one or more features of these statistical distributions (e.g., mean, variance, deviation from the mean, etc.) or certain combinations of several features of these statistical distributions can be used to determine (e.g., quantify) the effectiveness of the treatment. In some examples, the therapy data considered to generate certain statistical data (e.g., histograms) may be filtered to exclude data points collected during certain events, such as during meal times, during exercise, etc. In some examples, the time bins associated with these events may be specified by the user through a user interface.

In some embodiments, the statistical analysis can include analytical methods and tools that can compare the effects of different control parameter values. Some examples of analytical methods and tools that can be used with one or more of the embodiments described herein are described in the article "Statistical Tools to Analyze Continuous Glucose Monitor Data" (W. Clarke et al., Diabetes Technology and Therapeutics, vol. 11, S45-S54, 2009), which is incorporated herein by reference in its entirety. Examples of methods and tools that can facilitate the extraction of information from the complex and large amount of glycemic control information measured during the treatment period are discussed herein. In some cases, the treatment data used for the statistical analysis includes the subject's glucose trace or G(t). In some examples, G(t) can be a series of time-stamped blood glucose data received from a CGM sensor (see FIG. 17). In some examples, the glucose signal obtained from the CGM can represent blood glucose levels as a discrete time series that approximates G(t) in steps (e.g., readings every 2 minutes, every 5 minutes, every 10 minutes, etc.) that depend on the resolution of the particular device. In some examples, statistical analysis can be performed on the treatment data (e.g., glucose signals received from the CGM sensor) to provide assessments (e.g., comparative assessments) related to (1) mean blood glucose levels and deviations from normal glycemic control (sometimes referred to as euglycemia), (2) variability and risk assessments, and (3) clinical events such as postprandial glucose excursions and hypoglycemic episodes. In some embodiments, the assessments may be made based on two sets of treatment data collected over two time periods. In some such examples, the assessments may be used by the control system 510 to determine whether the subject's glycemic control has improved from a first treatment period to a second treatment period. In some examples, the assessments may be used by a health care provider to assess the subject's glycemic control over one or more time periods.

In some cases, the blood glucose control system may determine three values of average blood glucose: a mean value (e.g., calculated for the entire G(t) measured during the treatment period or portion of the treatment period), a pre-prandial mean value (e.g., calculated for a time window of 60-120 minutes post-prandial), and a post-prandial mean value (e.g., calculated for a time window of 0-60 minutes pre-prandial). Calculation of the pre-prandial and post-prandial mean values and the difference between the mean values may serve as an indication of the overall effectiveness of the pre-prandial bolus timing and bolus amount. In some examples, deviation from target or euglycemia may be assessed by determining the percentage of time spent within, below, or above pre-set target limits (e.g., G _min =70 and G _max =180 mg/dL). In some examples, the percentage of time within each range may be calculated via linear interpolation between successive glucose measurements. In some other examples, the percentage of time within additional ranges may be calculated. In some such examples, the probability of occurrence of extreme hypoglycemia and hyperglycemia may also be assessed. To quantify the fluctuation of blood glucose levels, in some examples, the fluctuation of BGL can be calculated using standard deviation and variance. In some cases, when focusing on the relationship between glucose fluctuation and the risk of hypoglycemia and hyperglycemia, a risk index can be defined that can serve as a measure of the overall glucose fluctuation. In some examples, a separate function can be calculated to divide the overall glucose fluctuation into two independent sections related to the fluctuation to hypoglycemia and hyperglycemia, while at the same time equalizing the amplitude of these fluctuations in terms of the risk they pose. For example, a BGL transition from 180 to 250 mg/dL may appear three times larger than a transition from 70 to 50 mg/dL, but in terms of risk, these fluctuations will appear equal. In some cases, an analysis of the rate of BGL change (e.g., measured in mg/dL/min) can be used to evaluate the dynamics of BGL fluctuation on a time scale of minutes. In other words, this is an evaluation of the "local" properties of the system, as opposed to the "global" properties described above. In some examples, the local characteristic can be evaluated in the vicinity of any point in time by the value BGL, its first derivative, or possibly its second derivative (acceleration).

In some examples, in addition to statistical analysis of treatment data, statistical analysis of user input provided during the first or second treatment periods may be used in determining or comparing treatment effectiveness at blocks 1306, 1314, and 1316 of process 1300. For example, the number and time of day that a subject exhibits a particular symptom may be used to determine treatment effectiveness.

In some cases, in addition to the statistical analysis of treatment data in blocks 1306, 1314, and 1316 of process 1300, statistical analysis of biomedical or physiological data received from one or more subject sensors (e.g., a smart watch, a weight sensor, etc.) may be used in determining or comparing treatment effectiveness. For example, from a subject's temperature, blood pressure, heart rate), weight sensor, or any other type of biomedical sensor.

In some examples, the process 1300 can be modified to determine an optimal value of Tmax, or a value of Tmax that provides improved maintenance of the subject's diabetes, by decreasing Tmax (increasing aggressiveness of treatment) after each treatment period in a series of treatment periods until statistical evaluation indicates that further reductions in Tmax do not improve the average glucose level without increasing the probability of hypoglycemia. Improved maintenance of the subject's diabetes may include maintaining the average glucose level closer to a set glucose level range or reducing the variability of the average glucose level over time compared to a previous control value (e.g., Tmax) setting. It should be understood that other metrics can be used to measure improvement in the maintenance of the subject's diabetes, such as reducing hypoglycemic risk events or reducing insulin administration without increasing the effects or corresponding risk of diabetes.

18 presents a flow chart of an exemplary automatic control parameter refinement process, according to certain embodiments. Process 1800 may be performed by any system capable of autonomously and/or automatically modifying a control algorithm and/or control parameters affecting the execution of the control algorithm based on feedback (e.g., from a blood glucose signal) regarding a treatment administered to subject 512. For example, process 1800 may be performed by one or more elements of glucose level control system 510. In some cases, at least certain operations of process 1800 may be performed by a separate computing system that receives blood glucose data from glucose level control system 510. While one or more different systems may perform one or more operations of process 1800, for ease of explanation and not to limit the present disclosure, process 1800 is described with respect to a particular system.

Process 1800 may be executed automatically and without user interaction. In some cases, a user may trigger process 1800 via a command or interaction with a user interface. However, once process 1800 is triggered, process 1800 may be executed automatically. Additionally, process 1800 may be executed continuously, periodically, or in response to a trigger. The trigger may be time-based and/or based on a measurement of the subject's glucose level. For example, the trigger may correspond to a determination that the subject's glucose level differs by more than a threshold value from a predicted glucose level predicted by the glucose level control algorithm based on administration of a medication. Additionally, the trigger may be based on activation or initial use of glucose level control system 510 by subject 512.

In some embodiments, the glucose level control system 510 can perform process 1800 to adjust one or more control parameters of the glucose control system 510 to improve glycemic control of the subject. The control parameters can include any control parameters that affect the operation of the glucose level control system 510 and/or the execution of a control algorithm of the glucose level control system 510. In some such embodiments, in addition to improving glycemic control of the subject, the process 1800 can take into account the subject's risk of hypoglycemia. In some embodiments, the process 1800 can include one or more of the embodiments described above with respect to process 1300.

Process 1800 begins at block 1802 where an initial value for a control parameter of the glucose control system (e.g., Tmax or other control parameter of the glucose control system selected to be optimized) is selected. The control parameter may be a control parameter of a pharmacokinetic (PK) model used by the control algorithm PK of the glucose control system 510. In some examples, the control parameter may be the time for insulin to reach a particular concentration level in the subject's plasma after administration of an insulin dose. In some cases, the initial value of the control parameter may be based on a treatment delivered during a time period prior to the first treatment period, a clinical value, or the subject's weight.

In some examples, the initial value of the control parameter may be selected using one or more of the embodiments described with respect to block 1304 of process 1300. In some embodiments, the control parameter may be a control parameter used by a control algorithm of a glucose control system to account for insulin accumulation in the subject. In some embodiments, the control parameter may be used to control the insulin dosing response of the control algorithm to blood glucose fluctuations of the subject based on a glucose level signal received from a glucose level sensor (e.g., an SGM sensor).

In block 1804, the control system 510 can provide a therapy during a first therapy period based at least in part on the glucose level signal and the initial value of the control parameter. In some embodiments, block 1804 can include one or more of the embodiments described above with respect to block 1304 of process 1300. In some embodiments, the first therapy data can include glycemic control information resulting from the delivery of the first therapy. In some examples, the system can store all or a portion of the therapy data generated during the first therapy period in a memory of the control system 510. In some examples, the therapy provided in block 1804 can include multiple drug deliveries.

In block 1806, the control system 510 can use statistical analysis of the first treatment data collected and stored in block 1804 to determine the therapeutic effect of the treatment provided during the first treatment period. In some examples, the statistical analysis can include calculating statistics as described above with reference to FIG. 17. In some cases, the statistical analysis can include a regression analysis between certain measured and/or calculated parameters in block 1804. In some such examples, the regression analysis can include determining an autoregressive model. In some examples, the control system 510 can determine the therapeutic effect using one or more of the embodiments described with respect to block 1306 of process 1300.

In block 1808, the control system 510 may modify the value of the control parameter compared to the initial value selected in block 1802 or the value used in the last treatment period. In some examples, the modified value may be a value that makes the treatment more aggressive (e.g., aggressive by a significant amount). For example, if the control parameter is Tmax, in block 1808, the value of Tmax may be reduced by a lesser amount (e.g., 5 minutes, 10 minutes, 15 minutes, or more) than the value used in the previous treatment period (e.g., the initial value or the last modified value). In some examples, the modified value of the control parameter may be received from a user interface of the blood glucose control system in response to a user interaction with the user interface. The previous treatment period may be the first treatment period or any previous treatment period. In some examples, the value of Tmax may be lowered by a significant amount (e.g., 10 minutes, 15 minutes, or other value). Furthermore, the amount by which Tmax is reduced may be less than the previous reduction during a previous iteration of the process 1800. In some embodiments, the control parameters may be modified automatically without action by a user. In some cases, modifying the control parameters may change the timing, dose size, or rate of injection of insulin administered to the subject.

In block 1810, the control system 510 provides a therapy to the subject based at least in part on the glucose signal and the modified control parameter received from block 1808. In some examples, the duration of the therapy period (in block 1810) may be equal to the duration of one or more previous therapy periods. In some other examples, the duration of the therapy period may be determined based on a determined therapeutic effect of the therapy delivered during one or more previous therapy periods. In some examples, in block 1810, the system may store all or a portion of the therapy data generated during the therapy period. In some examples, the therapy provided in block 1810 may include multiple drug deliveries. In some cases, the therapy data may include glycemic control information resulting from the delivery of the therapy.

In block 1812, the control system 510 determines a treatment effect of the treatment provided in block 1810 during the last treatment session. In some examples, the treatment effect may be determined based at least in part on the treatment data obtained and stored in block 1810. In some examples, the control system 510 may determine the treatment effect using one or more of the embodiments described with respect to block 1306 of process 1300.

In block 1814, the control system 510 performs a statistical analysis based at least in part on the determined therapeutic effects of the treatments provided and stored during the last treatment period and the treatment period prior to the last treatment period to obtain a comparative evaluation. In some such examples, the comparative evaluation can be based on the determined effects and a statistical analysis of the treatment data collected during the corresponding treatment periods. In some examples, the statistical analysis can include using the treatment data to generate a statistic (e.g., a distribution as shown in FIG. 17). In some examples, the statistical analysis can include the analytical methods described above. In some such examples, one or more characteristics of the statistical data can be used to compare the therapeutic effects. In some examples, the statistical analysis can include calculating one or more of a mean, median, mode, standard deviation, percentage, ratio, or probability based on the treatment data obtained during the last two treatment periods or the determined effects of the treatments provided during the last two periods.

At decision block 1816, the control system 510 may determine, based at least in part on the comparative evaluation received from block 1814, whether the values of the control parameters used during the last treatment period improved the glycemic control of the subject as compared to the treatment period prior to the last treatment period. In some embodiments, the control system 510 may determine whether the modified values of the control parameters resulted in a statistically significant improvement in glycemic control. In some embodiments, the control system 510 may determine whether the modified values of the control parameters resulted in an improvement in a physiological parameter of the subject. In these embodiments, the physiological parameter may be determined based at least in part on the glucose level signal received from the glucose level sensor.

If the control system 510 determines in decision block 1816 that the subject's glycemic control has not improved, the control system 510 may return to block 1810 and continue to provide treatment to the subject based on the last revised value of the control parameter without further modification.

If, at decision block 1816, the control system 510 determines that the value of the control parameter used during the last treatment period improved the subject's glycemic control compared to the treatment period prior to the last treatment period, the control system 510 proceeds to decision block 1818. In some cases, the improvement in glycemic control must be greater than a threshold level before the system 510 proceeds to block 1818. In some cases, the control system proceeds to block 1818 if the revised value of the control parameter results in a reduced occurrence of blood glucose excursions compared to the first value of the control parameter.

At decision block 1818, the control system 510 may determine whether the frequency and/or severity of hypoglycemic events increased during the last treatment period compared to the treatment period prior to the last treatment period. In some examples, if the control system 510 determines that the frequency and/or severity of hypoglycemic events increased during the last treatment period (e.g., above a threshold number or amount), the control system 510 may return to block 1810 and continue to provide treatment to the subject based on the last modified value of the control parameter without further modification. If, at decision block 1818, the control system determines that the change in the frequency and/or severity of hypoglycemic events is negligible (e.g., below a threshold number or amount), the control system may proceed to block 1808, where the control system 510 modifies the value of the control parameter. In some examples, the modified value may be a value that results in a more aggressive treatment (e.g., the value of Tmax may be reduced). In some such examples, the amount by which the control parameter is changed may be less than the reduction in one or more previous modifications.

In some examples, at block 1818, the control system may determine the risk, or frequency and severity, of one or more events other than hypoglycemia. For example, the control system may determine that the rate and magnitude of glucose concentration has increased beyond a threshold despite improvement in the subject's glycemic control. In some such examples, these additional risk determinations may be used to determine whether to maintain or modify the last value of the control parameter.

In some embodiments, a modified version of process 1800 may be used by the glucose control system, where the process stops at block 1816 and the control system continues to provide therapy based on the last modified value of the control parameter until it receives user input. In some such examples, the last value of the control parameter (modified at block 1808), the results of the comparative evaluation generated based on the comparison performed at block 1814 (e.g., whether a statistically significant improvement in the subject's glycemic control resulted from the change in the last control parameter), may be output to the subject, guardian, or health care provider via a user interface of the glucose control system 510 and/or a computing system (e.g., a smartphone, notebook, personal computer, etc.) connected (e.g., via a wireless link) to the glucose control system. In some such embodiments, based at least in part on the results of the comparative evaluation, the subject, guardian, or health care provider may change the value of the corresponding control parameter (e.g., interact with a user interface) prior to the next treatment session.

In some examples, the statistical analysis used to determine treatment efficacy (e.g., in blocks 1306 and 1312 of process 1300 and blocks 1806 and 1812 of process 1800) or to compare treatment efficacy (e.g., in blocks 1316 of process 1300 and 1814 of process 1800) may include regression analysis. In some examples, regression analysis can be used to find relationships between parameters calculated and/or measured during a treatment period. For example, with reference to FIG. 17, regression analysis can be used to find a relationship between _Ui and the rate of glucose concentration change (e.g., using G(t) around _ti ) for multiple treatments provided during a treatment period. In some cases, the results of one or more regression analyses can be used in an optimization process to determine values for the control parameters.

In some examples, the treatment data captured and stored during one or more treatment periods may be divided into equal time intervals, with each time interval beginning and ending at substantially the same specific start and end times within the 24-hour period. In some such examples, an autoregressive model may be derived for glycemic control over the time interval between the specific start and end times. The resulting autoregressive model may then be used to determine whether glycemic control has improved compared to a previous treatment period. In some cases, the resulting autoregressive model may be used to make additional adjustments to one or more control parameters in subsequent treatment periods (treatment periods after the period for which the autoregressive model was determined).

In some examples, the results of statistical analysis of the treatment data can be used to evaluate the precise glucose signal generated by the CGM sensor.

As mentioned above, in some examples, the glucose control system can generate a control parameter optimization report that can include some or all of the statistics calculated during the optimization process, the results of the statistical analysis of the treatment data and associated risk assessments, as well as graphical displays. In some such examples, a control variability grid analysis (CVGA) may be included in the control parameter optimization report to visualize the variability of the CGM data at a group level in terms of glucose control. In some examples, the graphs may include unique graph families, for example, to visualize the average blood glucose and deviation from target values, to visualize variability and risk assessments, and to visualize event-based clinical characteristics. In some other examples, the graph data can represent average blood glucose, and deviation from target glucose traces, as well as aggregate glucose traces representing time spent below, within, or above a pre-set target range, and visualize crossings of blood glucose thresholds. In still other examples, the control parameter optimization report can include graphs representing variability and risk assessment data. For example, risk traces may be presented to highlight essential variance (e.g., by equalizing the size of glucose deviations toward hypoglycemia and hyperglycemia, highlighting large glucose fluctuations, and constraining fluctuations to within the target range). In some other examples, a histogram of blood glucose rate of change may be included in the report, for example to present the spread and range of glucose transients. In yet other examples, a Poincare' plot may be included in the report to visualize the stability of the glucose signal during different treatment periods, which may also be associated with different values of the control parameter.

Exemplary embodiments The following is a list of multiple sets of exemplary numbered embodiments. The features described in the following list of exemplary embodiments can be combined with additional features disclosed herein. Furthermore, each set of exemplary numbered embodiments in the following list can be combined with one or more additional sets of exemplary numbered embodiments from the following list. Furthermore, additional inventive combinations of features are disclosed herein that are not specifically described in the following list of exemplary embodiments and do not include the same features as the embodiments listed below. For the sake of brevity, the following list of exemplary embodiments does not identify all inventive aspects of the present disclosure. The following list of exemplary embodiments is not intended to identify key features or essential features of any subject matter described herein.
1. A computer-implemented method for generating an indication of total carbohydrate therapy over a period of time using a drug pump configured to deliver at least insulin therapy to a subject, comprising:
a hardware processor configured to generate a dose control signal for the drug pump configured to deliver at least an insulin therapy to the subject;
receiving a glucose level for the subject;
determining, based at least in part on the glucose level, that a triggering event has occurred that elevates the subject's blood glucose level, the triggering event indicating that the subject is at imminent risk of hypoglycemia or that an episode of hypoglycemia is present in the subject;
determining an amount of a counter regulator to respond to said imminent risk of hypoglycemia or said episode of hypoglycemia;
determining a dosage of carbohydrate therapy based at least in part on said amount of said counter-regulatory agent;
tracking the determined dose of carbohydrate therapy over a time period that includes a plurality of hypoglycemic risk events or hypoglycemic episodes to generate said index of total carbohydrate therapy over said time period;
outputting said index of total carbohydrate regimen;
4. A computer-implemented method comprising:
2. The computer-implemented method of embodiment 1, further comprising providing to said subject said amount of said counter-regulator in response to said imminent risk of hypoglycemia or said onset of hypoglycemia.
3. The computer-implemented method of embodiment 1, further comprising providing to the subject the amount of the counter-regulator in response to the glucose level meeting or falling below a threshold glucose level.
4. The computer-implemented method of embodiment 3, wherein the threshold glucose level is set based on the subject's risk tolerance for a hypoglycemic event.
5. The computer-implemented method of embodiment 1, wherein said indicator of total carbohydrate therapy corresponds to a reduction in carbohydrates ingested by said subject.
6. The computer-implemented method of embodiment 1, wherein said index of total carbohydrate therapy corresponds to a carbohydrate reduction achievable by availability of said counter-regulatory agent.
7. The computer-implemented method of embodiment 1, wherein said index of total carbohydrate therapy corresponds to an amount of counter-regulatory agent provided to said subject as a carbohydrate replacement.
8. The computer-implemented method of embodiment 1, wherein said index of total carbohydrate therapy comprises an index of carbohydrate range.
9. The step of determining the dose of carbohydrate therapy comprises:
accessing a mapping between said counterregulators and carbohydrates;
determining the dose of carbohydrate therapy based at least in part on the mapping and the amount of the counter-regulator;
2. The computer-implemented method of embodiment 1, comprising:
10. The computer-implemented method of embodiment 9, wherein the mapping is based at least in part on the type of carbohydrate.
11. The computer-implemented method of embodiment 9, wherein the mapping is generated based on a clinical comparison of the counterregulator and the carbohydrate.
12. The computer-implemented method of embodiment 9, wherein the mapping is based at least in part on a physiological characteristic of the subject.
13. The computer-implemented method of embodiment 9, wherein the mapping is based at least in part on the type of counterregulator.
14. The computer-implemented method of embodiment 9, wherein the mapping comprises a formula relating the counterregulator to the carbohydrate.
15. The computer-implemented method of embodiment 9, wherein the mapping comprises a first mapping if the drug pump comprises a bi-hormonal pump configured to deliver a counterregulator therapy to the subject, and wherein the mapping comprises a second mapping if the drug pump is not configured to deliver the counterregulator therapy to the subject.
16. The computer-implemented method of embodiment 1, wherein the indicator of total carbohydrate therapy comprises one or more of an indicator of calories, an indicator of carbohydrates, an indicator of a sugar measure, an indicator of an amount of food, or an indicator of the subject's weight attributable to the carbohydrate therapy.
17. The computer-implemented method of embodiment 1, wherein the period corresponds to a particular time period, a number of events in the plurality of hypoglycemic risk events, or a number of occurrences in the plurality of hypoglycemic occurrences.
18. An automated blood glucose control system configured to generate an indication of total carbohydrate therapy over a period of time in said subject, comprising:
a drug delivery interface configured to operably connect to a drug pump configured to infuse a drug into the subject, the drug comprising at least insulin; and
A memory configured to store specific computer-executable instructions;
A computer-executable device that communicates with the memory and executes the specific computer-executable instructions to at least:
receiving a glucose level for the subject;
determining, based at least in part on the glucose level, that a triggering event has occurred that elevates the subject's blood glucose level, the triggering event indicating that the subject is at imminent risk of hypoglycemia or that an episode of hypoglycemia is present in the subject;
determining an amount of counterregulator to respond to said imminent risk of hypoglycemia or said occurrence of hypoglycemia;
determining a carbohydrate regimen based at least in part on said amount of said counter-regulator;
tracking the determined dose of carbohydrate therapy over a time period that includes a plurality of hypoglycemic risk events or hypoglycemic episodes to generate said index of total carbohydrate therapy over said time period;
outputting said index of total carbohydrate regimen;
a hardware processor configured to
1. An automated blood glucose control system comprising:
19. The automated blood glucose control system of embodiment 18, wherein the hardware processor is further configured to operate a control algorithm for automatic generation of a counter regulator dosing signal configured to operate the drug pump to control the subject's blood glucose level based at least in part on a glucose level signal received from a glucose level sensor operably connected to the subject indicating that the glucose level does not meet a threshold corresponding to the trigger event.
20. The memory is further configured to store a mapping between the counterregulator and the carbohydrate, and the hardware processor is further configured to:
Accessing the mapping from the memory;
and determining the dose of carbohydrate therapy based at least in part on the mapping and the amount of the counter-regulator.
20. An automated blood glucose control system according to embodiment 18.
21. The automated blood glucose control system according to embodiment 20, wherein said mapping comprises an algorithm that relates said counter regulators to said carbohydrates.
22. The automated blood glucose control system of embodiment 18, wherein the period corresponds to a particular time period, a number of events in the plurality of hypoglycemic risk events, or a number of occurrences in the plurality of hypoglycemic occurrences.

Further embodiments of the present disclosure can be described in view of the following numbered embodiments.
1. A computer-implemented method for modifying a therapy provided to a subject using a blood glucose control system, comprising:
a hardware processor configured to generate a dose control signal for the blood glucose control system;
receiving a glucose level signal from a glucose level sensor operably connected to the subject;
delivering a first therapy by the blood glucose control system to the subject during a first treatment period, the first therapy being delivered based at least in part on a first value of a control parameter used by a control algorithm to generate the dose control signal, the control parameter being used by the control algorithm to take into account insulin accumulation in the subject, thereby controlling the insulin dosing response of the control algorithm to blood glucose fluctuations of the subject as indicated by the glucose level signal;
determining a first effect corresponding at least in part to the first treatment, the first effect comprising analyzing glycemic control of blood glucose of the subject as indicated by the glucose level signal;
autonomously generating a second value of the control parameter, the autonomously generated second value being determined as a function based on the first value and the first effect;
changing the control parameter from the first value to the second value;
delivering a second therapy by the blood glucose control system to the subject during a second therapy period, the second therapy being delivered based at least in part on the second value of the control parameter, and altering the control parameter modifies the therapy provided to the subject;
4. A computer-implemented method comprising:
2. The hardware processor:
determining a second effect corresponding at least in part to the second treatment;
selecting one of the first value of the control parameter or the second value of the control parameter as an active control parameter value based at least in part on a comparison of the first effect and the second effect;
configuring the blood glucose control system to provide a therapy to the subject during a third treatment period based at least in part on the active control parameter value, wherein the selection of the active control parameter value modifies the therapy provided to the subject;
2. The computer-implemented method of embodiment 1, further comprising:
3. The computer-implemented method of embodiment 1, wherein the control parameter used by the control algorithm is related to at least one time constant used in calculating insulin accumulation in the subject by the control algorithm.
4. The computer-implemented method of embodiment 1, wherein the control parameter used by the control algorithm corresponds to a rate of insulin reduction in the subject.
5. The computer-implemented method of embodiment 1, wherein the first treatment time period comprises a time period corresponding to the administration of a plurality of treatment instances, and the first treatment comprises the plurality of treatment instances.
6. The computer-implemented method of embodiment 1, wherein modifying the control parameter to the second value modifies one or more of a timing, a dose size, or an administration rate of insulin administered during the second treatment period.
7. The computer-implemented method of embodiment 1, wherein the first value of the control parameter is based at least in part on one or more of a treatment delivered during a period of time prior to the first treatment period, a clinical value, or a body weight of the subject.
8. The computer-implemented method of embodiment 1, wherein the control parameter used by the control algorithm corresponds to the time for insulin in the subject's plasma to reach a particular concentration level following administration of an insulin dose.
9. A computer-implemented method for modifying a therapy provided to a subject using a blood glucose control system, comprising:
a hardware processor configured to generate a dose control signal for the blood glucose control system;
delivering a first therapy by the blood glucose control system to the subject during a first treatment period, the first therapy being delivered based at least in part on a first value of a control parameter used by a control algorithm to generate the dose control signal;
determining a first effect corresponding at least in part to the first treatment, the first effect including receiving a glucose level signal from a glucose level sensor operably connected to the subject;
autonomously generating a second value of the control parameter based at least in part on a baseline value of the control parameter and an output of a function defined based on glycemic control of the subject, wherein the glucose level signal comprises an indication of the glycemic control of the subject during the first treatment period;
changing the control parameter from the first value to the second value;
delivering a second therapy to the subject by the blood glucose control system during a second treatment period, the second therapy being delivered based at least in part on the second value of the control parameter, and altering the control parameter modifies the therapy provided to the subject;
4. A computer-implemented method comprising:
10. The hardware processor:
determining a second effect corresponding at least in part to the second treatment;
selecting one of the first value of the control parameter or the second value of the control parameter as an active control parameter value based at least in part on a comparison of the first effect and the second effect;
configuring the blood glucose control system to provide a therapy to the subject during a third treatment period based at least in part on the active control parameter value, wherein the selection of the active control parameter value modifies the therapy provided to the subject;
10. The computer-implemented method of embodiment 9, further comprising:
11. The computer-implemented method of embodiment 9, wherein the first treatment comprises a plurality of treatment instances administered over the first treatment period.
12. The computer-implemented method of embodiment 9, wherein the control parameter used by the control algorithm corresponds to the time for insulin in the subject's blood to reach a particular concentration level resulting from administration of an insulin dose.
13. A computer-implemented method for modifying a therapy provided to a subject using a blood glucose control system, comprising:
a hardware processor configured to generate a dose control signal for the blood glucose control system;
delivering a first therapy by the blood glucose control system to the subject during a first treatment period, the first therapy being delivered based at least in part on a first value of a control parameter used by a control algorithm to generate the dose control signal;
determining a first effect corresponding at least in part to the first treatment, the first effect including receiving a glucose level signal from a glucose level sensor operably connected to the subject;
autonomously generating a second value of the control parameter, the autonomously generated second value being determined as a function based at least in part on a baseline value;
changing the control parameter from the first value to the second value;
delivering a second therapy to the subject by the blood glucose control system during a second treatment period, the second therapy being delivered based at least in part on the second value of the control parameter, and altering the control parameter modifies the therapy provided to the subject;
determining a second effect corresponding at least in part to the second treatment;
performing a comparison of the first effect and the second effect autonomously without human action;
selecting one of the first value of the control parameter or the second value of the control parameter as an active control parameter value based at least in part on the comparison between the first effect and the second effect;
configuring the blood glucose control system to provide a therapy to the subject during a third treatment period based at least in part on the active control parameter value, wherein the selection of the active control parameter value modifies the therapy provided to the subject;
4. A computer-implemented method comprising:
14. The computer-implemented method of embodiment 13, wherein the second value of the control parameter is based at least in part on glycemic control indicated by the glucose level signal.
15. The computer-implemented method of embodiment 13, wherein the baseline value comprises the first value of the control parameter.
16. The computer-implemented method of embodiment 13, wherein the first value of the control parameter is determined based at least in part on the baseline value.
17. The computer-implemented method of embodiment 13, further comprising performing the comparison of the first effect and the second effect, wherein the comparison is performed substantially in real-time in response to determining the second effect.
18. The computer-implemented method of embodiment 13, further comprising performing the comparison between the first effect and the second effect, wherein the comparison between the first effect and the second effect comprises performing a statistical comparison between the first effect and the second effect.
19. The computer-implemented method of embodiment 13, further comprising performing the comparison between the first effect and the second effect, wherein the comparison between the first effect and the second effect comprises performing a regression analysis between at least the first effect and the second effect.
20. The computer-implemented method of embodiment 13, wherein the second value of the control parameter can be selected based on performing a regression analysis between an absorption time of subcutaneously administered insulin into the subject's blood and a glycemic control function, the glycemic control function being based at least in part on the glucose level signal.
21. An automated blood glucose control system configured to autonomously modify control parameters used by a control algorithm to generate a dose control signal that causes therapy to be provided to a subject, comprising:
a drug delivery interface configured to operably connect to a drug pump for infusing a drug into the subject;
a memory configured to store specific computer executable instructions and treatment data;
A computer-executable device that communicates with the memory and executes the specific computer-executable instructions to at least:
receiving a glucose level signal from a glucose level sensor operably connected to the subject;
delivering a first therapy to the subject during a first therapy period, the first therapy being delivered based at least in part on a first value of the control parameter used by the control algorithm to generate the dose control signal, the control parameter being used by the control algorithm to take into account insulin accumulation in the subject, thereby controlling the insulin dosing response of the control algorithm to blood glucose fluctuations in the subject as indicated by the glucose level signal;
determining a first effect corresponding at least in part to the first treatment, wherein determining the first effect comprises analyzing glycemic control of blood glucose of the subject as indicated by the glucose level signal;
autonomously generating a second value of the control parameter, the autonomously generated second value being determined as a function based on the first value and the first effect;
changing the control parameter from the first value to the second value;
delivering a second treatment to the subject during a second treatment period, the second treatment being delivered based at least in part on the second value of the control parameter, and altering the control parameter modifies the treatment provided to the subject.
a hardware processor configured to
1. An automated blood glucose control system comprising:
22. The hardware processor executes the specific computer-executable instructions to perform at least
determining a second effect corresponding at least in part to the second treatment;
selecting one of the first value of the control parameter or the second value of the control parameter as an active control parameter value based at least in part on a comparison of the first effect and the second effect;
providing a treatment to the subject during a third treatment time period based at least in part on the active control parameter value, the selection of the active control parameter value modifying the treatment provided to the subject.
[0023]
22. An automated blood glucose control system according to embodiment 21.
23. The automated blood glucose control system of embodiment 21, wherein the control parameter used by the control algorithm relates to at least one time constant used in the calculation of insulin accumulation in the subject by the control algorithm.
24. The automated blood glucose control system of embodiment 21, wherein the control parameter used by the control algorithm corresponds to a rate of insulin depletion in the subject.
25. The automated blood glucose control system of embodiment 21, wherein the first therapy period includes a time period corresponding to administration of a plurality of therapy instances, and the first therapy includes the plurality of therapy instances.
26. The automated blood glucose control system of embodiment 21, wherein modifying the control parameter to the second value modifies one or more of a timing, a dose size, or a rate of administration of insulin administered during the second treatment period.
27. The automated blood glucose control system of embodiment 21, wherein the first value of the control parameter is based at least in part on one or more of a therapy delivered during a period of time preceding the first treatment period, a clinical value, or a body weight of the subject.
28. The automated blood glucose control system of embodiment 21, wherein the control parameter used by the control algorithm corresponds to the time for insulin in the subject's plasma to reach a particular concentration level following administration of an insulin dose.
29. The automated blood glucose control system of embodiment 21, wherein the control parameter corresponds to Tmax or T1 _/2 .

Further embodiments of the present disclosure can be described in view of the following numbered embodiments.
1. A computer-implemented method for modifying a therapy provided to a subject using a blood glucose control system, comprising:
a hardware processor configured to generate a dose control signal for the blood glucose control system;
receiving a glucose level signal from a glucose level sensor operably connected to the subject;
causing the blood glucose control system to deliver a first therapy to the subject during a first treatment period, the first therapy being delivered based at least in part on a first value of a control parameter used by a control algorithm to generate the dose control signal, the control parameter being used by the control algorithm to take into account insulin accumulation in the subject, thereby controlling the insulin dosing response of the control algorithm to blood glucose fluctuations of the subject as indicated by the glucose level signal;
acquiring first therapy data including glycemic control information resulting from the delivery of the first therapy;
determining a first effect corresponding at least in part to the first treatment over a first time period, the first effect being determined based at least in part on the first treatment data;
setting the control parameter to a second value different from the first value;
delivering a second therapy to the subject by the blood glucose control system during a second treatment period, the second therapy being delivered based at least in part on the second value of the control parameter, and altering the control parameter modifies the therapy provided to the subject;
acquiring second therapy data including glycemic control information resulting from the delivery of the second therapy;
determining a second effect corresponding at least in part to the second treatment over a second time period, the second effect being determined based at least in part on the second treatment data; and
performing a statistical analysis based at least in part on the first effect and the second effect to obtain a comparative evaluation;
determining, based at least in part on the comparative evaluation, whether the second value of the control parameter results in improved glycemic control for the subject;
4. A computer-implemented method comprising:
2. The computer-implemented method of embodiment 1, wherein the control parameter is set to the first value or the second value based at least in part on a user interaction with a user interface of the blood glucose control system.
3. The computer-implemented method of embodiment 1, wherein the control parameter is automatically set to the first value or the second value without action by a user.
4. The computer-implemented method of embodiment 1, wherein the second value of the control parameter is selected autonomously.
5. The computer-implemented method of embodiment 1, further comprising the step of selecting the second value of the control parameter to cause the blood glucose control system to deliver a third therapy to the subject during a third treatment time period in response to determining that the second value results in the improvement in glycemic control for the subject.
6. The computer-implemented method of embodiment 1, wherein determining whether the second value of the control parameter results in the improvement in glycemic control for the subject comprises determining whether the second value of the control parameter results in a statistically significant improvement in glycemic control for the subject.
7. The computer-implemented method of embodiment 6, wherein the statistically significant improvement comprises a threshold level of improvement in glycemic control for the subject.
8. The computer-implemented method of embodiment 1, wherein determining whether the second value of the control parameter results in improved glycemic control for the subject comprises determining whether the second value of the control parameter results in an improvement in a physiological parameter for the subject.
9. The computer-implemented method of embodiment 8, wherein the physiological parameter is determined based at least in part on the glucose level signal.
10. The computer-implemented method of embodiment 1, wherein determining whether the second value of the control parameter results in the improvement in glycemic control of the subject comprises determining whether the second value of the control parameter results in a reduced occurrence of blood glucose excursion compared to the first value of the control parameter.
11. The computer-implemented method of embodiment 1, wherein determining whether the second value of the control parameter results in the improvement in glycemic control for the subject comprises determining whether the second value of the control parameter results in a reduced risk of occurrence of a hypoglycemic event compared to the first value of the control parameter.
12. The computer-implemented method of embodiment 1, wherein performing the statistical analysis comprises determining one or more of a mean, median, mode, standard deviation, percentage, ratio, or probability based on the first treatment data or the second treatment data.
13. The computer-implemented method of embodiment 1, wherein performing the statistical analysis comprises determining one or more of a mean, median, mode, standard deviation, percentage, ratio, or probability based at least in part on the first effect or the second effect.
14. The computer-implemented method of embodiment 1, wherein the control parameter used by the control algorithm corresponds to the time for insulin in the subject's plasma to reach a particular concentration level following administration of an insulin dose.
15. The computer-implemented method of embodiment 1, wherein delivering the first treatment during the first treatment time period comprises administering a plurality of treatment instances, at least one of the plurality of treatment instances being administered at a different time than at least one other treatment instance during the first treatment time period.
16. The computer-implemented method of embodiment 1, wherein the first treatment period and the second treatment period are of the same duration.
17. The computer-implemented method of embodiment 1, wherein a first plurality of instances of treatment is administered during said first treatment period and a second plurality of instances of treatment is administered during said second treatment period.
18. The computer-implemented method of embodiment 1, wherein setting the control parameter to the second value effects a modification to one or more of a timing, dose size, or rate of infusion of insulin administered during the second treatment period.
19. The computer-implemented method of embodiment 1, wherein the first value of the control parameter is based on one or more of a treatment delivered during a period of time prior to the first treatment period, a clinical value, or a body weight of the subject.
20. The computer-implemented method of embodiment 1, wherein the control algorithm is based at least in part on a pharmacokinetic (PK) model.
21. The computer-implemented method of embodiment 20, wherein the control parameters comprise parameters of the pharmacokinetic (PK) model.
22. The computer-implemented method of embodiment 1, wherein performing the statistical analysis comprises one or more of performing a regression analysis or generating an autoregressive model.
23. An automated blood glucose control system configured to autonomously modify control parameters used by a control algorithm to generate a dose control signal that causes therapy to be provided to a subject, comprising:
a drug delivery interface configured to operably connect to a drug pump for infusing a drug into the subject;
a memory configured to store specific computer executable instructions and treatment data;
A computer-executable device that communicates with the memory and executes the specific computer-executable instructions to at least:
receiving a glucose level signal from a glucose level sensor operably connected to the subject;
delivering a first therapy to the subject during a first therapy period, the first therapy being delivered based at least in part on a first value of a control parameter used by a control algorithm to generate the dose control signal, the control parameter being used by the control algorithm to take into account insulin accumulation in the subject, thereby controlling the insulin dosing response of the control algorithm to blood glucose fluctuations in the subject as indicated by the glucose level signal;
acquiring first therapy data including glycemic control information resulting from the delivery of the first therapy;
determining a first effect corresponding at least in part to the first treatment over a first time period, the first effect being determined based at least in part on the first treatment data;
setting the control parameter to a second value different from the first value;
delivering a second therapy to the subject by the blood glucose control system during a second therapy period, the second therapy being delivered based at least in part on the second value of the control parameter, and altering the control parameter modifies the therapy provided to the subject;
acquiring second therapy data including glycemic control information resulting from the delivery of the second therapy;
determining a second effect corresponding at least in part to the second treatment over a second time period, the second effect being determined based at least in part on the second treatment data;
performing a statistical analysis based at least in part on the first effect and the second effect to obtain a comparative evaluation;
determining whether the second value of the control parameter results in improved glycemic control for the subject based at least in part on the comparative evaluation.
a hardware processor configured to
1. An automated blood glucose control system comprising:
24. The automated blood glucose control system of embodiment 23, wherein in response to determining that the second value results in the improvement in glycemic control of the subject, the hardware processor is further configured to execute the specific computer-executable instructions to at least select the second value of the control parameter and cause the blood glucose control system to deliver a third therapy to the subject during a third therapy time period.
25. The automated blood glucose control system of embodiment 23, wherein determining whether the second value of the control parameter results in improved glycemic control for the subject comprises determining whether the second value of the control parameter results in a threshold level of improvement in at least one physiological parameter of the subject.
26. The automated blood glucose control system of embodiment 23, wherein determining whether the second value of the control parameter results in the improvement in glycemic control of the subject comprises determining whether the second value of the control parameter results in a reduced occurrence of blood glucose excursions or a reduced risk of an occurrence of a hypoglycemic event compared to the first value of the control parameter.
27. The automated blood glucose control system of embodiment 23, wherein the control parameter used by the control algorithm corresponds to the time for insulin in the subject's plasma to reach a particular concentration level following administration of an insulin dose.
28. The automated blood glucose control system of embodiment 23, wherein the first therapeutic period length is selected to encompass at least a particular number of therapeutic instances, and the second therapeutic period length is selected to encompass at least the particular number of therapeutic instances.
29. The automated blood glucose control system of embodiment 23, wherein performing the statistical analysis includes one or more of performing a regression analysis or generating an autoregressive model.
30. The automated blood glucose control system of embodiment 23, wherein the control algorithm is based at least in part on a pharmacokinetic (PK) model, and the control parameters include parameters of the pharmacokinetic (PK) model.

Further embodiments of the present disclosure can be described in view of the following numbered embodiments.
1. An automated blood glucose control system configured to generate a back-up therapy protocol including an insulin therapy index derived from autonomously determined insulin doses, comprising:
a drug delivery interface configured to operably connect to a drug pump for infusing a drug into the subject;
A memory configured to store specific computer-executable instructions;
A computer-executable device that communicates with the memory and executes the specific computer-executable instructions to at least:
receiving a glucose level signal from a sensor operatively configured to determine a glucose level of the subject;
generating a dose control signal using a control algorithm configured to autonomously determine a dose of insulin to be infused to the subject to control blood glucose of the subject based at least in part on the glucose level signal;
tracking an insulin therapy administered to the subject by the automated blood glucose control system over a tracking period comprising at least one day, wherein tracking the insulin therapy includes storing an indication of the autonomously determined dose of insulin delivered to the subject as basal insulin, as a correction bolus of insulin, or as a mealtime bolus of insulin;
generating at least one of a back-up infusion therapy protocol or a back-up pump therapy protocol including an insulin therapy indicator based at least in part on the insulin therapy administered to the subject over the follow-up period;
outputting the at least one of the backup infusion therapy protocol or the backup pump therapy protocol on a display when the automatic blood glucose control system is not providing therapy to the subject to enable maintenance of therapy at a rate determined by the automatic blood glucose control system.
a hardware processor configured to
1. An automated blood glucose control system comprising:
2. The automated blood glucose control system of embodiment 1, wherein the hardware processor is further configured to execute the specific computer-executable instructions to at least store in the memory the indication of the autonomously determined dose of insulin delivered to the subject.
3. The hardware processor executes the specific computer-executable instructions to establish a communication channel with at least an external computing system separate from the automated blood glucose control system;
transmitting the indication of the autonomously determined dose of insulin delivered to the subject to the external computing system.
2. The automated blood glucose control system of embodiment 1, further configured as follows:
4. The automatic blood glucose control system of embodiment 3, wherein the external computing system is a computing system of a data center, and the hardware processor is further configured to execute the specific computer-executable instructions to control at least a radio capable of communicating with the external computing system over a wide area network.
5. The automated blood glucose control system of embodiment 1, wherein the hardware processor is further configured to generate the back-up infusion therapy protocol by at least determining a number of long-acting insulin units based at least in part on an average total basal insulin provided to the subject per day over the follow-up period.
6. The automated blood glucose control system of embodiment 1, wherein each day of the tracking period is divided into a plurality of sub-periods, and wherein the hardware processor is further configured to generate the back-up pump therapy protocol by at least determining an hourly basal rate for each sub-period of the plurality of sub-periods.
7. The automated blood glucose control system of embodiment 1, wherein the hardware processor is further configured to generate the backup infusion therapy protocol or the backup pump therapy protocol by at least determining an average correction bolus provided to the subject per day over the tracking period.
8. The automated blood glucose control system of embodiment 1, wherein the hardware processor is further configured to generate the backup infusion therapy protocol or the backup pump therapy protocol by at least determining an average correction bolus provided to the subject over the tracking period.
9. The automated blood glucose control system of embodiment 1, wherein the hardware processor is further configured to generate the backup infusion therapy protocol or the backup pump therapy protocol by at least determining an average change in blood glucose attributable at least in part to units of insulin provided as a correction bolus to the subject during the tracking period.
10. The automated blood glucose control system of embodiment 1, wherein the hardware processor is further configured to generate the backup infusion therapy protocol or the backup pump therapy protocol by at least determining, for each mealtime of a plurality of mealtimes per day, an average mealtime bolus of insulin provided to the subject over the tracking period.
11. The hardware processor executes the specific computer-executable instructions to at least:
tracking a counter regulator therapy administered to the subject over the tracking period, wherein tracking the counter regulator therapy includes storing an indication of an autonomously determined dose of counter regulator delivered to the subject in response to the glucose level signal;
including in at least one of the backup infusion therapy protocol or the backup pump therapy protocol an indication of total and/or daily counterregulator provided to the subject over the follow-up period;
2. The automated blood glucose control system of embodiment 1, further configured as follows:
12. The control algorithm is further configured to autonomously determine a dose of insulin to be infused to the subject for controlling the subject's blood glucose based at least in part on the glucose level signal and a control parameter modifiable by user interaction with a control parameter selection interface element, and the hardware processor executes the specific computer-executable instructions to at least:
tracking user modifications to the control parameters over the tracking period, wherein tracking the user modifications includes storing in a treatment log whether each of the user modifications includes an increase or decrease of the control parameter from a stored control parameter value and a time when each of the user modifications was made;
generating a report of user modifications to the control parameter, the report including a measure of frequency of increases and decreases from the stored control parameter value, the report being included in at least one of the backup infusion therapy protocol or the backup pump therapy protocol;
[0023]
2. The automated blood glucose control system of embodiment 1.
13. A computer-implemented method for generating a back-up therapy protocol including an insulin therapy index derived from an autonomously determined dose of insulin determined by an automated blood glucose control system, comprising:
a hardware processor of the automated blood glucose control system,
receiving a glucose level signal from a sensor operatively configured to determine a glucose level of the subject;
generating a dose control signal using a control algorithm configured to autonomously determine a dose of insulin to be infused to the subject to control blood glucose of the subject based at least in part on the glucose level signal;
tracking an insulin therapy administered to the subject by the automated blood glucose control system over a tracking period comprising at least one day, the tracking of the insulin therapy comprising storing an indication of the autonomously determined dose of insulin delivered to the subject;
generating at least one of a backup infusion therapy protocol or a backup pump therapy protocol including an insulin therapy indicator based at least in part on the insulin therapy administered to the subject over the follow-up period;
outputting the at least one of the backup infusion therapy protocol or the backup pump therapy protocol on a display when the automatic blood glucose control system is not providing therapy to the subject to enable maintenance of therapy at a rate determined by the automatic blood glucose control system;
4. A computer-implemented method comprising:
14. The computer-implemented method of embodiment 13, wherein the autonomously determined dose of insulin comprises one or more of a basal insulin dose, a correction bolus of insulin, or a mealtime bolus of insulin.
15. Establishing a communication channel with an external computing system separate from the automated blood glucose control system;
transmitting to the external computing system the indication of the autonomously determined dose of insulin infused to the subject;
14. The computer-implemented method of embodiment 13, further comprising:
16. The step of generating at least one of a backup infusion therapy protocol or a backup pump therapy protocol comprises:
determining a number of long-acting insulin units based at least in part on the average total basal insulin provided to the subject per day over the follow-up period;
dividing the tracking period into a plurality of sub-periods and determining an hourly basal rate for each sub-period of the plurality of sub-periods;
determining an average correction bolus provided to the subject over the follow-up period;
determining an average change in blood glucose attributable at least in part to units of insulin provided as a correction bolus to said subject during said follow-up period; or determining, for each mealtime of a plurality of mealtimes per day, an average mealtime bolus of insulin provided to said subject over said follow-up period.
14. The computer-implemented method of embodiment 13, comprising one or more of:
17. The computer-implemented method of embodiment 13, further comprising tracking a counter regulator therapy administered to the subject over the follow-up period, wherein tracking the counter regulator therapy comprises storing an indication of an autonomously determined dose of counter regulator delivered to the subject in response to the glucose level signal, and generating the at least one of a backup infusion therapy protocol or a backup pump therapy protocol comprises including in the at least one of the backup infusion therapy protocol or the backup pump therapy protocol an indication of total and/or daily counter regulator provided to the subject over the follow-up period.
18. An automated blood glucose control system configured to generate a report of therapy protocol modifications made by a user of the automated blood glucose control system, comprising:
a drug delivery interface configured to operably connect to a drug pump for infusing a drug into a subject;
a memory configured to store certain computer executable instructions, stored control parameter values, and treatment logs;
A computer-executable device that communicates with the memory and executes the specific computer-executable instructions to at least:
receiving a glucose level signal from a sensor operatively configured to determine a glucose level of the subject;
generating a dose control signal using a control algorithm configured to autonomously determine a dose of insulin to be infused to the subject to control blood glucose of the subject based at least in part on the glucose level signal and a control parameter modifiable by user interaction with a control parameter selection interface element;
tracking user modifications to the control parameter over a tracking period comprising at least one day, wherein tracking the user modifications comprises storing in the treatment log whether each of the user modifications comprises an increase or decrease of the control parameter from the stored control parameter value and the time each of the user modifications was made;
generating a report of user modifications to said control parameters, said report including a measure of frequency of increases and decreases from said stored control parameter values;
a hardware processor configured to
1. An automated blood glucose control system comprising:
19. The automated blood glucose control system of embodiment 18, wherein said report further comprises a percentage of user modifications above or below said stored control parameter values over said tracking period.
20. The automated blood glucose control system of embodiment 18, wherein said report further comprises the number of times insulin infusion is paused over said tracking period.
21. The automated blood glucose control system of embodiment 18, wherein the report further includes the percentage of time that the stored control parameters are not changed by the user over the tracking period.
22. The automated blood glucose control system of embodiment 18, wherein the tracking period is divided into a plurality of sub-periods, and wherein the hardware processor is further configured to track user modifications to the control parameter for each sub-period of the tracking period, and wherein the report includes a measure of the frequency of increases and decreases from the stored control parameter value for each sub-period of the tracking period.
23. The automated blood glucose control system of embodiment 22, wherein at least a first subperiod of the plurality of subperiods is associated with a first value of the control parameter and at least a first subperiod of the plurality of subperiods is associated with a second value of the control parameter, and wherein the hardware processor is further configured to track user modifications to the first value of the control parameter during the first subperiod and to track user modifications to the second value of the control parameter during the second subperiod.
24. The automated blood glucose control system of embodiment 18, wherein the hardware processor is further configured to track user activity associated with the user modifications to the control parameter, and wherein the report of user modifications to the control parameter includes identification of user activities occurring during user modifications to the control parameter.
25. A computer-implemented method for generating a report of therapy protocol modifications made by a user of an automated blood glucose control system configured to infuse a drug into a subject, comprising:
a hardware processor of the automated blood glucose control system,
receiving a glucose level signal from a sensor operatively configured to determine a glucose level of the subject;
generating a dose control signal using a control algorithm configured to autonomously determine a dose of insulin to be infused to the subject to control blood glucose of the subject based at least in part on the glucose level signal and a control parameter modifiable by user interaction with a control parameter selection interface element;
tracking user modifications to the control parameter over a tracking period comprising at least one day, wherein tracking the user modifications comprises storing in a treatment log whether each of the user modifications comprises an increase or decrease in the value of the control parameter from a stored control parameter value and the time each of the user modifications was made; generating a report of the user modifications to the control parameter, the report including a measure of the frequency of increases and decreases from the stored control parameter value;
3. A computer-implemented method.
26. The computer-implemented method of embodiment 25, wherein the report further comprises the percentage of user modifications above or below the stored control parameter value over the tracking period.
27. The computer-implemented method of embodiment 25, wherein the report further includes the number of times insulin infusion was paused over the tracking period.
28. The computer-implemented method of embodiment 25, wherein the tracking period is divided into a plurality of sub-periods, further comprising tracking user modifications to the control parameter for each sub-period of the tracking period, and wherein the report includes a measure of the frequency of increases and decreases from the stored control parameter value for each sub-period of the tracking period.
29. The computer-implemented method of embodiment 28, wherein at least a first subperiod of the plurality of subperiods is associated with a first value of the control parameter and at least a first subperiod of the plurality of subperiods is associated with a second value of the control parameter, further comprising tracking user modifications to the first value of the control parameter for the first subperiod, and tracking user modifications to the second value of the control parameter for the second subperiod.
30. The computer-implemented method of embodiment 25, further comprising tracking user activity associated with the user modifications to the control parameters, wherein the report of user modifications to the control parameters includes an identification of user activities occurring during user modifications to the control parameters.

Terminology It is to be understood that not necessarily all objectives or advantages may be achieved in accordance with any particular embodiment described herein. Thus, for example, those skilled in the art will recognize that a particular embodiment may be configured to operate in a manner that achieves or optimizes one advantage or advantages taught herein without necessarily achieving other objectives or advantages that may be taught or suggested herein.

All of the processes described herein may be embodied in and fully automated through software code modules executed by a computing system including one or more computers or processors. The code modules may be stored on any type of non-transitory computer-readable medium or other computer storage device. Some or all of the methods may be embodied in dedicated computer hardware. Additionally, the computing system may include, be implemented as part of, or be in communication with, an automated blood glucose system, a portable medication system, or a portable medical device.

Many other variations beyond those described herein will be apparent from the present disclosure. For example, depending on the embodiment, certain acts, events, or functions of any of the algorithms described herein may be performed in a different order, added, merged, or omitted entirely (e.g., not all of the acts or events described may be necessary to the implementation of an algorithm). Furthermore, in some embodiments, acts or events may be performed simultaneously rather than sequentially, e.g., via multi-threaded processing, interrupt processing, or multiple processors or processor cores, or on other parallel architectures. Furthermore, different tasks or processes may be performed by different machines and/or computing systems that can function together.

The various example logic blocks and modules described with respect to the embodiments disclosed herein may be implemented or executed by a machine, such as a processing unit or processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. The processor may be a microprocessor, but alternatively the processor may be a controller, a microcontroller, or a state machine, combinations thereof, and the like. The processor may include electrical circuitry configured to process computer-executable instructions. In another embodiment, the processor includes an FPGA or other programmable device that performs logical operations without processing computer-executable instructions. The processor may also be implemented as a combination of computing devices, such as a combination of a DSP and a microprocessor, multiple microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration. Although described herein primarily with respect to digital technology, the processor may also include primarily analog components. The computing environment can include any type of computer system, including, but not limited to, a microprocessor-based computer system, a mainframe computer, a digital signal processor, a portable computing device, a device controller, or a computational engine within an appliance, to name a few examples.

In particular, conditional language such as "can,""could,""might," or "may" is understood within the context in which it is commonly used to convey that certain embodiments include certain features, elements, and/or steps, but other embodiments do not, unless otherwise specified. Thus, such conditional language generally conveys that the features, elements, and/or steps are somehow required by one or more embodiments, or that one or more embodiments necessarily require, with or without user input or prompting,
It is not intended to imply any logic for determining whether these features, elements, and/or steps are included or performed in any particular embodiment.

Disjunctive language, such as the phrase "at least one of X, Y, or Z," is otherwise understood in the context in which it is generally used to indicate that an item, term, etc. can be either X, Y, or Z, or any combination thereof (e.g., X, Y, and/or Z), unless specifically stated otherwise. Thus, such disjunctive language is generally not intended to, and should not, imply that a particular embodiment requires that at least one of X, at least one of Y, or at least one of Z, respectively, be present.

Any process illustrations, elements or blocks in the flow diagrams described herein and/or illustrated in the accompanying drawings should be understood as potentially representing modules, segments or portions of code that include one or more executable instructions for implementing a particular logical function or element in the process. Alternative implementations are within the scope of the embodiments described herein in which elements or functions may be omitted or may be performed in a different order than that illustrated or discussed, including substantially simultaneously or in reverse order, depending on the functionality involved, as will be appreciated by those skilled in the art.

Unless otherwise specified, articles such as "a" or "an" should generally be construed to include one or more of the listed items. Thus, phrases such as "a device configured to" are intended to include one or more of the listed devices. Such one or more listed devices may also be collectively configured to execute the listed descriptions. For example, "a processor configured to execute descriptions A, B, and C" may include a first processor configured to execute description A working in conjunction with a second processor configured to execute descriptions B and C.

It should be emphasized that many variations and modifications can be made to the above-described embodiments, and elements thereof should be understood to be within the scope of other acceptable examples. All such modifications and variations are intended to be included herein within the scope of the present disclosure.

Claims (1)

1. A computer-implemented method for modifying a therapy provided to a subject using a blood glucose control system, comprising:
a hardware processor configured to generate a dose control signal for the blood glucose control system;
receiving a glucose level signal from a glucose level sensor operably connected to the subject;
causing the blood glucose control system to deliver a first therapy to the subject during a first treatment period, the first therapy being delivered based at least in part on a first value of a control parameter used by a control algorithm to generate the dose control signal, the control parameter being used by the control algorithm to take into account insulin accumulation in the subject, thereby controlling the insulin dosing response of the control algorithm to blood glucose fluctuations of the subject as indicated by the glucose level signal;
acquiring first therapy data including glycemic control information resulting from the delivery of the first therapy;
determining a first effect corresponding at least in part to the first treatment over a first time period, the first effect being determined based at least in part on the first treatment data;
setting the control parameter to a second value different from the first value;
delivering a second therapy to the subject by the blood glucose control system during a second treatment period, the second therapy being delivered based at least in part on the second value of the control parameter, and altering the control parameter modifies the therapy provided to the subject;
acquiring second therapy data including glycemic control information resulting from the delivery of the second therapy;
determining a second effect corresponding at least in part to the second treatment over a second time period, the second effect being determined based at least in part on the second treatment data; and
performing a statistical analysis based at least in part on the first effect and the second effect to obtain a comparative evaluation;
determining, based at least in part on the comparative evaluation, whether the second value of the control parameter results in improved glycemic control for the subject;
4. A computer-implemented method comprising:

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