JP5201472B2 - Blood cell analyzer, blood cell analysis method and computer program - Google Patents

Description

  The present invention relates to a blood cell analyzer, a blood cell analysis method, and a computer program that can accurately estimate measurement data of an inflammation-related marker that is not a direct measurement target based on distribution data of white blood cells in a blood sample.

  Normally, five types of normal white blood cells, lymphocytes, monocytes, eosinophils, neutrophils, and basophils, are present at a predetermined ratio in normal peripheral blood. It is possible to determine whether or not the existence ratio of these components is normal by executing the classification count of the white blood cells in the blood.

  That is, when a disease is present, phenomena such as fluctuation of the classification ratio of these white blood cells and appearance of abnormal cells occur. For example, when an inflammatory reaction occurs in vivo due to bacterial infection or the like, the ratio of young leukocytes increases, and the ratio of lobulated nuclei, rod-shaped nuclei, etc. with a small number of lobulated nuclei increases, so-called Left shift occurs. Therefore, effective information for diagnosing whether or not a disease has occurred can be acquired by executing white blood cell classification and counting. In addition, the value of inflammation-related markers such as C-reactive protein (CRP) also increases.

For example, Patent Document 1 discloses a white blood cell classification and counting method capable of counting normal white blood cells and counting young white blood cells. Patent Document 2 discloses a measurement method for measuring the CRP concentration in blood without centrifuging the collected blood. Patent Document 3 discloses a similar pattern search device that enables a similar search in a white blood cell particle size pattern.
Japanese Patent Laid-Open No. 2006-91024 Japanese Patent No. 3780599 WO2005 / 050479 publication

  However, in Patent Documents 1 to 3 described above, it is necessary to separately measure CRP by an immunological measurement method as well as a mechanism for counting normal white blood cells, and a reagent, an apparatus, and the like are required. Of course, a device capable of performing CRP measurement and leukocyte measurement with a single device is also sold, but there is no change in that an immunoassay reagent for CRP measurement is required.

  The present invention has been made in view of such circumstances, and can acquire normal white blood cell distribution data in a blood sample and accurately estimate measurement data of inflammation-related markers based on the acquired distribution data. An object is to provide a blood cell analyzer, a blood cell analysis method, and a computer program.

In order to achieve the above object, the blood cell analyzer according to the first invention has a detection unit for detecting a predetermined component of white blood cells contained in a plurality of samples, and distribution data of white blood cells based on the detection result of the detection unit. A correlation information storage means for storing correlation information regarding the correlation between the distribution data and the measurement data of C-reactive protein , the acquired white blood cell distribution data, and the stored correlation A C-reactive protein estimating means for estimating C-reactive protein measurement data based on the information and an output means for outputting the estimated C-reactive protein measurement data are provided.

The blood cell analyzer according to a second aspect of the present invention is the blood cell analyzer according to the first aspect, further comprising category classification means for classifying the acquired white blood cell distribution data into a plurality of categories, wherein the correlation information storage means is provided for each classified category. Correlation information relating to the correlation between the distribution data included and the measurement data of C-reactive protein is stored.

According to a third aspect of the present invention, there is provided the blood cell analyzer according to the second aspect, further comprising a conversion means for converting the acquired white blood cell distribution data into distribution data for each of a plurality of small regions, wherein the correlation information storage means is converted. The correlation information is stored as a result of multiple regression analysis of the distribution data and the measurement data of the C-reactive protein , and the C-reactive protein estimation means stores the converted distribution data and the stored correlation. The measurement data of C-reactive protein is estimated based on the relationship information.

The blood cell analyzer according to the fourth invention is the blood cell analyzer according to the third invention, wherein the correlation information storage means is a plurality of small data converted from white blood cell distribution data Xk (k = 1, 2,..., N). distribution area data xki (i = 1,2, ···, s), and calculated based on the measurement data Ck of the obtained C-reactive protein, measurement data Ck and a plurality of small regions of the C-reactive protein The multiple regression function h with the distribution data xki (i = 1, 2,..., S) is stored as the correlation information, and the C-reactive protein estimation means obtains the distribution of the acquired white blood cells. On the basis of the distribution data xi for each small region converted from the data X and the multiple regression function h, the estimated measurement data C of C-reactive protein is calculated by equation (1).
C = h (x1, x2,..., Xi,..., Xs) Expression (1)

The blood cell analyzer according to a fifth aspect of the present invention is the blood cell analyzer according to the third aspect, wherein the correlation information storage means includes a plurality of small data converted from white blood cell distribution data Xk (k = 1, 2,..., N). Multiple regression coefficients c0, ci calculated by executing linear multiple regression analysis based on the region distribution data xki (i = 1, 2,..., S) and the acquired measurement data Ck of C-reactive protein. Is stored as the correlation information, and the C-reactive protein estimating means is based on the distribution data xi for each small region converted from the acquired white blood cell distribution data X and the multiple regression coefficients c0 and ci. The estimated measurement data C of the C-reactive protein is calculated by the equation (2).

Further, the blood cell analysis apparatus according to the sixth invention, in any one of the first to fifth invention, the measurement data of C-reactive protein, which is estimated, the measurement data after predetermined days elapse of C-reactive protein It is characterized by being.

Next, in order to achieve the above object, a blood cell analysis method according to a seventh aspect of the present invention is a blood cell analyzer that detects predetermined components of white blood cells contained in a plurality of specimens and acquires white blood cell distribution data based on the detection results. In the blood cell analysis method that can be performed in the method, the correlation information regarding the correlation between the distribution data and the measurement data of C-reactive protein is stored, and the acquired white blood cell distribution data and the stored correlation information Based on this, the measurement data of C-reactive protein is estimated, and the estimated measurement data of C-reactive protein is output.

The blood cell analysis method according to the eighth invention is the blood cell analysis method according to the seventh invention, wherein the acquired white blood cell distribution data is classified into a plurality of categories, and the distribution data and C-reactive protein measurement data included in each classified category. It stores the correlation information related to the correlation.

The blood cell analysis method according to the ninth invention is the blood cell analysis method according to the eighth invention, wherein the acquired white blood cell distribution data is converted into distribution data for each of a plurality of small regions, and the converted distribution data and C-reactive protein measurement data are converted. The correlation information is stored as a result of multiple regression analysis, and measurement data of C-reactive protein is estimated based on the converted distribution data and the stored correlation information.

In addition, the blood cell analysis method according to the tenth aspect of the invention is the ninth aspect of the invention, wherein a plurality of small region distribution data xki (i = i = n) converted from leukocyte distribution data Xk (k = 1, 2,..., N). 1, 2, ..., s), and C-reactive protein measurement data Ck calculated based on the acquired C-reactive protein measurement data Ck and distribution data xki (i = 1) of a plurality of small regions 2, ..., s) is stored as the correlation information, and is based on the distribution data xi for each small region converted from the acquired white blood cell distribution data X and the multiple regression function h. Then, the estimated measurement data C of C-reactive protein is calculated by equation (3).
C = h (x1, x2,..., Xi,..., Xs) (3)

The blood cell analysis method according to the eleventh aspect of the present invention is the blood cell analysis method according to the ninth aspect, wherein the distribution data xki (i = i = a) of the plurality of small regions converted from the distribution data Xk (k = 1, 2,. , S), and multiple regression coefficients c0 and ci calculated by executing linear multiple regression analysis based on the obtained measurement data Ck of C-reactive protein are stored as the correlation information. Based on the distribution data xi for each small region converted from the acquired white blood cell distribution data X and the multiple regression coefficients c0 and ci, the estimated measurement data C for C-reactive protein is calculated by the equation (4). Features.

Further, the blood cell analyzing method according to a twelfth invention, in any one of the seventh to eleventh invention, the measurement data of C-reactive protein, which is estimated, the measurement data after predetermined days elapse of C-reactive protein It is characterized by being.

Next, in order to achieve the above object, a computer program according to a thirteenth aspect of the present invention has a detection unit that detects a predetermined component of white blood cells contained in a plurality of specimens, and based on the detection results of the detection unit, In a computer program that can be executed by a blood cell analyzer that acquires distribution data, the blood cell analyzer stores correlation information relating to a correlation between the distribution data and measurement data of C-reactive protein. storage means, based on the obtained distribution data and the stored correlation information are leukocyte, C-reactive protein estimating means for estimating the measured data of the C-reactive protein, and the measurement data of the estimated C-reactive protein It functions as an output means for outputting.

A computer program according to a fourteenth aspect of the present invention is the computer program according to the thirteenth aspect , wherein the blood cell analyzer functions as a category classification unit that classifies the acquired white blood cell distribution data into a plurality of categories, and the correlation information storage unit includes: It is made to function as a means to memorize | store the correlation information regarding the correlation of the said distribution data contained for every classified category, and the measurement data of C-reactive protein .

The computer program according to a fifteenth aspect of the invention is the computer program according to the fourteenth aspect , wherein the blood cell analyzer functions as conversion means for converting the acquired white blood cell distribution data into distribution data for each of a plurality of small regions. Storage means is made to function as means for storing the correlation information as a result of multiple regression analysis of the converted distribution data and C-reactive protein measurement data, and the C-reactive protein estimation means is converted into the converted distribution data. And based on the stored correlation information, it functions as a means for estimating measurement data of C-reactive protein .

The computer program according to a sixteenth aspect of the present invention is the computer program according to the fifteenth aspect , wherein the correlation information storage means is a plurality of small regions converted from white blood cell distribution data Xk (k = 1, 2,..., N). Calculated from the distribution data xki (i = 1, 2,..., S) and the obtained measurement data Ck of the C-reactive protein , the measurement data Ck of the C-reactive protein and a plurality of small regions Functioning as means for storing a multiple regression function h with the distribution data xki (i = 1, 2,..., S) as the correlation information, and the C-reactive protein estimation means is used to obtain the acquired white blood cell distribution data. Based on the distribution data xi for each small region converted from X and the multiple regression function h, the C-reactive protein estimated measurement data C is made to function as a means for calculating by equation (5).
C = h (x1, x2,..., Xi,..., Xs) (5)

A computer program according to a seventeenth aspect of the present invention is the computer program according to the fifteenth aspect , wherein the correlation information storage means is a plurality of small regions converted from white blood cell distribution data Xk (k = 1, 2,..., N). Multiple regression coefficients c0 and ci calculated by executing linear multiple regression analysis based on the distribution data xki (i = 1, 2,..., S) and the obtained measurement data Ck of C-reactive protein. The C-reactive protein estimation means is made to function as a means for storing the correlation information, and the C-reactive protein estimation means is based on the distribution data xi for each small region converted from the acquired white blood cell distribution data X and the multiple regression coefficients c0 and ci. The C-reactive protein estimated measurement data C is made to function as a means for calculating by the equation (6).

The computer program according to the eighteenth invention, in any one of the thirteenth to seventeenth invention, the measurement data of C-reactive protein, which is estimated is the measured data after a predetermined number of days elapsed in C-reactive protein It is characterized by being.

In the first invention, the seventh invention, and the thirteenth invention, correlation information relating to the correlation between the acquired distribution data and the measurement data of C-reactive protein is stored, and the acquired white blood cell distribution data and are stored. Based on the correlation information, C-reactive protein measurement data is estimated, and the estimated C-reactive protein measurement data is output. Unknown leukocyte distribution data by calculating and storing correlation information between measurement data of C-reactive protein that needs to be measured by other devices, etc., and distribution data of the acquired whole leukocytes Even if it is acquired, measurement data of C-reactive protein can be estimated based on the stored correlation information. Therefore, the measurement data of C-reactive protein , which is not the measurement target, can be accurately estimated from the results of the conventional blood cell analyzer without the need for a separate device or reagent for acquiring the measurement data of C-reactive protein. It is possible to detect the occurrence of a disease at an early stage with an inexpensive blood cell analyzer with a simple configuration.

In the second invention, the eighth invention, and the fourteenth invention, the acquired white blood cell distribution data is classified into a plurality of categories, and the correlation between the white blood cell distribution data included in each classified category and the measurement data of C-reactive protein. Store correlation information regarding the relationship. By calculating and storing correlation information based on a category that shows a high correlation between white blood cell distribution data and C-reactive protein measurement data, C-reactive protein measurement data can be estimated more accurately. can do.

In the third, ninth, and fifteenth inventions, the obtained white blood cell distribution data is converted into distribution data for each of a plurality of small regions, and the converted distribution data and the measurement data of the C-reactive protein are subjected to multiple regression analysis. As a result, the correlation information is stored. Based on the converted distribution data and the stored correlation information, C-reactive protein measurement data is estimated. By converting the acquired white blood cell distribution data into distribution data for each of a plurality of small regions, for example, when an inflammatory reaction occurs in a living body due to a disease, the ratio of young white blood cells increases, Multiple regression analysis can be performed by selecting areas with large changes in distribution data such as so-called left shift where the ratio of few cells, rod-shaped nuclei, etc. increases, and correlation information is calculated efficiently be able to.

In the fourth, tenth, and sixteenth inventions, a plurality of small region distribution data xki (i = 1, 2, n) converted from leukocyte distribution data Xk (k = 1, 2,..., N). · · ·, s), and calculated based on the measurement data Ck of the obtained C-reactive protein, the distribution data xki measurement data Ck and a plurality of small regions of the C-reactive protein (i = 1, 2, · .., S) and the multiple regression function h are stored. Based on the distribution data xi for each small region converted from the acquired white blood cell distribution data X and the multiple regression function h, estimated measurement data C for C-reactive protein is calculated by Equation (7).
C = h (x1, x2,..., Xi,..., Xs) (7)

Thereby, the measurement data of C-reactive protein can be accurately estimated based on the distribution data of the small region.

In the fifth, eleventh and seventeenth inventions, a plurality of small region distribution data xki (i = 1,2, n) converted from leukocyte distribution data Xk (k = 1, 2,..., N). , S), and multiple regression coefficients c0 and ci calculated by executing linear multiple regression analysis based on the obtained measurement data Ck of C-reactive protein . Based on the distribution data xi for each small region converted from the acquired white blood cell distribution data X and the multiple regression coefficients c0 and ci, the estimated measurement data C of C-reactive protein is calculated by the equation (8).

By executing the multiple regression analysis on the assumption that the relationship between the acquired white blood cell distribution data and the measurement data of the C-reactive protein is linear, the measurement data of the C-reactive protein is obtained based on the distribution data of the small region. It can be estimated with high accuracy.

Sixth aspect, in the twelfth aspect and eighteenth invention, the measurement data of C-reactive protein was estimated boss is preferably a measurement data after predetermined days elapse of C-reactive protein.

According to the above configuration, the correlation information between the C-reactive protein measurement data that needs to be measured by another device or the like and the leukocyte distribution data acquired by the blood cell analyzer is calculated and stored. Thus, measurement data of C-reactive protein can be estimated based on the stored correlation information with respect to unknown leukocyte distribution data. Therefore, the measurement data of C-reactive protein , which is not the measurement target, can be accurately estimated from the results of the conventional blood cell analyzer without the need for a separate device or reagent for acquiring the measurement data of C-reactive protein. It is possible to detect the occurrence of a disease at an early stage with an inexpensive blood cell analyzer with a simple configuration.

  Hereinafter, in the present embodiment, a blood cell analyzer that executes blood cell classification processing will be described as an example, and will be specifically described based on the drawings.

  FIG. 1 is a perspective view schematically showing a configuration of a blood cell analyzer according to an embodiment of the present invention. As shown in FIG. 1, the blood cell analyzer according to the present embodiment includes a measuring device 1 and a calculation display device 2 connected so as to be able to perform data communication with the measuring device 1.

  The measuring device 1 and the calculation display device 2 are connected via a communication line (not shown), and control the operation of the measuring device 1 by data communication with each other, and the measurement data output from the measuring device 1 is transmitted. Process to get analysis results. The measuring device 1 and the calculation display device 2 may be connected via a network, or may be integrated as one device to exchange data by interprocess communication or the like.

  The measuring device 1 detects characteristic information such as white blood cells and reticulocytes in the blood using a flow cytometry method, and transmits the detection result as measurement data to the calculation display device 2. Here, the flow cytometry method is a method of forming a sample flow including a measurement sample and irradiating the sample flow with a laser beam to forward scattered light and side scattered light emitted from particles (blood cells) in the measurement sample. In this method, light such as side fluorescence is detected, whereby particles (blood cells) in the measurement sample are detected.

  FIG. 2 is a block diagram showing the configuration of the measuring apparatus 1 of the blood cell analyzer according to the embodiment of the present invention. The measuring apparatus 1 includes an apparatus mechanism unit 4, a detection unit 5 that performs measurement of a measurement sample, an analog processing unit 6 for the output of the detection unit 5, a display / operation unit 7, and the operations of the above-described hardware units. And a control board unit 9 to be controlled.

  The control board unit 9 includes a control unit 91 having a control processor and a memory for operating the control processor, and a 12-bit A / D conversion unit 92 that converts a signal output from the analog processing unit 6 into a digital signal. And an arithmetic unit 93 that stores the digital signal output from the A / D conversion unit 92 and executes a process of selecting data to be output to the control unit 91. The control unit 91 is connected to the display / operation unit 7 via the bus 94a and the interface 95b, and is connected to the arithmetic display device 2 via the bus 94b and the interface 95c. The calculation unit 93 outputs the calculation result to the control unit 91 via the interface 95d and the bus 94a. Further, the control unit 91 transmits the calculation result (measurement data) to the calculation display device 2.

  The device mechanism unit 4 is provided with a sample preparation unit 41 for preparing a measurement sample from the reagent and blood. The sample preparation unit 41 prepares a white blood cell measurement sample, a reticulocyte measurement sample, and a platelet measurement sample.

  FIG. 3 is a block diagram schematically illustrating the configuration of the sample preparation unit 41 according to the embodiment of the present invention. The sample preparation unit 41 includes a blood collection tube 41a filled with a predetermined amount of blood, a sampling valve 41b for sucking blood, and a reaction chamber 41c.

  The sampling valve 41b is configured to be able to quantify blood in the blood collection tube 41a sucked by a suction pipette (not shown). The reaction chamber 41c is connected to the sampling valve 41b, and is configured so that a predetermined reagent and a staining solution can be further mixed with blood quantified by the sampling valve 41b. The reaction chamber 41c is connected to the detection unit 5, and is configured to flow into the detection unit 5 a measurement sample in which a predetermined reagent and a staining solution are mixed in the reaction chamber 41c.

  Thereby, the sample preparation unit 41 can prepare a measurement sample in which white blood cells are stained and red blood cells are hemolyzed as a white blood cell measurement sample. In addition, a measurement sample in which reticulocytes are stained can be prepared as a sample for reticulocyte measurement, and a measurement sample in which platelets are stained can be prepared as a sample for platelet measurement. The prepared measurement sample is supplied to a sheath flow cell of the detection unit 5 described later together with the sheath liquid.

  FIG. 4 is a block diagram schematically illustrating the configuration of the detection unit 5 and the analog processing unit 6 according to the embodiment of the present invention. As shown in FIG. 4, the detection unit 5 includes a light emitting unit 501 that emits laser light, an irradiation lens unit 502, a sheath flow cell 503 that is irradiated with laser light, and a laser beam that is emitted from the light emitting unit 501. A condensing lens 504, a pinhole 505, and a PD (photodiode) 506 disposed on an extension line in the direction (a beam stopper (not shown) is disposed between the sheath flow cell 503 and the condensing lens 504). , A condensing lens 507, a dichroic mirror 508, an optical filter 509, a pin hole 510, an APD (avalanche photodiode) 511, and a dichroic mirror, which are arranged in a direction crossing the direction in which the laser light emitted from the light emitting unit 501 travels. PD (photodiode) 512 arranged on the side of 508 Eteiru.

  The light emitting unit 501 is provided to emit light to a sample flow including a measurement sample that passes through the inside of the sheath flow cell 503. The irradiation lens unit 502 is provided to irradiate the sample stream with the light emitted from the light emitting unit 501. The PD 506 is provided to receive forward scattered light emitted from the sheath flow cell 503. Information regarding the size of particles (blood cells) in the measurement sample can be obtained from the forward scattered light emitted from the sheath flow cell 503.

  The dichroic mirror 508 is provided to separate the side scattered light and the side fluorescence emitted from the sheath flow cell 503. Specifically, the dichroic mirror 508 is provided to cause the side scattered light emitted from the sheath flow cell 503 to enter the PD 512 and to cause the side fluorescence emitted from the sheath flow cell 503 to enter the APD 511. The PD 512 is provided to receive side scattered light. Internal information such as the size of the nuclei of particles (blood cells) in the measurement sample can be obtained from the side scattered light emitted from the sheath flow cell 503.

  The APD 511 is provided for receiving side fluorescence. When a fluorescent material such as a stained blood cell is irradiated with light, light having a wavelength longer than the wavelength of the irradiated light is emitted. The side fluorescence intensity increases as the degree of staining increases. Therefore, characteristic information regarding the degree of staining of blood cells can be obtained by measuring the side fluorescence intensity emitted from the sheath flow cell 503. Therefore, the classification of white blood cells and other measurements can be performed based on the difference in lateral fluorescence intensity. The PDs 506 and 512 and the APD 511 convert the received optical signals into electric signals, amplify them by the amplifiers 61, 62 and 63, and transmit them to the control board unit 9.

  FIG. 5 is a block diagram showing a configuration of the calculation display device 2 of the blood cell analyzer according to the embodiment of the present invention. The calculation display device 2 has a function of acquiring correlation information regarding the correlation between the distribution data of white blood cells and the measurement data of the inflammation-related marker, in addition to the function of the calculation display device constituting the conventional blood cell analyzer. As shown in FIG. 5, the arithmetic display device 2 includes a CPU (Central Processing Unit) 21, a RAM 22, a storage device 23, an input device 24, a display device 25, an output device 26, a communication interface 27, a portable disk drive 28, and The internal bus 29 is connected to the hardware described above. The CPU 21 is connected to each hardware unit as described above of the arithmetic display device 2 via the internal bus 29, and executes various software functions according to the computer program 100 stored in the storage device 23. The RAM 22 is composed of a volatile memory such as SRAM or SDRAM, and a load module is expanded when the computer program 100 is executed, and stores temporary data generated when the computer program 100 is executed.

  The storage device 23 includes a built-in fixed storage device (hard disk), a volatile memory such as SRAM, and a nonvolatile memory such as ROM. The computer program 100 stored in the storage device 23 is downloaded by a portable disk drive 28 from a portable recording medium 90 such as a DVD or CD-ROM in which information such as programs and data is recorded. To the RAM 22 for execution. Of course, a computer program downloaded from an external computer connected to the network via the communication interface 27 may be used.

  The storage device 23 is configured by a built-in fixed storage device (hard disk) or the like. The storage device 23 is a distribution data storage unit 231 that stores white blood cell distribution data acquired by the measurement device 1, an inflammation-related marker measurement data storage unit 232 that stores measurement data of inflammation-related markers acquired by another testing device 3, And a correlation information storage unit 233 that stores correlation information regarding the correlation. The CPU 21 analyzes the white blood cell distribution data and the inflammation related marker measurement data stored in the distribution data storage unit 231 and the inflammation related marker measurement data storage unit 232, respectively, thereby measuring the inflammation related marker from the white blood cell distribution data. Correlation information for estimating data is calculated. The distribution data storage unit 231, the inflammation-related marker measurement data storage unit 232, and the correlation information storage unit 233 are not limited to be provided in the storage device 23, but are stored in an external computer and stored in a communication interface. 27 may be used to make an inquiry. Further, the correlation information is calculated by an external computer having the same configuration as the calculation display device 2, and the calculated correlation information is stored in the correlation information storage unit 233 of the storage device 23 in advance. Also good. In this case, it is not necessary to store the distribution data and measurement data necessary for calculating the correlation information in the storage device 23 of the calculation display device 2, and the calculation process of the correlation information by the CPU 21 can be omitted. .

  The communication interface 27 is connected to the internal bus 29, and is capable of transmitting and receiving data by being connected to the measuring apparatus 1 via a communication line. That is, instruction information indicating the start of measurement is transmitted to the measurement apparatus 1 and measurement data is received.

  The input device 24 is a data input medium such as a keyboard and a mouse. The display device 25 is a display device such as a CRT monitor or LCD, and displays the analysis result graphically. The output device 26 is a printing device such as a laser printer or an inkjet printer.

  With the measurement device 1 and the calculation display device 2 of the blood cell analyzer having the above-described configuration, the patient's blood is measured, and the white blood cells contained in the blood are converted into lymphocytes, monocytes, eosinophils, neutrophils, When the basophils are classified, a scattergram as shown in FIG. 6 is created and displayed on the display device 25. FIG. 6 is an exemplary diagram of a scattergram at the time of white blood cell classification measurement (DIFF measurement). In FIG. 6, the vertical axis represents the side fluorescence intensity, and the horizontal axis represents the side scattered light intensity. Hereinafter, a method for classifying white blood cells used in the blood cell analyzer according to the present embodiment will be described.

  In the blood cell analyzer according to the embodiment of the present invention, a scattergram as shown in FIG. 6 can be obtained. The scattergram includes a lymphocyte distribution region 101 in which lymphocytes classified by the clustering technique are distributed, a monocyte distribution region 102 in which monocytes are distributed, an eosinophil distribution region 103 in which eosinophils are distributed, and neutrophils. A distributed neutrophil distribution area 104 and a basophil distribution area 105 in which basophils are distributed are displayed. Then, by counting the classified blood cells, the number of lymphocytes, monocytes and the like can be obtained. The above-described classification and counting of white blood cells were performed by the method described in US Pat. No. 5,555,196. Note that a computer program for executing the above-described white blood cell classification method and data used for the execution of the computer program are stored in the storage device 23 in advance.

  By performing multiple regression analysis to extract the relationship between various diseases and the particle size pattern of leukocyte distribution data, the present inventors have a high correlation between leukocyte distribution data and inflammation-related marker measurement data. Found to show. Therefore, by obtaining in advance a correlation between the leukocyte distribution data acquired by a conventional blood cell analyzer and the measurement data of the inflammation-related marker acquired by another test device, the leukocyte distribution data can be used for inflammation. Measurement data of related markers can be estimated.

  First, a procedure for obtaining a correlation between white blood cell distribution data and inflammation-related marker measurement data will be described. Hereinafter, although the case where the correlation information regarding the correlation is calculated by the calculation display device 2 according to the embodiment will be described, the calculation is not limited to the calculation of the correlation information by the calculation display device 2, and external After being calculated by the computer, it may be acquired via a network or a portable recording medium.

  FIG. 7 is a flowchart showing a procedure of processing for calculating correlation information regarding the correlation of the CPU 21 of the calculation display device 2 according to the embodiment of the present invention. The CPU 21 of the calculation display device 2 reads the white blood cell distribution data stored in the distribution data storage unit 231 of the storage device 23 (step S701).

  The CPU 21 reads the measurement data of the inflammation-related marker acquired by another method, for example, another test apparatus and stored in the inflammation-related marker measurement data storage unit 232 of the storage device 23 (step S702), and the read leukocyte Correlation information about the correlation between the distribution data and the measurement data of the inflammation-related marker is calculated.

  Specifically, the read distribution data of leukocytes is converted into distribution data for each of a plurality of small regions (step S703), and based on the distribution data for each converted small region and the read measurement data of the inflammation-related marker. Regression analysis is performed (step S704), and correlation information regarding the correlation is calculated (step S705). FIG. 8 is an explanatory diagram of a conversion method to distribution data for each small region based on a scattergram displaying white blood cell distribution data.

  In the example of FIG. 8, the scattergram is expressed as a data group in which the frequency F (i, j) is assigned to each address (i, j) of n × n (n = 256). When there are too many types of data to handle, it is preferable to compress the scattergram data. For example, when the address of the scattergram is converted so that it becomes one address (small area) every four addresses, that is, when the scattergram is compressed to n / 2 × n / 2, the frequency of each address of the compressed scattergram G (i, j) can be expressed as shown in Equation (9).

  For example, when an inflammatory reaction occurs in a living body due to a disease, a so-called left shift occurs in which the ratio of young leukocytes increases and the number of segmental nuclei, rod-shaped nuclei and the like having a small number of segmental nuclei increases. In this way, by converting the original distribution data into distribution data for each small area consisting of four addresses, the number of data can be compressed, and correlation information can be calculated efficiently.

When the multiple regression analysis is performed by compressing 256 × 256 distribution data into 32 × 32 distribution data, the CPU 21 converts a plurality of white blood cell distribution data Xk (k = 1, 2,..., N). The distribution data xki (i = 1, 2,..., S) (s = 1024) of the small area of the measurement area is used as an explanatory variable, and the measurement data Ck of the acquired inflammation-related marker is measured data of the inflammation-related marker. A multiple regression function h between Ck and a plurality of small area distribution data xki (i = 1, 2,..., S) is calculated. When the CPU 21 newly obtains distribution data xi for each small region converted from the white blood cell distribution data X by calculating the multiple regression function h as the correlation information, the CPU 21 calculates the estimated measurement data C of the inflammation-related marker. Can be calculated by equation (10).
C = h (x1, x2,..., Xi,..., Xs) (10)

  Of course, a plurality of small region distribution data xki (i = 1, 2,..., S) converted from the white blood cell distribution data Xk (k = 1, 2,..., N) and the acquired inflammation. The linear multiple regression analysis may be executed on the assumption that a linear correlation exists with the measurement data Ck of the related marker. In this case, the CPU 21 uses a plurality of small region distribution data xki (i = 1, 2,..., S) converted from the white blood cell distribution data Xk (k = 1, 2,..., N). A linear multiple regression analysis is executed using the acquired measurement data Ck of the inflammation-related marker as an objective variable as an explanatory variable, and multiple regression coefficients c0 and ci are calculated as correlation information. When the CPU 21 obtains distribution data xi for each small region newly converted from the white blood cell distribution data X by calculating the multiple regression coefficients c0 and ci, the CPU 21 obtains the estimated measurement data C of the inflammation-related marker. It can be calculated by equation (11).

  The CPU 21 stores the calculated correlation information in the correlation information storage unit 233 of the storage device 23 (step S706).

  The acquired white blood cell distribution data may be classified into a plurality of categories, and correlation information regarding the correlation between the distribution data and the measurement data of the inflammation-related marker may be stored for each classified category. By calculating and storing correlation information based on a category showing high correlation between distribution data and measurement data of inflammation-related markers, measurement data of inflammation-related markers can be estimated more accurately. Because.

  FIG. 9 is a flowchart showing a processing procedure of the control unit 91 of the control board unit 9 and the CPU 21 of the calculation display device 2 of the measurement apparatus 1 according to the embodiment of the present invention. When the control unit 91 of the control board unit 9 of the measuring apparatus 1 detects that the measuring apparatus 1 is activated, the control unit 91 performs initialization (step S912), and checks the operation of each part of the measuring apparatus 1. In addition, when the CPU 21 of the calculation display device 2 detects that the calculation display device 2 is activated, the CPU 21 executes initialization (initialization of the program) (step S901) and displays a menu screen on the display device 25 (step S901). Step S902). On the menu screen, it is possible to accept selection of DIFF measurement, RET measurement, and CBC measurement, accept a measurement start instruction, and a shutdown instruction. In the present embodiment, the case where DIFF measurement is selected on the menu screen will be described below.

  The CPU 21 of the calculation display device 2 determines whether or not a measurement start instruction has been received (step S903). If the CPU 21 determines that a measurement start instruction has not been received (step S903: NO), the CPU 21 performs step S904. Thru | or step S909 is skipped. When the CPU 21 determines that the measurement start instruction has been received (step S903: YES), the CPU 21 transmits instruction information indicating the measurement start to the measurement apparatus 1 (step S904). The control unit 91 of the control board unit 9 of the measurement apparatus 1 determines whether or not the instruction information indicating the start of measurement has been received (step S913), and determines that the control unit 91 has received the instruction information indicating the start of measurement. In the case (step S913: YES), the control unit 91 causes a barcode reader (not shown) to read a barcode label (not shown) affixed to the container containing the blood, thereby identifying the blood. Information (sample ID) is acquired (step S914). When the control unit 91 determines that the instruction information indicating the start of measurement has not been received (step S913: NO), the control unit 91 skips steps S914 to S918.

  The control unit 91 transmits the acquired identification information (sample ID) to the calculation display device 2 (step S915), and the CPU 21 of the calculation display device 2 determines whether the identification information (sample ID) has been received (step S915). Step S905). When the CPU 21 determines that the identification information (sample ID) has not been received (step S905: NO), the CPU 21 enters a reception waiting state. When the CPU 21 determines that the identification information (sample ID) has been received (step S905: YES), the CPU 21 inquires the storage device 23 to acquire the stored patient information (step S906), and stores the patient information. It transmits to the measuring apparatus 1 (step S907). Thereby, it is possible to specify who the blood is the sample to be analyzed.

  Next, the control unit 91 of the control board unit 9 of the measuring apparatus 1 determines whether or not patient information has been received (step S916), and when the control unit 91 determines that it has not been received (step S916: NO), the control unit 91 enters a reception waiting state. When the control unit 91 determines that it has been received (step S916: YES), the control unit 91 controls the sample preparation unit 41 to prepare the measurement sample, and then starts the measurement sample measurement process (step S917). . Specifically, DIFF measurement is performed, and electrical signals corresponding to the received light intensity of side scattered light and side fluorescence are output to the control board unit 9 via the detection unit 5 and the analog processing unit 6. The A / D conversion unit 92 of the control board unit 9 converts the acquired analog signal into, for example, a 12-bit digital signal, and the calculation unit 93 performs predetermined processing on the digital signal output from the A / D conversion unit 92. Applied to the control unit 91. The control unit 91 transmits the received integer string information as measurement data to the calculation display device 2 (step S918).

  The CPU 21 determines whether or not measurement data has been received (step S908). If the CPU 21 determines that the measurement data has been received (step S908: YES), the CPU 21 performs an analysis process based on the received measurement data. Is executed (step S909). When the CPU 21 determines that the measurement data has not been received (step S908: NO), the CPU 21 enters a reception waiting state.

  FIG. 10 is a flowchart showing the analysis processing procedure executed in step S909 of FIG. 9 by the CPU 21 of the calculation display device 2 according to the embodiment of the present invention. 10, the CPU 21 of the calculation display device 2 creates distribution data based on the received measurement data (step S1001), and stores the distribution data in association with patient information in the distribution data storage unit 231 of the storage device 23. (Step S1002). The CPU 21 creates a scattergram as shown in FIG. 6 and displays it on the display device 25 as the white blood cell classification result (step S1003).

  The CPU 21 reads out the distribution data stored in the distribution data storage unit 231 using the patient information as key information and the correlation information stored in the correlation information storage unit 233 (step S1004), and obtains measurement data of the inflammation-related marker. It estimates and memorize | stores in the inflammation related marker measurement data storage part 232 of the memory | storage device 23 (step S1005). The CPU 21 determines whether or not an instruction to display the measurement data of the estimated inflammation-related marker has been received (step S1006).

  When the CPU 21 determines that the display instruction has not been received (step S1006: NO), the CPU 21 returns the process to step S910 in FIG. If the CPU 21 determines that a display instruction has been received (step S1006: YES), the CPU 21 reads measurement data of an inflammation-related marker (for example, CRP) from the inflammation-related marker measurement data storage unit 232 (step S1007), and the display device 25. (Step S1008), and the process returns to Step S910 of FIG.

  Returning to FIG. 9, the CPU 21 of the calculation display device 2 determines whether or not a shutdown instruction has been received (step S <b> 910), and when the CPU 21 determines that a shutdown instruction has not been received (step S <b> 910: NO), the CPU 21. Returns the processing to step S903 and repeats the above-described processing. When the CPU 21 determines that a shutdown instruction has been received (step S910: YES), the CPU 21 transmits shutdown instruction information to the measurement apparatus 1 (step S911).

  The control unit 91 of the control board unit 9 of the measuring apparatus 1 determines whether or not the shutdown instruction information has been received (step S919), and the control unit 91 determines that the shutdown instruction information has not been received. (Step S919: NO), control unit 91 returns the process to step S913, and repeats the above-described process. When the control unit 91 determines that the shutdown instruction information has been received (step S919: YES), the control unit 91 executes the shutdown (step S920) and ends the process.

  In addition, when the correlation information regarding the correlation between the distribution data and the measurement data of the inflammation-related marker is stored for each classified white blood cell distribution data, the distribution data is classified into a plurality of categories in the blood cell analyzer. It is necessary to perform processing. Thereby, the measurement data of the inflammation-related marker can be estimated using the correlation information corresponding to the classified category. As a method for classifying distribution data, a method such as a self-organizing map can be used.

  FIG. 11 is an exemplary diagram of a white blood cell distribution map using C-reactive protein (CRP) as an inflammation-related marker and classified into 5 × 5 categories using a self-organizing map. In the example of FIG. 11, class 0, class 1, class 2, class 3, and class 4 are set from left to right on the top level, and classes 5, class 6, and so on are moved down one step from left to right on the next level. Are classified into class 20, class 21, class 22, class 23, class 24 from left to right at the bottom. For example, class 8 is considered to be a group containing many normal samples and samples having a relatively low degree of inflammation, and class 6 is considered to be a group containing many samples having a high degree of inflammation.

  12 and 13 are graphs showing correlations between measured values and estimated values of CRP in class 8 and class 6, respectively. 12 and 13, the vertical axis represents the estimated value in the blood cell analyzer according to the present embodiment, and the horizontal axis represents the actual measurement value. The estimated value is the distribution data when one distribution data of 256 channels × 256 channels at the time of DIFF measurement of leukocytes is divided into 32 × 32 small regions of 4 channels × 4 channels. As an explanatory variable, estimation is performed using a multiple regression function obtained by executing a multiple regression analysis using a rod-shaped nucleus as an objective variable. As shown in FIGS. 12 and 13, the correlation coefficients r between the two are 0.683 and 0.708, respectively, indicating a high correlation between the estimated value and the actually measured value, and used as clinical laboratory information. It was confirmed that it was possible.

  Furthermore, by using the correlation obtained by performing multiple regression analysis of white blood cell distribution data at a certain time point and CRP measurement data several days later, CRP measurement several days later from the current white blood cell distribution data It is also possible to estimate the data. FIG. 14 and FIG. 15 are graphs showing correlations between measured values and estimated values after 4 to 7 days of CRP in class 8 and class 6, respectively. In FIG. 14, the vertical axis represents the estimated value in the blood cell analyzer according to the present embodiment, and the horizontal axis represents the actual measurement value. In FIG. 15, the horizontal axis represents the estimated value in the blood cell analyzer according to the present embodiment, and the vertical axis represents the actual measurement value.

  As shown in FIGS. 14 and 15, the correlation coefficients r between the two are 0.933 and 0.665, respectively, suggesting that it is possible to predict the CRP value after several days.

  Further, the feature of the distribution data may be further emphasized by the feature enhancement filter, the emphasized distribution data may be classified into 5 × 5, and each correlation may be obtained. By doing in this way, it is possible to estimate the measurement data of the rod-shaped nuclei or segmented nuclei more accurately. As the feature enhancement filter, for example, a single regression analysis result of distribution data may be used.

  As described above, according to the present embodiment, correlation information between the measurement data of the inflammation-related marker acquired from other than the blood cell analyzer and the white blood cell distribution data acquired by the blood cell analyzer is calculated and stored. Thus, the measurement data of the inflammation-related marker can be estimated based on the stored correlation information with respect to the unknown white blood cell distribution data. Therefore, it is possible to accurately estimate the measurement data of the inflammation-related marker that is not the measurement target only from the result of the conventional blood cell analyzer, without requiring another device or reagent for acquiring the measurement data of the inflammation-related marker. It is possible to detect the occurrence of a disease at an early stage with an inexpensive blood cell analyzer with a simple configuration.

  In the above-described embodiment, the analysis result is displayed on the display device 25 of the calculation display device 2. However, the present invention is not limited to this, and there is another computer connected via a network. It may be displayed on a display device.

  Further, the present invention is not limited to the above-described embodiments, and it goes without saying that various modifications, substitutions and the like are possible within the scope of the gist of the present invention. For example, the inflammation-related marker may be any one of C-reactive protein, serum amyloid A, and erythrocyte sedimentation rate. When the inflammation-related marker is C-reactive protein, the measurement data of the estimated inflammation-related marker is It may be measurement data of a C-reactive protein after a predetermined number of days.

It is a perspective view which shows typically the structure of the blood cell analyzer which concerns on embodiment of this invention. It is a block diagram which shows the structure of the measuring apparatus of the blood cell analyzer which concerns on embodiment of this invention. It is a block diagram which illustrates typically the composition of the sample preparation part concerning an embodiment of the invention. It is a block diagram which illustrates typically composition of a detecting part and an analog processing part concerning an embodiment of the invention. It is a block diagram which shows the structure of the calculation display apparatus of the blood cell analyzer which concerns on embodiment of this invention. It is an illustration figure of the scattergram at the time of leukocyte classification measurement (DIFF measurement). It is a flowchart which shows the procedure of the process which calculates the correlation information regarding the correlation of CPU of the calculation display apparatus which concerns on embodiment of this invention. It is explanatory drawing of the conversion method to the distribution data for every small area based on the scattergram which displays white blood cell distribution data. It is a flowchart which shows the process procedure of CPU of the control board part of the measuring apparatus which concerns on embodiment of this invention, and a calculation display apparatus. It is a flowchart which shows the analysis processing procedure of CPU of the calculation display apparatus which concerns on embodiment of this invention. It is an illustration figure of the white blood cell distribution map which employ | adopted C-reactive protein (CRP) as an inflammation related marker, and was classified into 5x5 using the self-organization map. It is a graph which shows the correlation with the measured value and estimated value of CRP in class 8. It is a graph which shows the correlation with the measured value and estimated value of CRP in class 6. It is a graph which shows the correlation with the actual value and the estimated value after 4 to 7 days of CRP in class 8. It is a graph which shows the correlation with the actual value and the estimated value after 4 to 7 days of CRP in class 6.

Explanation of symbols

DESCRIPTION OF SYMBOLS 1 Measuring apparatus 2 Computation display apparatus 4 Apparatus mechanism part 5 Detection part 6 Analog processing part 9 Control board part 21 CPU
22 RAM
DESCRIPTION OF SYMBOLS 23 Memory | storage device 24 Input device 25 Display apparatus 26 Output device 27 Communication interface 28 Portable disk drive 29 Internal bus 90 Portable recording medium 91 Control part 92 A / D conversion part 93 Calculation part 100 Computer program 231 Distribution data storage part 232 Inflammation Related marker measurement data storage unit 233 Correlation information storage unit

Claims (18)

In a blood cell analyzer having a detection unit for detecting a predetermined component of white blood cells contained in a plurality of specimens and acquiring white blood cell distribution data based on a detection result of the detection unit,
Correlation information storage means for storing correlation information regarding the correlation between the distribution data and the measurement data of C-reactive protein ;
C-reactive protein estimation means for estimating measurement data of C-reactive protein based on the acquired white blood cell distribution data and stored correlation information;
A blood cell analyzer comprising: output means for outputting measurement data of the estimated C-reactive protein . A category classification means for classifying the acquired white blood cell distribution data into a plurality of categories;
2. The correlation information storage means stores correlation information relating to a correlation between the distribution data included in each classified category and measurement data of C-reactive protein. blood cell analysis apparatus according to. Conversion means for converting the acquired white blood cell distribution data into distribution data for each of a plurality of small regions,
The correlation information storage means stores the correlation information as a result of multiple regression analysis of the converted distribution data and measurement data of C-reactive protein ,
The C-reactive protein estimation means, based on the converted distribution data and the stored correlation information are, according to claim 2, characterized in that you have to estimate the measurement data of C-reactive protein Blood cell analyzer. The correlation information storage means includes
A plurality of small region distribution data xki (i = 1, 2,..., S) converted from the white blood cell distribution data Xk (k = 1, 2,..., N), and the acquired C reactivity. The multiple regression function h of the measurement data Ck of C-reactive protein and the distribution data xki (i = 1, 2,..., S) of a plurality of small regions calculated based on the measurement data Ck of the protein Stored as correlation information,
The C-reactive protein estimation means includes:
Based on the distribution data xi for each small region converted from the acquired white blood cell distribution data X and the multiple regression function h, the estimated measurement data C of C-reactive protein is calculated by equation (1). blood cell analyzer according to claim 3, characterized in.
C = h (x1, x2,..., Xi,..., Xs) Expression (1) The correlation information storage means includes
A plurality of small region distribution data xki (i = 1, 2,..., S) converted from the white blood cell distribution data Xk (k = 1, 2,..., N), and the acquired C reactivity. Multiple regression coefficients c0 and ci calculated by executing linear multiple regression analysis based on protein measurement data Ck are stored as the correlation information;
The C-reactive protein estimation means includes:
Based on the distribution data xi for each small region converted from the acquired white blood cell distribution data X and the multiple regression coefficients c0 and ci, the estimated measurement data C for C-reactive protein is calculated by the equation (2). The blood cell analyzer according to claim 3 , wherein the blood cell analyzer is provided.

The blood cell analyzer according to any one of claims 1 to 5, wherein the estimated measurement data of C-reactive protein is measurement data after a predetermined number of days have elapsed for C-reactive protein . In a blood cell analysis method that can be executed by a blood cell analyzer that detects predetermined components of white blood cells contained in a plurality of specimens and acquires white blood cell distribution data based on detection results,
Storing correlation information relating to the correlation between the distribution data and the measurement data of C-reactive protein;
Based on the acquired white blood cell distribution data and the stored correlation information, the measurement data of C-reactive protein is estimated,
Blood cell analyzing how to and outputting measurement data of the estimated C-reactive protein. The acquired white blood cell distribution data is classified into multiple categories,
8. The blood cell analysis method according to claim 7, wherein correlation information relating to a correlation between the distribution data included in each classified category and measurement data of C-reactive protein is stored . Convert the acquired white blood cell distribution data into distribution data for each of multiple small areas,
Storing the correlation information as a result of multiple regression analysis of the converted distribution data and C-reactive protein measurement data ;
9. The blood cell analysis method according to claim 8 , wherein measurement data of C-reactive protein is estimated based on the converted distribution data and stored correlation information . A plurality of small region distribution data xki (i = 1, 2,..., S) converted from the white blood cell distribution data Xk (k = 1, 2,..., N), and the acquired C reactivity. The multiple regression function h of the C-reactive protein measurement data Ck calculated from the protein measurement data Ck and the distribution data xki (i = 1, 2,..., S) of a plurality of small regions Memorize it as correlation information,
On the basis of the distribution data xi for each small region converted from the acquired white blood cell distribution data X and the multiple regression function h, estimated measurement data C of C-reactive protein is calculated by Equation (3). The blood cell analysis method according to claim 9.
C = h (x1, x2,..., Xi,..., Xs) (3) A plurality of small region distribution data xki (i = 1, 2,..., S) converted from the white blood cell distribution data Xk (k = 1, 2,..., N), and the acquired C reactivity. Storing multiple regression coefficients c0 and ci calculated by executing linear multiple regression analysis based on protein measurement data Ck as the correlation information;
Based on the distribution data xi for each small region converted from the acquired white blood cell distribution data X and the multiple regression coefficients c0 and ci , estimated measurement data C for C-reactive protein is calculated by the equation ( 4 ). The blood cell analysis method according to claim 9 .

12. The blood cell analysis method according to any one of claims 7 to 11, wherein the estimated measurement data of C-reactive protein is measurement data after a predetermined number of days have elapsed for C-reactive protein . In a computer program that has a detection unit that detects a predetermined component of white blood cells contained in a plurality of specimens and that can be executed by a blood cell analyzer that acquires white blood cell distribution data based on the detection result of the detection unit,
The blood cell analyzer,
Correlation information storage means for storing correlation information regarding the correlation between the distribution data and the measurement data of C-reactive protein,
C-reactive protein estimation means for estimating C-reactive protein measurement data based on the acquired white blood cell distribution data and stored correlation information; and
Output means for outputting estimated C-reactive protein measurement data
Computer program that is characterized in that to function as a. The blood cell analyzer,
Function as a category classification means to classify the acquired white blood cell distribution data into multiple categories,
Claim 13, characterized in that to function as means for storing correlation information about the correlation between the measurement data of the distribution data and the C-reactive protein contained the correlation information storage means, for each classified category computer program according to. The blood cell analyzer ,
Function as a conversion means for converting the acquired white blood cell distribution data into distribution data for each of a plurality of small regions,
The correlation information storage means functions as means for storing the correlation information as a result of multiple regression analysis of the converted distribution data and C-reactive protein measurement data ,
Claim 14, wherein the C-reactive protein estimation means, based on the converted distribution data and the stored correlation information are to function as a means you estimate measurement data C-reactive protein A computer program described in 1 . The correlation information storage means
A plurality of small region distribution data xki (i = 1, 2,..., S) converted from the white blood cell distribution data Xk (k = 1, 2,..., N), and the acquired C reactivity. The multiple regression function h of the C-reactive protein measurement data Ck calculated from the protein measurement data Ck and the distribution data xki (i = 1, 2,..., S) of a plurality of small regions Function as a means of storing as correlation information ,
The C-reactive protein estimation means,
Based on the distribution data xi for each small region converted from the acquired white blood cell distribution data X and the multiple regression function h, the C-reactive protein estimated measurement data C is caused to function as a means for calculating by the equation (5). computer program according to claim 15, wherein.
C = h (x1, x2,..., Xi,..., Xs) (5) The correlation information storage means
A plurality of small region distribution data xki (i = 1, 2,..., S) converted from the white blood cell distribution data Xk (k = 1, 2,..., N), and the acquired C reactivity. Functioning as means for storing multiple regression coefficients c0 and ci calculated by executing linear multiple regression analysis based on protein measurement data Ck as the correlation information;
The C-reactive protein estimation means,
Functions as means for calculating estimated measurement data C of C-reactive protein by equation (6) based on the distribution data xi for each small region converted from the acquired white blood cell distribution data X and the multiple regression coefficients c0 and ci The computer program according to claim 15, wherein:

Measurement data of the estimated C-reactive protein, a computer program according to any one of claims 13 to 17, characterized in that the measurement data after the predetermined number of days elapsed in C-reactive protein.

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