JP6960618B2 - Clinical evaluation device, clinical evaluation method and clinical evaluation program - Google Patents

Description

The present invention relates to the configuration of a clinical evaluation device, a clinical evaluation method, and a clinical evaluation program, and more specifically, to a technique for obtaining a clinical evaluation index for a patient with a myogenic disease.

Diseases that are caused by the muscle itself and cause the muscle to atrophy or weaken are called myopathy. A typical disease is muscular dystrophy. On the other hand, even if the anterior horn cells of the spinal cord and peripheral nerves are affected, the muscles atrophy and weaken. Diseases caused by nerves are called neurogenic diseases (neuropathy).

Among the above-mentioned myogenic diseases, "muscular dystrophy" is a hereditary disease in which muscle atrophy and weakness progress, and is a general term for diseases in which muscle properties are changed or broken due to gene mutation. , Is one of the nationally designated intractable diseases.

As muscular dystrophy progresses and muscle strength weakens, motor function in each part of the body becomes impaired. This can interfere with daily life and cause complications with other illnesses.

It has been elucidated that muscular dystrophy is caused by heredity or gene mutation. Many causative genes have been discovered and are called causative genes. The genetic mutation that causes muscular dystrophy may be inherited from parent to child, or it may be caused by the mutation even if the parent does not have muscular dystrophy.

As for the cause of muscular dystrophy, it was clarified in 1987 that the cause of Duchenne muscular dystrophy was due to a mutation in the dystrophin gene. The causative genes have been elucidated one after another, such as Duchenne muscular dystrophy and Fukuyama congenital muscular dystrophy in 1998, and the cause of their illness is now an academically elucidated problem for most muscular dystrophy patients.

In recent years, new treatments such as exon skipping and stop codon read-through (stop codon skipping) have been developed for Duchenne muscular dystrophy (DMD) that develops in childhood, and their safety and efficacy have been verified. Clinical trials are currently underway.

In clinical trials of muscular dystrophy, evaluation of motor function by a physiotherapist is important as a means (outcome measure) to show the result of the target treatment.

For example, at present, walking for 6 minutes is often used. The 6-minute walking test has been adopted as the main evaluation method of motor function corresponding to walking ability and endurance. For example, it has been adopted as a major outcome item in clinical trials of the effects of exon skipping treatment as described above.

On the other hand, in recent years, accelerometers have become smaller, and as a method for measuring human movements and behaviors, accelerometers that can be worn on the human body may be used. That is, such acceleration measurement is used in various fields such as medical treatment, sports, and behavioral analysis.

For example, conventionally, research has been actively conducted on measuring human body movements and biological signals and using them as an interface between a person and a machine.

Alternatively, there is a report on a behavior recognition device that recognizes a subject's movement or behavior using an acceleration sensor. For example, Patent Document 1 describes a measuring means for observing a state change associated with a subject's movement or behavior, a feature amount extracting means for extracting a feature amount in the observation result, and a movement or behavior to be recognized by the recognition device. A storage means for storing the feature amount of the above, and a recognition means for recognizing the movement or behavior of the subject from the feature amount extracted from the observation result and the stored feature amount are disclosed. The state changes observed by the measuring means include acceleration, angular acceleration, velocity, position, rotation, and bending angle, as well as pulse, blood pressure, body temperature, blood glucose level, respiration, myoelectricity, electrocardiogram, which are biological information of the subject. Blood flow and brain waves are mentioned. The recognized movements are basic movements such as crouching, running, and walking, climbing a ladder, going down stairs, operating a control panel at a high place, operating a valve on the floor, suddenly falling down, and falling from a high place. , Still, leisurely walking, powerful walking, very quiet running, standard walking, healthy walking, healthy running, patient collapsed, suffering, and other complicated movements are listed.

Further, Patent Document 2 describes a sensor unit including an acceleration sensor that detects a pedestrian's motion rhythm as a system for assisting walking of an elderly person or a person with a walking disability, and a measured value of the motion rhythm detected by the sensor unit. A recording unit that records the above, a target setting unit that sets a target value related to the pedestrian's motion rhythm, a timing generation unit that generates a timing signal based on the difference between the measured value and the target value, and a timing generation unit. A gait evaluation system including a stimulus generating unit that generates a rhythm stimulus that can be recognized by a pedestrian based on a timing signal is disclosed.

Further, Patent Document 3 describes a conversion unit that is installed in the human body and acquires an output from a sensor that measures acceleration or pressure applied to the human body as a measuring device that acquires information for accurately evaluating a decrease in walking function. A measuring device provided with a control means for measuring the contact time of both feet, which is the time required from the heel contact of one foot to the takeoff of the toe of the other foot during walking, based on the sensor output is provided on the back of the user's waist. Install in the center. As a result, a technique is disclosed in which the measuring device acquires the contact time of both feet, which is information for accurately evaluating the deterioration of the walking function.

Further, Patent Document 4 is mounted on the midline of the waist of the person to be measured as a walking posture meter capable of presenting the temporal transition of the walking posture in continuous walking in daily life in a more understandable manner for the user. An evaluation amount that quantitatively expresses the walking posture of the person to be measured based on the output of the acceleration sensor and the acceleration sensor for each predetermined unit period within a predetermined continuous walking period of 10 minutes or less. A walking posture meter including an evaluation unit that is repeatedly obtained and a display processing unit that displays the repeatedly obtained evaluation amounts on a display screen in chronological order is disclosed.

Furthermore, since motor function evaluation using acceleration / angular velocity parameters is excellent for quantifying movement and monitoring physical activity, it has been used in recent years to evaluate physical activity of DMD patients due to the miniaturization and weight reduction of measurement devices. It has begun (see Non-Patent Document 1).

On the other hand, as such an acceleration sensor, a small system that can detect not only acceleration but also angular velocity has been developed by applying MEMS (Micro Electro Mechanical Systems) technology (for example, Patent Document 5, Patent Document 5, See Patent Document 6).

Japanese Unexamined Patent Publication No. 10-113343

Japanese Unexamined Patent Publication No. 2010-264320

Japanese Unexamined Patent Publication No. 2012-179114

Japanese Unexamined Patent Publication No. 2014-217649

Japanese Unexamined Patent Publication No. 2010-185835

Japanese Unexamined Patent Publication No. 2011-133244

Eur J Paediatr Neurol., 15: 40-7, 2011., PLoS One, 11 (6): e0156696, 2016

Despite the above-mentioned circumstances, the "6-minute walking test" has a problem that the therapeutic effect cannot always be determined sensitively. Further, in order to use it as an objective clinical evaluation index, there is a problem that the physical and psychological state of the patient has a great influence. Furthermore, when viewed as a clinical evaluation index, there is a problem that it cannot always be said that the state of activities of daily living is evaluated.

In other words, in the area of muscular dystrophy, there is little experience in clinical trials, so it cannot be said that the selection of evaluation items and evaluation methods have been established.

Improvement of targeted biological markers in any clinical trial is considered to be the minimum requirement. However, following this, if muscle mass increases, muscle strength increases, ADL (Activities of Daily Living) improves, and QOL (Quality of life) does not improve as a final result, really beneficial results will be obtained. It is not a cure to bring.

In actual clinical trials and clinical trials, a method for appropriately evaluating each of muscle mass, muscle strength, ADL, and QOL will be required.

In particular, Duchenne muscular dystrophy is the most common and most severe hereditary muscular disease, and many DMD patients are characterized by weakness and present with progressive gait disturbance. Therefore, along with the development of new treatments, functional evaluation for sensitively determining the therapeutic effect is required.

Therefore, as a clinical evaluation index for patients with myogenic diseases, an evaluation method applicable to highly quantitative evaluation of motor function and monitoring of physical activity is required.

However, at present, motor function evaluation based on acceleration / angular velocity parameters as described above reflects motor dysfunction and abnormal movements, but it is unclear how to indicate changes in pathological conditions related to skeletal muscle injury. There are many points.

The present invention has been made to solve the above-mentioned problems, and an object of the present invention is a clinical evaluation capable of highly quantitative evaluation of motor function as a clinical evaluation index for patients with myogenic diseases. To provide equipment, clinical evaluation methods and clinical evaluation programs.

According to one aspect of the present invention, it is a clinical evaluation device for clinically evaluating a predetermined disease of a test subject, and is different from an acceleration sensor attached to the body part of the test subject and a healthy group. Data obtained by time-series analysis of the acceleration measured in advance for each stage of the disease by the acceleration sensor attached to the body of the target group for the target group including the disease group and the predetermined value in the target group. of a storage device for storing in association with an index indicating the degree of injury of skeletal muscle, based on the correlation between the index indicating the degree of the obtained data and injury from stored in the storage device acceleration, the subject It is provided with a clinical index estimation means for estimating an index of a test subject from acceleration data measured by an acceleration sensor.

Preferably, the target of the time series analysis is the absolute value of acceleration.

Preferably, a plurality of accelerometers are attached to the body portion of the test subject, and the clinical index estimation means estimates the index based on the data obtained from the plurality of accelerations acquired from the plurality of acceleration sensors. ..

Preferably, further comprising an angular velocity sensor attached to the trunk body portion of the subject, the storage device, to the control group were measured every beforehand stage by the angular velocity sensor mounted on the body portion of the subject group velocity The angular velocity statistic obtained by time-series analysis is stored in association with the index indicating the degree of damage to a predetermined skeletal muscle in the target group, and the clinical index estimation means is the data obtained from the acceleration and the angular velocity. Estimate indicators based on statistics.

Preferably, the disease is a myogenic disease.

Preferably, the test subject is a human.

Preferably, the test subject is a dog.

Preferably, an index indicating the degree of injury of a given skeletal muscle is the average signal-to-noise ratio of the skeletal muscle sites in T2-weighted images by nuclear magnetic resonance imaging.

According to another aspect of the present invention, a clinical evaluation device for assisting in the evaluation of the effect of a drug or drug candidate in the clinical evaluation of a predetermined disease for a test subject in which the drug or drug candidate is prescribed in a predetermined volume and usage. As an operating method, the clinical evaluation device is attached to the body part of the subject group with respect to the acceleration sensor attached to the body part of the test subject and the target group including the healthy group and the disease group, unlike the test subject. A storage device that stores data obtained by time-series analysis of acceleration measured in advance for each stage of the disease by the attached acceleration sensor and an index indicating the degree of damage to a predetermined skeletal muscle in the target group. And, based on the correlation between the data obtained from the acceleration stored in the storage device and the index indicating the degree of injury, the index of the test subject is estimated from the acceleration data measured by the acceleration sensor for the test subject. The index estimation means is provided, and the clinical index estimation means includes a step of outputting an index for each of the acceleration data measured over time by the acceleration sensor attached to the subject.

According to still another aspect of the present invention, a system having a computing device and a storage device is a method for supporting clinical evaluation that outputs an index for supporting clinical evaluation of a predetermined disease for a test subject, and is a method of supporting clinical evaluation. Is different from the data obtained by time-series analysis of the acceleration measured in advance for each stage of the disease by the acceleration sensor attached to the body of the target group for the target group including the healthy group and the disease group. , A step of associating with an index indicating the degree of damage to a predetermined skeletal muscle in the target group and storing it in a storage device, and a step of the arithmetic device acquiring acceleration data from an acceleration sensor mounted on the body portion of the test subject. Based on the correlation between the data obtained from the acceleration stored in the storage device and the index indicating the degree of injury, the calculation device uses the acceleration data measured by the acceleration sensor for the test subject to obtain the index of the test subject. It includes a step of estimating and outputting.

According to yet another aspect of the present invention, a system having a computing device and a storage device is a method of assisting a test subject in assessing the effect of a drug or drug candidate on a given disease, unlike the test subject. For the target group including the healthy group and the disease group, the data obtained by time-series analysis of the acceleration measured in advance for each stage of the disease by the acceleration sensor attached to the body part of the target group, and the target group. Accelerometer data from accelerometers mounted on the body of the subject , where the arithmetic device took the drug or drug candidate, and the steps stored in the storage device in association with an index indicating the degree of damage to a given skeletal muscle in acquiring, computing device, based on the correlation between the index indicating the degree of the obtained data and injury from accelerations stored in the storage device, by the acceleration data measured by the acceleration sensor for the test subject, It includes a step of estimating and outputting an index of a test object.

According to yet another aspect of the present invention, in a clinical evaluation program for causing a computer having a computing device and a storage device to perform arithmetic processing that supports clinical evaluation for performing a clinical evaluation of a predetermined disease on a subject. Therefore, unlike the test subjects, the clinical evaluation program was measured in advance for each stage of the disease by an acceleration sensor attached to the body of the target group for the target group including the healthy group and the disease group. A step of associating the data obtained by time-series analysis of acceleration with an index indicating the degree of damage to a predetermined skeletal muscle in the target group and storing it in a storage device and a calculation device are attached to the body part of the test subject. acquiring acceleration data from the acceleration sensor that is, arithmetic unit, based on the correlation between the index indicating the degree of the obtained data with the injury from the stored acceleration in the storage device, an acceleration sensor for subject The computer is made to perform a step of estimating and outputting an index of the test object based on the measured acceleration data.

According to the clinical evaluation device, clinical evaluation method, and clinical evaluation program of the present invention, it is possible to perform highly quantitative motor function evaluation and obtain a clinical evaluation index for a patient with myogenic disease.

In addition, according to the clinical evaluation device, clinical evaluation method, and clinical evaluation program of the present invention, it is possible to obtain a clinical evaluation index for a patient with myogenic disease while monitoring the physical activity of the subject.

, Is a functional block diagram for explaining the configuration of the sensor device 100 and the data analysis computer 1000 constituting the clinical evaluation device. It is a figure which shows the hardware structure of the data analysis computer 1000 in the block diagram format. It is a conceptual diagram for demonstrating the physical quantity measured by a sensor device 100. It is a functional block diagram for demonstrating the function performed by the data analysis computer 1000. It is a figure for _{demonstrating CXMD J} which is a model animal for muscular dystrophy. It is a figure which shows the change of the age and pathological condition of a muscular dystrophy dog CXMD _J. It is a figure which shows the usage mode of the sensor device 100 of this embodiment. It is a figure which shows the change by the age of the acceleration absolute value of a target dog. It is a figure which shows the change by the age of the acceleration absolute value of a target dog. It is a figure which shows the age-related transition of the ratio of the three-direction axis of the acceleration absolute value. It is a figure which shows the age-related transition of the average value of the absolute value of the angular velocity. It is a figure which shows the aging transition of the clinical grading score and the trend estimation using the local level model. It is a figure for demonstrating the evaluation of skeletal muscle injury by MRI imaging. It is a figure which shows the average SNR (Signal to Noise Ratio) in the T2-weighted image of the annular cross section of the lower leg of a target dog in comparison with a healthy dog and a patient dog. It is a figure which shows the Pearson correlation coefficient between the acceleration / angular velocity parameter measured by the sensor device attached to the lumbar region, and the average SNR of each skeletal muscle. It is a figure which shows the correlation between the absolute value of acceleration and the average SNR value of a fast muscle dominant muscle, and the correlation of acceleration X, and the average SNR value of a slow muscle dominant muscle.

Hereinafter, the configuration of the clinical evaluation device, the clinical evaluation method, and the clinical evaluation program according to the embodiment of the present invention will be described with reference to the drawings. In the following embodiments, the components and processing steps having the same reference numerals are the same or equivalent, and the description thereof will not be repeated if they are not necessary.

(Embodiment)
FIG. 1 is a functional block diagram for explaining the configurations of the sensor device 100 and the data analysis computer 1000 that constitute the clinical evaluation device.

The sensor device 100 has a communication front end 101 for wirelessly receiving a synchronization signal or time signal, data and commands from the data analysis computer 1000, and a clock interface (clock I / F) for receiving the synchronization signal or time signal. It includes 102, a data analysis computer 1000, and a data input / output interface (data input / output I / F) 106 for exchanging measurement data and commands by wireless communication in the wireless communication method as described above.

The sensor device 100 further includes a clock generation unit 104 that generates a clock signal for controlling the measurement timing based on the signal from the clock I / F 102, and a microcomputer 110 for controlling the measurement operation of the sensor device 100. .. The microcomputer 110 operates based on the clock signal from the clock generation unit 104, and performs a control operation according to a command from the data analysis computer according to an internal program.

Here, the "synchronization signal" is a timing signal for synchronizing the internal clock of the data analysis computer and the clock generated by the clock generation unit 104. The "time signal" is a signal including information (time information) representing the time itself sent from the data analysis computer 1000, and the clock generation unit 104 is reset by this time information and is represented by the time information. Start measuring the time from the time.

From the data analysis computer 1000, both the "synchronization signal" and the "time signal" may be transmitted to the sensor device 100, or either of them may be used as the internal clock of the data analysis computer 1000. It may be configured to synchronize with the internal clock of the sensor device 100.

The microcomputer 110 uses the sensor interface (sensor I / F) 132 to input the acceleration measurement data from the acceleration sensor 120, the angular velocity measurement data from the angular velocity sensor 122, and the signal from the external sensor 130 connected as needed. The receiving external sensor amplifier 134 amplifies and receives the A / D converted measurement data, and manages these measurement data in an integrated manner. The external sensor 130 is not particularly limited, and for example, a sensor that acquires a biological signal such as a myoelectric signal, an electrocardiographic signal, or an electroencephalogram signal can be used.

Prior to the start of the measurement, the clock generation unit 104 is reset by receiving the synchronization signal or the time information in order to synchronize the internal clock. The reset of the clock generation unit 104 is performed based on the signal from the data analysis computer 1000 in a wired or wireless communication environment controlled with a negligible small delay. The microcomputer 110 adds a time stamp generated based on the synchronization signal or time information obtained from the clock generation unit 104 to the measurement data, and transmits the time stamp to the data analysis computer 1000.

FIG. 2 is a diagram showing the hardware configuration of the data analysis computer 1000 in a block diagram format.

As shown in FIG. 2, the computer main body 2010 constituting the data analysis computer 1000 includes a CPU (Central Processing Unit) 2040 connected to the bus 2050 and a ROM, respectively, in addition to the disk drive 2030 and the memory drive 2020. Includes memory including (Read Only Memory) 2060 and RAM (Random Access Memory) 2070, a non-volatile rewritable storage device, such as a hard disk 2080, and a communication interface 2090 for communicating over a network. I'm out. An optical disk such as a CD-ROM2200 is mounted on the disc live 2030. A memory card 2210 is attached to the memory drive 2020.

As will be described later, when the program of the data analysis computer 1000 operates, the database that stores the information that is the basis of the operation will be described as being stored in the hard disk 2080. However, such a database may be stored on another computer that communicates over the network.

In FIG. 2, a CD-ROM2200 is assumed as a medium on which information such as a program installed on the computer can be recorded, but another medium such as a DVD-ROM (Digital Versatile Disc) or the like is assumed. However, it may be a memory card, a USB memory, or the like. In that case, the computer main body 2200 is provided with a drive device capable of reading these media.

The main part of the data analysis computer 1000 is composed of computer hardware and software executed by the CPU 2040. Generally, such software is stored in a storage medium such as a CD-ROM2200 and distributed, is read from the storage medium by a disk drive 2030 or the like, and is temporarily stored in a hard disk 2080. Alternatively, when the device is connected to the network, it is temporarily copied from the server on the network to the hard disk 2080. Then, it is further read from the hard disk 2080 to the RAM 2070 in the memory and executed by the CPU 2040. If it is connected to the network, it may be directly loaded into the RAM and executed without being stored in the hard disk 2080.

The program for functioning as the data analysis computer 1000 does not necessarily include an operating system (OS) that causes the computer body 2010 to execute functions such as an information processing device. The program need only include a portion of the instruction that calls the appropriate function (module) in a controlled manner to obtain the desired result. It is well known how the computer system 2010 works, and detailed description thereof will be omitted.

Further, the number of computers that execute the above program may be singular or plural. That is, centralized processing may be performed, or distributed processing may be performed.

Further, the CPU 2040 may be one processor or a plurality of processors. That is, it may be a single-core processor or a multi-core processor.

FIG. 3 is a conceptual diagram for explaining a physical quantity measured by the sensor device 100.

As shown in FIG. 3A, it is assumed that the outer shape of the sensor device 100 is a substantially rectangular parallelepiped, and the x-axis, y-axis, and z-axis are set.

As shown in FIG. 3B, the sensor device 100 first uses the acceleration sensor 120 to measure the acceleration as the rate of change of the velocity of the sensor device in a predetermined direction (x-axis direction, y-axis direction, z-axis direction). measure. The unit is G, and 1G is about 9.8 m / s ² .

As shown in FIG. 3B, the sensor device 100 also measures the angular velocity around a predetermined direction (x-axis direction, y-axis direction, z-axis direction) of the sensor device by the angular velocity sensor 122. The unit is dps (degree per sec).

FIG. 4 is a functional block diagram for explaining the functions performed by the data analysis computer 1000.

The CPU 2040 functions and operates as a standard data collection unit 2040.1 and a clinical evaluation index generation unit 2040.2 according to the program.

The standard data collection unit 2040.1 receives acceleration data from the first test subject groups of different stages and performs time-series analysis as described later, and the acceleration data time-series analysis unit 2041 and the first of different stages. The output of the angular velocity data time series analysis unit 2042 and the acceleration data time series analysis unit 2041 and the output of the angular velocity data time series analysis unit 2042, which receive the angular velocity data from the test subject group and perform time series analysis as described later. It includes a standard data integration unit 2043 that receives data on the degree of muscle injury at each stage of the first test subject group as input, integrates it as standard data, and stores it in the non-volatile storage device 2080.

Here, it is assumed that the first test subject group of different stages also includes a test subject in a healthy state.

The clinical evaluation index generation unit 2040.2 receives acceleration data and / or angular velocity data for a second test subject different from the first test subject group as input, and standard data stored in the non-volatile storage device 2080. 2046 includes a muscle injury degree estimation unit 2046 that outputs an estimated value of the muscle injury degree from the correlation in.

As the input of the muscle injury degree estimation unit 2046, the value at that time after smoothing by performing the time series analysis up to the time when the estimation as described later is executed is used, and the muscle at that time is used. It is also possible to output an estimated value of the degree of injury.

"Muscle injury degree data" is a parameter indicating the degree of injury to a predetermined skeletal muscle, and is, for example, "MRI (magnetic resonance imaging) for a predetermined skeletal muscle" as described later. ) May be the "average SNR value in the T2-weighted image" itself, or may be a value indicating the corresponding rank when the "average SNR value" is divided into a plurality of ranks in advance. Furthermore, it is desirable that the "predetermined skeletal muscle" is a fast muscle significant muscle. However, the "predetermined skeletal muscle" may be a slow muscle significant muscle. Alternatively, the "predetermined skeletal muscle" may be one type of skeletal muscle of a specific site, and the "muscle injury degree data" may be "average SNR value" or rank information for the site, or "muscle injury". The degree data may be a composite parameter such as a sum of weighted average SNR values for a plurality of types of skeletal muscle.

Further, the "estimated value of the degree of muscle injury" is the second input by the muscle injury degree estimation unit 2046 based on the correlation of the standard data stored in the non-volatile storage device 2080 as described above. It refers to an "estimated value representing the degree of muscle injury" estimated for acceleration data and / or angular velocity data for the test subject.

In FIG. 4, the standard data collection unit 2040.1 and the clinical evaluation index generation unit 2040.2 have been described as functions in the same computer system 2010, but the configuration is not necessarily limited to such a configuration.

For example, the standard data collection unit 2040.1 and the non-volatile storage device 2080 are configured by one computer system, and the standard data stored in the non-volatile storage device 2080 is copied to another non-volatile storage device and a clinical evaluation index. The generation unit 2040.2 may be configured by another computer system. That is, the acquisition of standard data and the estimation of the degree of muscle injury may be configured to be executed by different computer systems.

Further, as will be described later, the degree of muscle injury is estimated by the estimation process of the time-series analysis data of the acceleration data of the subject and / or the time-series analysis data of the angular velocity data of the subject and the regression analysis of the degree of muscle injury. Or, for example, when the relationship between the time-series analysis data of the subject's acceleration data and / or the time-series analysis data of the subject's angular velocity data and the degree of muscle injury is expressed by a neural network, this is machine-learned. The estimation model obtained by training may be used, and more generally, the estimation model obtained by training a specific model by machine learning may be used.

Further, when the "muscle injury degree data" includes the injury degree data for a plurality of types of skeletal muscles, for example, the data of the average SNR value, the muscle injury degree estimation unit 2046 may estimate the muscle injury degree, for example. , The weighted sum of the damage degree data for a plurality of types of skeletal muscles derived by a statistical method such as canonical correlation analysis may be input. Alternatively, when machine learning using a neural network or the like is used as described above, the weighted sum of the damage degree data for a plurality of types of skeletal muscles derived by such machine learning may be input.
[Measurement results and clinical evaluation indicators using model animals]
The measurement results of muscular dystrophy by model animals will be described below.

FIG. 5 is a diagram for explaining _{CXMD J} , which is a model animal for muscular dystrophy.

As shown in FIG. 5 (a), as a model animal, a muscular dystrophy dog (Canine X-linked muscular dystrophy in Japan: CXMD _J ) created as a disease model of Duchenne muscular dystrophy (DMD) is used.

The muscle dystrophy dog CXMD _J is a small dog by artificially fertilizing the frozen sperm of the Golden Retriever muscle dystrophy dog, which was previously established as a dog DMD model, into a beagle dog, and further mating the produced female carrier dog with the beagle dog. It is a systematic Beagle species that is easy to maintain.

FIG. 5B shows an optical micrograph of a histopathological image of skeletal muscle in comparison with a normal dog and a patient dog.

Muscular dystrophy dog CXMD _J lacks dystrophin due to a point mutation in the dystrophin gene intron 6, and histopathology such as necrosis / regeneration of muscle fibers in skeletal muscle, inflammatory cell infiltration, fibrosis and fat infiltration similar to DMD. You can check the target findings.

As a result, the muscular dystrophy dog CXMD _J presents with various clinical symptoms such as systemic skeletal muscle atrophy, joint contracture, spinal kyphosis, myocardial damage, and gastrointestinal damage based on progressive pathology.

Here, for example, a method for treating a muscular dystrophy dog by exon skipping will be briefly described as follows.

i) Genomic DNA on the chromosome contains sequences required for translation into proteins (exons) and sequences not required (introns), alternating with exons, introns, exons, and introns. .. The dystrophin gene consists of 79 exons. Introns are removed from the messenger RNA (mRNA) precursor transcribed using genomic DNA as a template, and mRNA consisting only of exons is synthesized. The process by which this intron is removed and the exons are joined together is called splicing. Dystrophin is synthesized by reading (translating) amino acids from mRNA through splicing.

ii) In muscular dystrophy dogs, a gene mutation in the intron 6 of the dystrophin gene causes exons 7 to be skipped without being spliced normally in the pre-mRNA, and exons 6 and 8 are directly linked in mRNA. Then, the reading frame of the amino acid shifts, and the reading frame encoding the translation stop to the amino acid appears in exon 8. Therefore, dystrophin is not synthesized and the symptoms of muscular dystrophy appear. However, if you block the splicing of exons 6 and 8 with morpholino, you can forcibly skip exons 6 and 8. Exons 5 and 9 are directly linked, and a slightly smaller dystrophin is synthesized in order to eliminate the deviation of the amino acid reading frame.

iii) A single-stranded mRNA precursor is transcribed using double-stranded genomic DNA as a template, but antisense a single-stranded synthetic nucleic acid analog having a (complementary) sequence opposite to that of the mRNA precursor. -Called an oligonucleotide. Antisense oligonucleotides can be made from nucleic acid compounds consisting of bases, sugars, and phosphate groups, of which morpholinic rings, which are artificial compounds, replace the deoxyribose ring in DNA and the ribose ring in RNA. Nucleic acid compounds using the above are called morpholinos. Exons can be skipped by inhibiting pre-mRNA splicing with morpholino.

It has been confirmed that administration of such morpholino enables systemic treatment of muscular dystrophy dogs.

Furthermore, clinical trials and clinical trials of therapeutic agents using morpholino compounds that induce exon 53 skipping of the human dystrophin gene are also being conducted in humans.

FIG. 6 is a diagram showing changes in the age and pathological condition of the muscular dystrophy dog CXMD _J.

As shown in FIG. 6, the muscular dystrophy dog CXMD _J suffers from diaphragmatic injury due to the start of respiration at birth and has a mortality rate of 20 to 30% within 1 week of age.

Furthermore, clinical symptoms progress with age, and atrophy of limb muscles and trunk muscles is observed from 2 months of age, which is the onset time.

At 1 year of age, fibrosis is seen in the muscles, the symptoms become chronic, and the progression of the pathological condition is stagnant.

Therefore, the horizontal axis of FIG. 6 represents the stage of myogenic disease (eg, muscular dystrophy). As will be described later, for the muscular dystrophy dog CXMD _J , skeletal muscle damage is quantified by MRI measurement for each stage.

FIG. 7 is a diagram showing a usage mode of the sensor device 100 of the present embodiment.

First, FIG. 7A is a diagram showing the right side surface of the muscular dystrophy dog with the sensor device 100 attached, and FIG. 7B is a diagram showing the back side surface.

An acceleration sensor 120 and an angular velocity sensor 122 are provided inside the sensor device 100, and measurement data of acceleration and angular velocity are transmitted from the sensor device 100 to the data analysis computer 1000 by wireless communication. The wireless communication method may be so-called wireless LAN communication, or the Bluetooth (registered trademark) method may be used as the short-range communication method.

The data analysis computer 1000 performs processing such as storage of measurement data received from the sensor device 100 by wireless communication, data display, and data analysis.

As shown in FIGS. 7 (a) and 7 (b), the sensor device 100 includes the dorsal chest near the 6th thoracic spinous process between the dorsal margins of the left and right scapulas and the 7th lumbar vertebra between the left and right iliac sacrococcygeal nodules. It is attached to the dorsal lumbar region near the spinous process.

In measuring the acceleration and angular velocity, the measurement will be carried out in 15m running x 4 times. However, the measurement will end when the person sits down due to fatigue.

The measurement targets are 5 males of affected dogs aged 2 to 12 months, 4 males and 2 females of healthy dogs.

Gravitational acceleration is removed as a DC component at rest for 2-10 seconds.

In addition, in order to analyze the relationship between the pathological condition and acceleration / angular velocity, the following items are also evaluated.

(1) Clinical evaluation: Clinical grading score, self-issued activity (2) Muscle injury evaluation: MRI imaging (T2-weighted image) (front and hind limb muscles Figures 8 and 9 show changes in the absolute acceleration value of the target dog with age. It is a figure which shows.

FIG. 8 (a) shows the change in the absolute value of the acceleration of the chest with age in comparison between the healthy dog and the affected dog.

FIG. 9A shows the change in the absolute value of the acceleration of the lumbar region with age in comparison between the healthy dog and the affected dog.

Here, the absolute value of acceleration | a | is expressed by the following equation, where the components of acceleration in the x-direction, y-direction, and z-direction are ax, ay, and az, respectively.

また、図８（ｂ）は、胸部の加速度絶対値を時系列データとしてローカルレベルモデルにより解析した結果の月齢による変化を、健常犬と患犬とで対比して示す。

In addition, FIG. 8B shows the change with age as a result of analyzing the absolute value of the acceleration of the chest as time series data by a local level model in comparison between the healthy dog and the affected dog.

Further, FIG. 9B shows the change with age as a result of analyzing the absolute value of the acceleration of the waist as time series data by a local level model in comparison between the healthy dog and the affected dog.

(State space representation of time series model)
Hereinafter, the "local level model" will be described.

The state space model is one of the statistical methods that can handle time series data. In time series data, the measured values at a certain point in time are often correlated with the measured values before and after that, that is, there is a temporal autocorrelation. Therefore, in order to analyze time series data, a statistical method considering autocorrelation is required.

The state-space model also models the system itself to be observed (but not directly) and the process of actually obtaining the observed values separately. As a result, it is possible to explicitly handle the error (noise) derived from the system itself and the error (noise) associated with the observation, and it is possible to obtain a reasonable guess about the temporal change of the system.

Of the state-space models, the structure of the model called the local level model can be obtained by making the following assumptions.

i) Observation data that changes in time series is generated from the true state + observation error that also changes in time series.

ii) The true state moves in the state before the temporary point + the movement range.

For example, mathematically, it can be formulated as follows.

ここで、式（１）は、状態から観測値が得られる過程をモデル化したもので、データモデル（あるいは「観測モデル」）と呼ばれる。式（２）は、状態の変化をモデル化したもので、プロセスモデル（あるいは「システムモデル」）と呼ばれる。y_t は時点tにおける観測値のベクトル、θ_tは時点ｔにおける状態のベクトルである。Ｆ_t, Ｇ_tは係数行列であり、v_t, w_tはそれぞれ、データモデルおよびプロセスモデルの時点tの誤差ベクトルである。Ｖ_t はデータモデルの誤差の分散共分散行列、Ｗ_tはプロセスモデルの誤差の分散共分散行列である。ｍ₀，Ｃ₀はそれぞれ、時点０における状態θ₀の平均のベクトルおよび分散共分散行列である。

Here, the equation (1) is a model of the process of obtaining the observed value from the state, and is called a data model (or "observed model"). Equation (2) models a change in state and is called a process model (or "system model"). y _t is the vector of the observed value at the time point t, and θ _t is the vector of the state at the time point t. F _t and G _t are coefficient matrices, and v _t and w _t are error vectors of the time points t of the data model and the process model, respectively. V _t is the variance-covariance matrix of the error of the data model, and W _t is the variance-covariance matrix of the error of the process model. m ₀ and C ₀ are the mean vector and the variance-covariance matrix _{of the state θ 0} at time point 0, respectively.

Tools for analyzing such time-series data using a state-space model include dlm (Bayesian and Likelihood Analysis of Dynamic Linear Models; Petris et al. 2009; Petris 2010) and KFAS (Kalman Filter and Smoother) in the R package. For Exponential Family State Space Models; Helske 2015), Stan (Stan Development Team 2014), etc.

As the estimation method, a combination of Markov chain Monte Carlo methods (MCMC) and Bayesian estimation, a Kalman filter, or a combination of Hamiltonian Monte Carlo methods and Bayesian estimation can be used.

With the "local level model" as described above, it is possible to obtain a curve obtained by smoothing the time change of these values with respect to the time series data of the observed acceleration and angular velocity.

The acceleration / angular velocity parameters of the affected dog are generally lower than those of the healthy dog, and from FIGS. 8 (b) and 9 (b), the time series analysis of the absolute acceleration values indicating the acceleration in all three directions shows. The difference between the affected dog and the healthy dog is already observed at the age of 2 months at the onset time, and is particularly remarkable in the chest. On the other hand, in the absolute value of the acceleration of the lumbar region, a significant decrease with age is observed in the affected dog.

FIG. 10 is a diagram showing the age-related transition of the ratio of the absolute acceleration values on the three-direction axes.

That is, the ratio of each direction axis is a percentage display of the value obtained by dividing the absolute value of the acceleration of each direction axis by the absolute value of acceleration | a |.

In FIG. 10, the solid line indicates the affected dog, and the dotted line indicates the healthy dog.

The x-axis proportion of propulsion in the direction of travel tends to decrease in affected dogs. In addition, the ratio of the y-axis direction, which is the left-right direction to the traveling direction, tends to increase in the affected dog, which is considered to reflect the agitated gait.

The ratio of the z-axis direction, which is the vertical direction to the traveling direction, tends to increase in the affected dog, and is considered to reflect the bunny hop.

In the figure, "*" means that the affected dog and the healthy dog are significantly different in the Holm multiplex test with the significance level <0.05, and "**" means the Holm multiplex with the significance level <0.01. It means that the test is significantly different.

FIG. 11 is a diagram showing the secular transition of the average value of the absolute values of the angular velocity.

It can be seen that the angular velocity of the chest in the y-axis direction is most noticeably different between the healthy dog and the affected dog.

FIG. 12 is a diagram showing age-related changes in clinical grading scores and trend estimation using a local level model.

First, FIG. 12 (a) shows the age-related transition of the clinical grading score.

The clinical grading score is shown as the total score of the following 8 items evaluated on a 5-point scale. The higher the score, the more severe the symptoms.

1) Hypersalivation 2) Dysphagia 3) Gait disorder 4) Temporalis muscle atrophy 5) Thigh muscle atrophy 6) Macroglossia 7) Activity 8) Sitting posture Therefore, in healthy dogs, the score is 8 points, which is the most severely affected dog. Then, it becomes 40 points.

As shown in FIG. 12 (a), in healthy dogs, the score does not change with age, but in affected dogs, the severity increases with age, and the progression of symptoms is almost stagnant at 12 months. Recognize.

FIG. 12B shows the result of trend estimation using the local level model.

That is, it is the result of trend estimation of the relationship between the acceleration / angular velocity parameters and the clinical evaluation score shown in FIG. 12 (a) by the local level model.

In the figure, ◯ is the median value, and the bar indicates the 95% confidence interval.

From FIG. 12B, the trend of the score increases by 1 in logarithm, while the trend of all items except the absolute value of the z-axis component of the angular velocity of the waist takes a value significantly lower than 0. That is, it is shown that these items decrease as the severity increases. Furthermore, while the trend of the proportion in the X-axis direction shows a decreasing tendency, the trend of the proportion in the Y-axis direction increases significantly especially in the chest, so it is considered that the agitation of the gait increases due to the aggravation.

FIG. 13 is a diagram for explaining the evaluation of skeletal muscle injury by MRI imaging.

FIG. 13 shows an image of an annular cross section of the lower leg of the target dog as a T2-weighted image.

As shown in the central portion of FIG. 13 as a water component, the T2-weighted image shows high brightness of water.

In contrast, skeletal muscle is usually visualized on a T2-weighted image with a low signal. When water accumulates in the muscle parenchyma due to a disease, the site becomes hyperintensity. Therefore, T2-weighted images can be used to evaluate the state of muscle damage in the skeletal muscle region.

On the right side of FIG. 13, each skeletal muscle on the T2-weighted image is shown by surrounding it with a white line.

The correspondence between each symbol and the corresponding skeletal muscle is as follows.

TC: Tibialis anterior EDL: Extensor digitorum longus FHL: Flexor digitorum longus FDL: Flexor digitorum longus GM: Medial gastrocnemius GL: Lateral gastrocnemius FDS: Flexor hallucis longus Figure 14 shows the lower leg of the target dog. It is a figure which shows the average SNR (Signal to Noise Ratio) in the T2-weighted image of the annular cross section in comparison between a healthy dog and a patient dog.

The values for affected dogs are shown in dark diamonds, and the values for healthy dogs are shown in gray diamonds.

The SNR value and the average SNR value are defined by the following equations.

ここで、ＳＩは、あるピクセルの輝度値であり、Ｓｄ_airは、犬の体外の空間での輝度の標準偏差を意味する。

Here, SI is the brightness value of a certain pixel, and Sd _air means the standard deviation of the brightness in the space outside the dog's body.

Further, SNR _{i, Right} is the SNR value of the i-th partial region of the corresponding muscle region of the right foot, and Pixel _{i, Right} is the number of pixels of the i-th partial region, and SNR _{i, Left} is the SNR value of the i-th subregion of the corresponding muscle region of the left foot, and Pixel _{i, Left} is the number of pixels of the i-th subregion. Pixel _total is the total number of pixels in the right foot subregion and the left foot subregion. Here, as an example, it is assumed that i = 1, 2, and 3 and three partial regions are considered in each muscle region. However, the number of subregions may be larger than this. Further, the designation of the range of the partial area is, for example, manually set by the operator of the MRI apparatus while observing the image.

As can be seen from FIG. 14, it can be seen that the average SNR value of the affected dog is large for both the lower leg muscle and the thigh muscle. In other words, T2-weighted images by MRI quantitatively show that affected dogs have a greater degree of skeletal muscle injury than healthy dogs.

FIG. 15 is a diagram showing the Pearson correlation coefficient between the acceleration / angular velocity parameters measured by the sensor device mounted on the lumbar region and the average SNR of each skeletal muscle.

FIG. 15 shows data of a 1 year old patient dog. The number of individuals is 5.

In the figure, the portion surrounded by the solid line frame is the portion having the Pearson correlation coefficient r <−0.9, and the portion surrounded by the dotted line frame is the portion having the Pearson correlation coefficient r> 0.9.

FIG. 16 is a diagram showing the correlation between the absolute value of acceleration and the average SNR value of the fast muscle dominant muscle and the correlation between the acceleration X and the average SNR value of the slow muscle dominant muscle.

In DMD, it is known that fast muscle fibers in skeletal muscle tissue are progressively reduced as compared with slow muscle fibers. It is assumed that the motor function that requires instantaneous force may be impaired depending on the pathological condition according to the degree of injury of the fast muscle.

From FIG. 16, the association with the degree of muscle injury of the hind limb muscles using the T2-weighted image of MRI at 12 months of age shows a negative correlation tendency in the fast muscle dominant muscle of the lower leg, whereas in the slow muscle dominant muscle. It shows a significantly positive correlation.

That is, as shown in FIG. 16A, in the tibialis anterior muscle and the extensor digitorum longus muscle, which are the dominant muscles of the fast muscle, the higher the absolute value of acceleration (magnitude), the lower the average SNR value, that is, the skeletal muscle. It can be seen that the degree of injury is low.

This means that in the fast muscle dominant muscle, the absolute acceleration value decreases as the injury progresses, which is considered to reflect the progression pattern of the injury.

On the other hand, as shown in FIG. 16B, in the gastrocnemius lateral head and the flexor digitorum superficialis muscle, which are the slow muscle dominant muscles, the higher the acceleration X (acceleration component in the traveling direction), the higher the average SNR value, that is, that is, It can be seen that the degree of damage to the skeletal muscle is high.

This means that in the slow muscle dominant muscle, the acceleration component in the traveling direction increases as the injury progresses, which is considered to reflect the dynamics of the gait posture.

From the above, in the muscular dystrophy dog CXMD _J , the following can be said about the acceleration / angular velocity parameters of the affected dog.

1) It is lower than that of healthy dogs and shows a decrease as the condition progresses. The difference between the chest and the lumbar region is that in the lumbar region, the degree of decrease due to age (increased degree of injury progression) is large.

2) Abnormal operation can be detected by the ratio of the components of the absolute acceleration value in each axial direction.

3) Shows different changes depending on the degree of injury at the muscle site.

Therefore, by acquiring in advance the data of acceleration and angular velocity detected by the sensor device attached to the body of the test subject for each stage of the patient dog group and the healthy dog group, for example, a new therapeutic agent. In non-clinical studies against, objective indicators for treatment can be obtained.
[Clinical evaluation index for humans]
In addition, when the above-mentioned clinical evaluation index is applied to humans, skeletal muscle damage quantified by MRI measurement is also applied to humans and to a target group including a healthy subject group and a patient group. The degree of is obtained in advance for each stage.

Accelerometers are attached to the chest and lumbar region, which are the center of gravity of the upper body and the center of gravity of the human body, respectively. Dogs, which are quadrupeds, have a characteristic that the center of gravity is located in the chest and the forelimbs serve as struts and kick out with the hindlimbs. The movements of the chest and waist do not directly match between humans and dogs, but in humans, the waist is associated with the movement of the center of gravity linked to the movement of the lower limbs, and the chest is associated with abnormal movements resulting from the swing of the body axis. Tends to reflect results.

Therefore, it is feasible to apply the results of dogs, which are model animals as described above, to humans, for example, by taking the following procedure.

i) From 2-5 years old when gait abnormalities begin to appear in patients with muscular dystrophy to 7-11 years old when gait disorders progress, multiple subjects in the healthy subject group and patient group who are similar in age and physique run 10 m or run for 6 minutes. Follow-up evaluation of gait tests such as gait based on natural history.

ii) Evaluation of the degree of muscle injury by MRI imaging or the like is performed in parallel, and as an objective evaluation value of the degree of muscle injury, an average SNR value of a T2-weighted image or a selective fat suppression T2-weighted image is obtained.

iii) The same skeletal muscle may be evaluated for the degree of injury throughout the pathological condition. Alternatively, select the skeletal muscles of the buttocks, thighs, and lower legs, which are markedly injured depending on the time of the condition, and the semitendinosus muscles such as the soleus muscle, the middle gluteal muscle, the biceps femoris, and the quadriceps femoris in the early stage of the condition. , Semimembranosus muscle, tibialis anterior muscle, soleus muscle, etc., and in the latter stage, gracilis muscle, sartorius muscle, etc. may be targeted.

iv) As described above, for healthy humans and patients, evaluation data of the degree of muscle injury based on MRI images acquired for each stage, acceleration data at the sensor mounting site in each stage, and / Alternatively, the result of the time series analysis of the angular velocity data is integrated as "standard data" and stored in the non-volatile storage device 2080.

v) Based on the correlation of the standard data stored in the non-volatile storage device 2080, the muscle injury degree estimation unit 2046 estimated with respect to the input acceleration data and / or angular velocity data for the second test subject. "Estimated value indicating the degree of muscle injury" is output.

That is, it was found that the results of time-series analysis of the data measured by the accelerometer and the angular velocity sensor attached to the predetermined part of the dog according to the stage of the dog characterize the stage. It is assumed that the results of time-series analysis of the data measured by the accelerometer and the angular velocity sensor mounted on the predetermined site similarly characterize the stage.

Of course, dogs and humans are expected to have different types of skeletal muscles of interest, and different models for estimating stage from acceleration and / or angular velocity measurement data, but so. Even if there are various differences, it is possible to derive a human-specific estimation model for a large amount of data from a large number of target groups by, for example, statistical processing as described above or machine learning. ..

Then, by using the clinical evaluation device having the above configuration, it is possible to objectively and quantitatively obtain an index supporting the diagnosis of myogenic disease by the estimated value output from this clinical evaluation device. ..

That is, conventionally, the diagnosis of myogenic disease has required a doctor to make a comprehensive judgment from a plurality of tests and observations. On the other hand, based on the index from the clinical evaluation device having the above configuration, the doctor can judge the presence or absence of myogenic disease or the stage (pathological stage) of myogenic disease. It is possible to help do.

Further, by using the index from the clinical evaluation device having the above configuration, it is possible to realize a method for evaluating the effect of a drug (or drug candidate) for myogenic disease. For example, by adopting such an index as an endpoint, a subject is allowed to take such a drug (or drug candidate) in a predetermined dose and usage, and a clinical trial is conducted by measuring a change in the above index over time. It is also possible to carry out. In this case, for example, the index may be used as the primary evaluation item and other evaluation items may be used together as the secondary evaluation item.

The embodiments disclosed this time are examples of configurations for concretely implementing the present invention, and do not limit the technical scope of the present invention. The technical scope of the present invention is indicated by the scope of claims, not the description of the embodiment, and includes modifications within the scope of the wording of the claims and the scope of equal meaning. Is intended.

100 sensor device, 101 communication front end, 102 clock I / F, 104 clock generator, 106 data input / output I / F, 110 microcomputer, 120 acceleration sensor, 130 external sensor unit, 132 sensor I / F, 134 external sensor amplifier , 1000 Data analysis computer, 2010 computer body, 2020 memory disk drive, 2030 optical disk drive, 2040 CPU, 2050 bus, 2060 ROM, 2070 RAM, 2080 hard disk, 2100 keyboard, 2110 mouse, 2120 display, 2210 memory card, 2200 Optical disc.

Claims (12)

A clinical evaluation device for clinically evaluating a predetermined disease of a test subject.
An accelerometer mounted on the body of the test subject and
Unlike the test subject, a time-series analysis of the acceleration measured in advance for each stage of the disease by an acceleration sensor attached to the body portion of the subject group for the subject group including the healthy group and the disease group. A storage device that stores the data obtained by the above-mentioned data and an index indicating the degree of damage to a predetermined skeletal muscle in the target group in association with each other.
Based on the correlation between the data obtained from the acceleration stored in the storage device and the index indicating the degree of injury, the acceleration data measured by the acceleration sensor for the test subject is used to obtain the subject. A clinical evaluation device including a clinical index estimation means for estimating an index. The clinical evaluation device according to claim 1, wherein the target of the time series analysis is the absolute value of the acceleration. The acceleration sensor has a plurality is attached to the torso portion of the subject,
The clinical evaluation device according to claim 1, wherein the clinical index estimation means estimates the index based on data obtained from a plurality of accelerations acquired from each of the plurality of acceleration sensors. Further comprising an angular velocity sensor attached to the trunk body portion of the subject,
The storage device includes a statistic of the angular velocity obtained by time-series analysis of the angular velocity measured in advance for each stage by the angular velocity sensor attached to the body portion of the target group with respect to the target group, and the target. Stored in association with an indicator of the degree of injury to a given skeletal muscle in the group
The clinical evaluation device according to claim 1, wherein the clinical index estimation means estimates the index based on the data obtained from the acceleration and the statistic of the angular velocity. The clinical evaluation device according to any one of claims 1 to 4, wherein the disease is a myogenic disease. The clinical evaluation device according to any one of claims 1 to 5, wherein the test subject is a human. The clinical evaluation device according to any one of claims 1 to 5, wherein the test subject is a dog. The index indicating the degree of injury of a given skeletal muscle is the average signal-to-noise ratio of the skeletal muscle sites in T2-weighted images by nuclear magnetic resonance imaging, clinical evaluation apparatus according to claim 7 wherein. A method of operating a clinical evaluation device for supporting the evaluation of the effect of a drug or drug candidate in the clinical evaluation of a predetermined disease for a test subject in which a drug or drug candidate is prescribed in a predetermined volume and usage.
The clinical evaluation device is
An accelerometer mounted on the body of the test subject and
Unlike the test subject, a time-series analysis of the acceleration measured in advance for each stage of the disease by an acceleration sensor attached to the body portion of the subject group for the subject group including the healthy group and the disease group. A storage device that stores the data obtained by the above-mentioned data and an index indicating the degree of damage to a predetermined skeletal muscle in the target group in association with each other.
Based on the correlation between the data obtained from the acceleration stored in the storage device and the index indicating the degree of injury, the acceleration data measured by the acceleration sensor for the test subject is used to obtain the subject. It is equipped with a clinical index estimation means for estimating the index.
An operation method comprising a step in which the clinical index estimation means outputs the index for each of the acceleration data measured over time by an acceleration sensor attached to the subject. It is a clinical evaluation support method that outputs an index for supporting the clinical evaluation of a predetermined disease for a test subject by a system having an arithmetic unit and a storage device.
Unlike the test subject, a time-series analysis of the acceleration measured in advance for each stage of the disease by an acceleration sensor attached to the body portion of the target group for the target group including the healthy group and the disease group. The step of associating the data obtained by the above with the index indicating the degree of damage to a predetermined skeletal muscle in the target group and storing it in the storage device, and
A step in which the arithmetic unit acquires acceleration data from an acceleration sensor mounted on the body portion of the test object.
Based on the correlation between the data obtained from the acceleration stored in the storage device and the index indicating the degree of injury, the arithmetic unit uses the acceleration data measured by the acceleration sensor for the test subject. , A method for supporting clinical evaluation , comprising a step of estimating and outputting the index of the test subject. A method of assisting in the evaluation of the effect of a drug or drug candidate on a given disease for a subject by a system having an arithmetic unit and a storage device.
Unlike the test subject, a time-series analysis of the acceleration measured in advance for each stage of the disease by an acceleration sensor attached to the body portion of the target group for the target group including the healthy group and the disease group. a step of the data obtained, in association with an index indicating the degree of injury of a predetermined skeletal muscle in the subject group, and stores in the storage device by,
A step in which the arithmetic unit acquires acceleration data from an acceleration sensor mounted on the body portion of the test subject who has taken the drug or drug candidate.
Based on the correlation between the data obtained from the acceleration stored in the storage device and the index indicating the degree of injury, the arithmetic unit uses the acceleration data measured by the acceleration sensor for the test subject. A method of supporting the evaluation of the effect of a drug or a drug candidate, which comprises a step of estimating and outputting the index of the test subject. A clinical evaluation program for causing a computer having an arithmetic unit and a storage device to perform arithmetic processing for supporting clinical evaluation for performing clinical evaluation of a predetermined disease on a subject, and the clinical evaluation program is a clinical evaluation program.
Unlike the test subject, a time-series analysis of the acceleration measured in advance for each stage of the disease by an acceleration sensor attached to the body portion of the target group for the target group including the healthy group and the disease group. The step of associating the data obtained by the above with the index indicating the degree of damage to a predetermined skeletal muscle in the target group and storing it in the storage device, and
A step in which the arithmetic unit acquires acceleration data from an acceleration sensor mounted on the body portion of the test object.
The arithmetic unit, based on the correlation between the index indicating the degree of the obtained data and the injury from the acceleration stored in the storage device, by the subject the acceleration data measured by the acceleration sensor for , A clinical evaluation program that causes a computer to perform a step of estimating and outputting the index of the test subject.

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