APTT Extension Factor Estimation System

Abstract

This invention provides an APTT prolongation factor estimation system. The APTT prolongation factor estimation system includes: one or more facilities, each having an analytical device for measuring the coagulation reaction of a blood sample; a database storing data related to the coagulation reaction of the blood sample and data on APTT prolongation factors; and a computer for estimating the APTT prolongation factor of the blood sample based on the data related to the coagulation reaction from the analytical device and the data stored in the database. The facility further includes: a data transmission unit for transmitting the data related to the coagulation reaction of the blood sample obtained from the analytical device to the computer; and a data receiving unit for receiving the estimation results of the APTT prolongation factor of the blood sample estimated by the computer as measured by each analytical device.

Description

Technical Field

[0001] This invention relates to an APTT extension factor estimation system. Background Technology

[0002] Coagulation tests are used to diagnose a patient's coagulation function by adding prescribed reagents to a blood sample and measuring clotting time. Coagulation tests can assess a patient's hemostatic and fibrinolytic abilities. Typical examples of clotting times include prothrombin time (PT), activated partial thromboplastin time (APTT), and thrombin time. In recent years, automated analytical devices for automatically measuring coagulation results have been widely used, enabling convenient implementation of these tests.

[0003] Causes of prolonged clotting time include the effects of anticoagulants, a reduction in components involved in clotting, congenital deficiencies of clotting factors, and the presence of autoantibodies that inhibit acquired coagulation. For example, in cases of prolonged APTT, a crossmixing test is generally performed to determine whether the APTT prolongation is due to a clotting factor inhibitor (anticoagulant), lupus anticoagulant (LA), or a deficiency of clotting factors such as hemophilia. In the crossmixing test, the APTT (immediate reaction) is measured after mixing normal plasma, test plasma, and mixed plasma containing test plasma and normal plasma in various volume ratios, and after incubation at 37°C for two hours (delayed reaction). The crossmixing test results are plotted, with the APTT measurement (seconds) on the vertical axis and the volume ratio of test plasma to normal plasma on the horizontal axis. The plots of the immediate and delayed reactions show a "downward convex," "straight," or "upward convex" pattern depending on the APTT prolongation factor. The factors contributing to APTT prolongation are determined based on these patterns of the immediate and delayed reactions.

[0004] In cases where APTT prolongation is determined to be due to coagulation factor inhibitors, the inhibitor titer is generally determined using the Bethesda method. In the Bethesda method, a sample prepared by mixing a diluted series of the tested plasma with normal plasma is incubated at 37°C for two hours. The residual activity of the coagulation factor in the sample is then measured, and the titer of the coagulation factor inhibitor is determined based on the measured value and a calibration curve. The Bethesda method is currently the standard quantitative method for determining the titers of coagulation factor VIII (FVIII) and factor IX (FIX) inhibitors.

[0005] In coagulation tests, a coagulation reaction curve can be derived by measuring the amount of coagulation reaction after adding reagents to a blood sample over time. This coagulation reaction curve has different shapes depending on the type of abnormality in the coagulation system (Non-Patent Document 1). Therefore, a method for determining abnormalities in the coagulation system based on the coagulation reaction curve is disclosed. For example, Patent Documents 1-3 and Non-Patent Documents 2-4 describe methods that evaluate whether a patient's coagulation factors are abnormal based on parameters related to the first and second derivative curves of the coagulation reaction curve concerning the patient's blood, such as maximum coagulation rate, maximum coagulation acceleration, maximum coagulation deceleration, and the time to reach these maximum values. Patent Document 4 describes a method that determines the severity of hemophilia based on the average rate of change of the coagulation rate up to the time until the patient's coagulation reaction reaches the maximum coagulation rate or maximum coagulation acceleration. Patent document 5 describes a method for determining the presence of an FVIII inhibitor based on the ratio of the slope of a straight line representing the dilution ratio of coagulation time relative to the dilution ratio of patient plasma to the slope of a straight line representing the dilution ratio of coagulation time relative to control plasma. Patent document 6 describes a method for extracting multiple parameters from the first and second derivative waveforms of a coagulation waveform, and for calculating the predicted concentration of each coagulation factor using multivariate backward correlation based on these parameters, employing a learned neural network for this prediction.

[0006] A method for centralized management of data from multiple analytical devices and the maintenance of these devices is provided. Patent Document 7 discloses a method for accuracy management of an analytical device, which evaluates whether the accuracy management measurement of the analytical device is normal or abnormal by receiving accuracy management measurement values ​​from the target analytical device and comparing the received measurement values ​​with a statistical benchmark generated based on accuracy management measurement values ​​from multiple analytical devices. Patent Document 8 discloses a method for evaluating biological samples, comprising: transmitting raw data of the biological sample, including numerical values ​​representing physical treatments or chemical reactions of cells performed by the device and cell images, from the device to an analytical facility, and analyzing the raw data, monitoring the integrity of the analysis, and monitoring the operation of the device in the analytical facility.

[0007] Patent Document 1: Japanese Patent Application Publication No. 2016-194426

[0008] Patent Document 2: Japanese Patent Application Publication No. 2016-118442

[0009] Patent Document 3: Japanese Patent Application Publication No. 2017-106925

[0010] Patent Document 4: Japanese Patent Application Publication No. 2018-017619

[0011] Patent Document 5: Japanese Patent Publication No. 2018-517150

[0012] Patent Document 6: U.S. Patent No. 6,524,861

[0013] Patent Document 7: Japanese Patent Application Publication No. 2017-187473

[0014] Patent Document 8: Japanese Patent Publication No. 2016-508610

[0015] Non-patent literature 1: British Journal of Haematology, 1997, 98: 68-73

[0016] Non-Patent Literature 2: Clinical Hematology, 2017, vol. 58, no. 9, p. 1754, PS2-37-7

[0017] Non-patent literature 3: Journal of the Japanese Society for Thrombosis and Haemostasis, 2018, vol.29, no.2, p.184, O-056, P-080

[0018] Non-patent literature 4: Journal of the Japanese Society for Thrombosis and Haemostasis, 2018, vol.29, no.4, pp.413-420 Summary of the Invention

[0019] This invention provides an APTT extension factor estimation system.

[0020] The inventor provides the following content.

[0021] [1] An APTT prolongation factor estimation system, comprising:

[0022] One or more facilities, each equipped with an analytical device for measuring the coagulation reaction of the blood sample being tested;

[0023] The database stores data related to coagulation reactions in blood samples and data on factors that prolong APTT; and

[0024] The computer, based on data related to coagulation reactions from the analytical device and data stored in the database, estimates the factors contributing to the prolongation of APTT in the tested blood sample.

[0025] The facility also includes: a data transmission unit that transmits data related to the coagulation reaction of the tested blood sample obtained by the analysis device to the computer; and a data receiving unit that receives the estimated results of the computer regarding the APTT prolongation factor of the tested blood sample as determined by each analysis device.

[0026] [2] According to the system described in [1], wherein,

[0027] The above facilities also have:

[0028] The analysis device control unit controls the aforementioned analysis device; and / or

[0029] The data display unit displays the data received by the aforementioned data receiving unit.

[0030] [3] According to the system described in [1] or [2], wherein,

[0031] The data related to the coagulation reaction of the blood sample sent to the computer and the estimated results of the APTT prolongation factor of the blood sample obtained by the computer are stored in the database.

[0032] [4] The system according to any one of [1] to [3], wherein,

[0033] It also has an algorithm learning unit that uses data on coagulation reactions of blood samples and data on APTT prolongation factors stored in the aforementioned database to perform machine learning on the APTT prolongation factor estimation algorithm.

[0034] [5] The system according to any one of [1] to [4], wherein,

[0035] The data related to the above coagulation reaction includes one or more parameters relating to the coagulation reaction curve or the first or second derivative of the coagulation reaction curve, and related to the waveform or weighted average point.

[0036] [6] According to the system described in [5], wherein,

[0037] Select parameters related to the waveform from the solidification time, the maximum peak height of the first or second differential curve, and the time to reach the maximum peak height. Select parameters related to the weighted average point from the weighted average time, weighted average height, peak width, flatness, time rate, and peak region of the calculation object region of the first or second differential curve.

[0038] [7] According to the system described in [6], wherein,

[0039] The parameters associated with the aforementioned weighted average point include one or more parameters selected from weighted average time, weighted average height, peak width, flatness, time rate, and peak region for each of the two or more operand regions of the aforementioned first differential curve.

[0040] [8] According to the system described in [7], wherein,

[0041] When the above first derivative curve is set as F(t), and the times when F(t) is a specified value x are set as t1 and t2, the above operation target region is the region where F(t)≥x, t is time, t1 < t2, and the above weighted average time and weighted average height are respectively expressed as vT and vH shown in the following formulas.

[0042] [Formula 1]

[0043]

[0044]

[0045] Here,

[0046]

[0047] The above peak width is expressed as the time length of F(t)≥x from t1 to t2, that is, vB.

[0048] The above flatness ratio is expressed as the ratio vAB of this vH to this vB.

[0049] The above time ratio is expressed as the ratio vTB of this vT to this vB.

[0050] The above peak region is expressed as the area under the curve vAUC.

[0051] 〔9〕According to the system described in 〔8〕, wherein,

[0052] The above specified value x is 5 to 90% of the maximum value of the above F(t).

[0053] 〔10〕According to the system described in any one of 〔5〕 to 〔9〕, wherein,

[0054] By selecting one or more of the above parameters related to the above waveform or weighted average point from the blood specimens stored in the above database that are approximate to the specimen to be tested, and estimating the APTT prolongation factor of the selected specimen as the APTT prolongation factor of the specimen to be tested, the APTT prolongation factor of the above specimen to be tested is estimated.

[0055] 〔11〕According to the system described in 〔10〕, wherein,

[0056] Regression analysis is used to perform the selection of the above specimens. [[ID=4G]]

[0057] 〔12〕According to the system described in 〔10〕, wherein,

[0058] The selection of the above specimens is performed using an APTT prolongation factor estimation algorithm obtained through machine learning.

[0059]

[13] The system according to any one of [1] to

[12] , wherein,

[0060] The data on APTT prolongation factors mentioned above pertain to coagulation factor deficiency, lupus anticoagulant positivity, presence or absence of coagulation factor inhibitors, or coagulation factor concentration.

[0061]

[14] According to the system described in

[13] , wherein,

[0062] The presumption of APTT prolongation factors for the above-mentioned blood samples includes presuming coagulation factor deficiency, lupus anticoagulant positivity, or the presence or absence of coagulation factor inhibitors as APTT prolongation factors for the sample.

[0063]

[15] According to the system described in

[13] , wherein,

[0064] The estimation of APTT prolongation factors in the above-mentioned blood samples includes the estimation of the coagulation factor concentration of the sample.

[0065]

[16] The system according to any one of [1] to

[15] , wherein,

[0066] The aforementioned data transmission unit sends data related to the coagulation reaction of the managed specimens to the aforementioned computer.

[0067] The computer will compare data related to the coagulation reaction of the managed specimen with data related to the coagulation reaction of past managed specimens obtained using reagents from different batches.

[0068] The measurement data from the aforementioned analytical device are corrected based on this comparison result.

[0069] The managed specimen is a blood specimen with known APTT prolongation factors, and the previous managed specimen is the same specimen as the managed specimen or a specimen with the same APTT prolongation factors as the managed specimen.

[0070]

[17] One method is the APTT prolongation factor estimation method, which includes:

[0071] The coagulation reaction of the blood sample was measured in each of the more than one facilities;

[0072] Data related to the coagulation reaction of the blood sample being examined, acquired at each facility, is sent to the computer from each facility.

[0073] The computer estimates the APTT prolongation factor of the tested blood sample based on data related to the coagulation reaction of the blood sample, data on the coagulation reaction of the blood sample stored in the database, and data on APTT prolongation factors; and

[0074] The estimated result of the APTT prolongation factor of the tested blood sample obtained by the computer is sent to the facility that measures the coagulation reaction of the tested blood sample.

[0075]

[18] According to the method described in

[17] , wherein,

[0076] It also includes storing the data related to the coagulation reaction of the blood sample sent to the computer and the estimated results of the APTT prolongation factor of the blood sample obtained by the computer in the database.

[0077]

[19] According to the method described in

[17] or

[18] , wherein,

[0078] It also includes using data on coagulation reactions of blood samples and data on APTT prolongation factors stored in the aforementioned database to perform machine learning on the APTT prolongation factor estimation algorithm.

[0079]

[20] According to the method described in

[19] , wherein,

[0080] The APTT prolongation factor estimation algorithm obtained through the above machine learning was used to implement the estimation of the APTT prolongation factor of the above-mentioned blood sample by the above computer.

[0081] According to the present invention, a database of coagulation reaction curves from various specimens enables highly accurate estimation of APTT prolongation factors based on a large amount of knowledge. According to the present invention, accurate determination of APTT prolongation factors is possible even in medical institutions with limited experience in diagnosing APTT abnormalities. Furthermore, according to the present invention, even in medical institutions, clinics, facilities, etc., where cross-mixing tests and inhibitor titer measurements are not performed, the determination of APTT prolongation factors can be performed solely based on data from coagulation reaction curves. Attached Figure Description

[0082] Figure 1 A: The regression line of the parameter set. Figure 1 B: The first differential curve between the sample and the template. SampleNo.67: The sample; Template A: The template.

[0083] Figure 2 A: The first differential curve of the test subject (Sample no. 57) and the template (#027) (top) and the regression line of the parameter set (bottom). Figure 2 B: The first differential curve of the test subject (Sample no. 57) and the template (#134) (top) and the regression line of the parameter set (bottom). Detailed Implementation

[0084] 1. APTT Extension Factor Estimation System

[0085] As factors prolonging the APTT (Advanced Peripheral Tissue Test), deficiencies in coagulation factor inhibitors (anticoagulation factors), lupus anticoagulants (LA), hemophilia, and other coagulation factor deficiencies can usually be inferred. Previously, the determination of APTT prolongation factors was generally based on cross-mixing tests and inhibitor titer determination using the Bethesda method, relying on APTT results derived from coagulation reaction curves. However, implementing all these diagnostic tests within a single healthcare institution is costly and labor-intensive, making it impractical. Furthermore, ensuring and improving the accuracy of the determination requires the accumulation of extensive knowledge, but patients with abnormal APTT, such as those with coagulation factor deficiencies, occur very infrequently, making it difficult to gather sufficient knowledge within a single healthcare institution. These issues can lead to biases in the institution's test results.

[0086] As disclosed in Patent Documents 1-5, methods for determining coagulation factor anomalies and the presence of coagulation factor inhibitors are based on waveform-related parameters such as peak height, time, or slope of the first or second derivative curve of the coagulation reaction curve. Furthermore, the applicant has provided methods for determining APTT prolongation factors, inhibitor titers, and the presence or absence of coagulation factor inhibitors or the types of inhibitors lacking using parameters related to the waveform or weighted average point of the coagulation reaction curve or its first or second derivative curve (e.g., coagulation time, weighted average time, weighted average height, peak width, flatness, and time rate, and their ratios or differences) (e.g., Japanese Patent Applications 2019-086658, 2019-084727, and 2019-016474). These methods not only determine APTT prolongation factors based on objective indicators calculated from various parameters measured by an analytical device, but also automate the determination process.

[0087] To more accurately determine the APTT prolongation factor based on parameters related to the waveform or weighted average point of the coagulation reaction curve, or its first or second derivative curve, it is necessary to accumulate data from coagulation reaction curves of multiple specimens containing both normal and abnormal APTT values, and to categorize the normal and abnormal waveforms and parameters. However, as mentioned above, the frequency of patients with abnormal APTT is low, making it difficult to collect sufficient data from only one medical institution.

[0088] If data from patients scattered across various locations is collected, databased, and shared, it becomes possible to achieve improved accuracy due to a dramatic increase in the amount of accumulated data, and to reduce the discrepancies in examination results across different medical institutions due to the standardization of judgment criteria.

[0089] 1.1 Overview

[0090] This invention provides an APTT prolongation factor estimation system. The APTT prolongation factor estimation system of this invention includes: one or more facilities, each having an analytical device for measuring the coagulation reaction of a blood sample; a database storing data related to the coagulation reaction of the blood sample and data on APTT prolongation factors; and a computer for estimating the APTT prolongation factor of the blood sample based on the data related to the coagulation reaction from the analytical device and the data stored in the database. The facility further includes: a data transmission unit for transmitting the data related to the coagulation reaction of the blood sample obtained from the analytical device to the computer; and a data receiving unit for receiving the estimation results of the APTT prolongation factor of the blood sample estimated by the computer as measured by each analytical device.

[0091] The APTT extension factor estimation system of the present invention may further include: an analysis device control unit for controlling the analysis device; and / or a data display unit for displaying data received by the data receiving unit.

[0092] The APTT prolongation factor estimation system of the present invention may further include an algorithm learning unit, which uses data related to the coagulation reaction of blood samples and data on APTT prolongation factors stored in the above-mentioned database to perform machine learning on the APTT prolongation factor estimation algorithm.

[0093] The following illustration Figure 1 Table 1 shows a conceptual diagram of one embodiment of the APTT extension factor estimation system of the present invention and an embodiment of the APTT extension factor estimation method using the system. Figure 1 In this diagram, three facilities, A, B, and C, are equipped with analytical devices for measuring the coagulation reaction of blood samples, as well as communication devices with data transmission and reception units. The facilities transmit the coagulation reaction data measured by the analytical devices to a server shared by the three facilities via the data transmission unit. (Illustrated) Figure 1 In this system, the server connects to three facilities, but the number of facilities connected to the server can be increased or decreased depending on the server's processing capacity, with no particular limitation. The coagulation reaction data acquired by the server is sent to a computer, which uses this data, along with data from a database (DB), to perform APTT prolongation factor estimation. This computer and database can be programmed into the server separately or configured independently. (Illustration follows) Figure 1In this system, the computer and database are programmed into a server. The computer's estimated APTT prolongation factor results can be sent from the server and received by the facility's data receiving unit. Additionally, the estimated APTT prolongation factor results are accumulated in the database. The facility can determine the APTT prolongation factor of a blood sample based on the received estimated APTT prolongation factor results. The determination results in the facility can be accumulated in the database via the data sending unit and the server. The server may also have a built-in algorithm learning unit, or it may be connected to an external algorithm learning unit. The algorithm learning unit can construct an APTT prolongation factor estimation algorithm using machine learning with data from the database and provide it to the computer.

[0094] Indication Figure 1

[0095]

[0096] Table 1

[0097]

[0098] As facilities, list all facilities that are necessary to perform coagulation reaction tests or to obtain and analyze the results of such tests, such as medical institutions like hospitals and clinics, examination facilities, and research facilities.

[0099] The analytical device can be a commercially available blood analyzer capable of measuring the coagulation reaction of a blood sample (e.g., a plasma sample). Preferably, the analyzer is capable of performing the following measurements.

[0100] 1) Time-lapse solidification reaction measurement based on photometric data, etc.

[0101] It can also calculate the solidification reaction curve based on the measured data.

[0102] 2) APTT (Activated Partial Thromboplastin Time) Measurement

[0103] According to the analytical device, when the APTT measurement result of the sample is prolonged, the next immediate and delayed reactions can be automatically implemented.

[0104] A) APTT measurement of the subject

[0105] B) APTT measurement to confirm the presence of an immediate response without heating the mixed plasma of normal plasma and the subject.

[0106] C) APTT measurement of a mixture of normal plasma and the subject's plasma after heating to confirm the presence of a delayed response.

[0107] Examples of blood analysis devices capable of performing the above-mentioned measurements 1) and 2) include the CP3000 automated coagulation analyzer (Sekisui Medical Co., Ltd.).

[0108] As data sent to the computer, examples include data on the time-dependent coagulation reaction of blood samples (measurement data). This measurement data can be the raw measured data, or the data of the coagulation reaction curve or its first or second derivative curve calculated from it, or parameters related to the weighted average point of the waveform of the coagulation reaction curve or its first or second derivative curve, or a combination thereof.

[0109] Examples of parameters related to waveform or weighted average points include waveform-related parameters (solidification time calculated from the solidification reaction curve, maximum peak height with respect to the first or second differential curve, and its time, etc.) and weighted average point-related parameters (weighted average time with respect to the first or second differential curve, weighted average height, peak width, flatness, time rate, etc.). Parameters related to waveform or weighted average points will be explained in section 1.2.2 below.

[0110] In cross-mixing tests, for normal samples, test samples, and mixed samples containing test samples and normal samples in various volume ratios, the coagulation reaction before heating (immediate reaction) and the coagulation reaction after heating (delayed reaction) can be measured and transmitted as measurement data. The parameters related to the waveform or weighted average point mentioned above before and after heating, or the differences or ratios of the calculated parameters before and after heating, can also be calculated and transmitted as measurement data.

[0111] In cross-mixing tests, the mixed samples contain the test sample and normal sample in a volume ratio of 1:9 to 9:1, preferably 4:6 to 6:4, and more preferably 5:5. If necessary, the test sample may be diluted before mixing with the normal sample, and the diluted test sample and normal sample may be mixed in the above ratio. Heating treatment is performed, for example, at 30°C or higher and 40°C or lower, preferably 35°C or higher and 39°C or lower, more preferably 37°C, for no more than 1 hour, preferably 2 to 30 minutes, and more preferably 5 to 30 minutes. However, prior to the above heating treatment, the sample may also undergo heating treatment as in conventional coagulation reaction assays, for example, heating at 30°C or higher and 40°C or lower for no more than 1 minute.

[0112] In addition to the measurement data mentioned above, measurement conditions (such as the identification number of the analysis device, the photometric module number, the type, identification number, and batch number of the reagents used) and data on whether the APTT of the blood sample is delayed can also be sent as needed.

[0113] The blood sample is a blood sample from the subject whose APTT prolongation factor needs to be determined (hereinafter also referred to as the test sample). Preferably, the test sample is a blood sample showing APTT prolongation. As the blood sample, plasma supplemented with citrate is preferred. To calibrate the measurement results, measurement data for the administration sample can also be sent. The administration sample can be a blood sample known to have or not have APTT prolongation and prolongation factors. By comparing the measurement data from the administration sample with the measurement results from the same sample in the past (or a sample with the same prolongation factor), the measurement data of the facility's analytical apparatus can be calibrated, or the analytical conditions can be checked for abnormalities. The transmission of administration sample data can be performed each time test sample data is transmitted, but it can also be performed separately from the transmission of test sample data, for example, periodically or in conjunction with reagent changes. In the former case, the measurement of the administration sample is preferably performed before the test sample, more preferably immediately before the measurement of the test sample.

[0114] Parameters related to the aforementioned waveforms or weighted average points can also be calculated in each facility and sent to the computer, or they can be calculated in the computer using raw data from solidification reaction measurements, solidification reaction curves, or their first or second derivative curves sent from each facility. In the following specification, without distinguishing between data from facilities and data calculated by a computer, the raw data from solidification reaction measurements, data from solidification reaction curves, or their first or second derivative curves, and data on parameters of these curves related to the waveforms or weighted average points are collectively referred to as "measurement data."

[0115] The estimation of the APTT prolongation factor in the computer can be implemented based on the measurement data of the test subjects that were subjected to coagulation reaction measurement in various facilities and the data stored in the database.

[0116] The database contains: the status of previously acquired specimens (e.g., normal, presence or absence of various coagulation factor inhibitors, inhibitor titer, LA, coagulation factor deficiency, type of deficient coagulation factor, concentration of various coagulation factors, etc.) as well as coagulation reaction curves or their first or second derivative curves for the specimen, or parameters related to waveforms or weighted average points calculated from these curves.

[0117] For example, by comparing the measurement data of the tested specimen with measurement data of various specimens containing specimens with normal and various APTT prolongation factors stored in a database, the APTT prolongation factor of the tested specimen can be estimated. For example, the shape of the coagulation reaction curves, or their first or second derivative curves, can be directly compared between the tested specimen and the specimens in the database, or their parameters related to the waveform or weighted average point can be compared. The APTT prolongation factor of the tested specimen can be estimated based on the comparison results. For example, specimens with high concordance rates with the tested specimen's parameters can be selected from the database, and the APTT prolongation factor of the selected specimen (e.g., coagulation factor deficiency, LA, coagulation factor inhibitor) can be estimated as the APTT prolongation factor of the tested specimen. Detailed steps for estimating the APTT prolongation factor in a computer are described later.

[0118] Representative examples of presumed factors prolonging APTT include coagulation factor deficiency, LA, and the presence of coagulation factor inhibitors. Furthermore, more detailed factors can be presumed as shown in Table 2 below.

[0119] Table 2

[0120] Estimated APTT prolongation factor

[0121]

[0122] **: Factors other than VIII and IX (II, V, X, XI, XII, and prokallikrein)

[0123] #: Factor activity value

[0124] FVIII:C, FIX:C, F**:C indicate the concentration of coagulation factor.

[0125] The computer-calculated estimate of the APTT prolongation factor for the subject is sent to the facility that acquired the subject's data. In addition to the factor names or factor classification names shown in Table 2 above, the estimate may also include factor classification index values, the maximum concordance rate between the parameter and the samples in the database when the estimate was calculated, and statistical indicators (correlation coefficient, coefficient of determination, significance level, etc.). In cases where the prolongation factor is not determined to be a single factor, multiple prolongation factors are estimated, or a prolongation factor cannot be estimated, the information may also include the maximum concordance rate of the parameter, statistical indicators (correlation coefficient, coefficient of determination, significance level, etc.), and be sent to the facility as needed.

[0126] The estimated APTT prolongation factor sent from the computer is received by the facility's data receiving unit and stored or used. For example, based on this estimated result, the facility's physician can determine the APTT prolongation factor of the examined individual.

[0127] The estimated results of the APTT prolongation factor of the tested specimen calculated by the computer can be accumulated in the database. The database can be updated to add new measurement data (data of coagulation reaction curve or its first or second derivative curve, or parameters related to the waveform or weighted average point calculated from these curves) and estimated status (presence, type, etc. of APTT prolongation factor) of the tested specimen sent from the computer.

[0128] The database may also include the results of assessments of APTT prolongation factors for the subject performed at the facility. These assessment results of APTT prolongation factors can be transmitted to the database via a data transmission unit. In this invention, by importing the APTT prolongation factors of the subject as determined by the physician into the database, it is possible to establish a correlation between the measurement data and clinical diagnosis, or to improve the accuracy of computer-predicted results.

[0129] 1.2 APTT Extension Factor Estimation Algorithm

[0130] 1.2.1 Template Matching

[0131] It is not necessarily limited to algorithms in computers used to estimate the APTT prolongation factor of the subject based on measurement data about the subject and data stored in the database, such as template matching as shown below.

[0132] In the database, the status related to APTT prolongation factors in blood samples (e.g., presence or absence of APTT prolongation factors, presence or absence of various coagulation factor inhibitors, inhibitor titers, LA, coagulation factor deficiency, types of deficient coagulation factors, concentrations of various coagulation factors, etc.) is correlated with coagulation assay data (coagulation reaction curves or their first or second derivative curves, or parameters related to waveforms or weighted average points calculated from these curves, etc.). As a result, the database provides templates for coagulation assay data for samples with various APTT prolongation factors. By using template matching to investigate which template the sample's assay data most closely approximates, samples with the same APTT prolongation factors as the sample can be selected from the database. The APTT prolongation factors of the selected samples can be presumed as the APTT prolongation factors of the sample. Therefore, the more samples included in the database, the more accurate the presumption of APTT prolongation factors according to the present invention can be improved.

[0133] In a more specific example of template matching, regression analysis is performed between the parameter set related to the waveform or weighted average point of the tested specimen (the tested parameter set) and the parameter set related to the waveform or weighted average point of each specimen in the database (the template parameter set). Specimens in the database with the template parameter set that best matches the tested parameter set (e.g., the highest correlation) are selected. The APTT extension factor of the selected specimen is then extrapolated as the APTT extension factor of the tested specimen.

[0134] The parameter sets used for template matching that are related to waveforms or weighted average points preferably include two or more parameter sets from the parameters related to waveforms or weighted average points shown in Table 3, preferably three or more, and more preferably five or more. When the parameter type is a parameter related to weighted average points (e.g., weighted average time, weighted average height, peak width, flatness, or time rate), for each parameter type, it is preferable to obtain data (parameters) for 3 to 100, more preferably 5 to 20 computational target regions (described later). Next, a linear regression equation is derived between the parameter set being tested and the template parameter set for each specimen in the database. For example, when the parameter set represents the peak width vB ​​and flattening vAB of the first differential curve related to the weighted average point of the first differential curve at the operand regions of 5%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, and 90%, vB5% of the tested parameter set is set as X, and vB5% of the template parameter set is set as Y for plotting. Similarly, vB10% to vB90% and vAB5% to vAB90% are plotted. Next, the linear regression equation for the obtained graph is obtained. Specimens from the database that produce regression lines with high correlation are selected. Preferably, specimens from the database that produce regression lines with the highest correlation are selected.

[0135] Table 3

[0136] Examples of parameters related to waveforms or weighted average points included in the parameter set used for template matching.

[0137] The first-order differential curve is F(t), the second-order differential curve is F'(t), and the value of the operand region is x% (t is time).

[0138]

[0139]

[0140] Figure 1 A shows an example of a linear regression equation. Figure 1A represents the regression line between the sample (Sample no. 67) with FVIII activity less than 0.2% and the sample in the database (Template A). The parameter set uses a set of parameters consisting of peak width vB, weighted average time vT, weighted average height vH, flattening vAB, and time rate vTB (a total of 50 parameters) for 10 operational regions (x = 5%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, and 90%) of the first differential curve. Figure 1 B shows the first-order differential curves of the sample (Sample No. 67) and the reference (Template A) in Figure 1A (obtained by first-order differentiation after zero-point adjustment and relativity of the coagulation reaction curve). The two curves have very similar shapes, indicating that the approximation of coagulation characteristics reflects the correlation between the parameter sets of the samples.

[0141] Table 4 shows examples of approximate index values ​​(correlation coefficients) used in template matching based on linear regression to determine the approximate values ​​of the measured data of the tested specimens and the measured data of specimens in the database. The approximate index values ​​output as the inference result are displayed from the top position to a pre-set position N (N≥1). "B" in Table 4 indicates the case where N=2.

[0142] Table 4

[0143]

[0144] 1.2.2 Calculation of parameters related to waveform or weighted average points

[0145] The steps for calculating parameters related to waveforms or weighted average points based on raw data related to the coagulation reaction of blood samples obtained from the analytical device are described. The coagulation reaction curve is a curve obtained by arranging the raw data related to the coagulation reaction obtained from the analytical device in a time series. For the coagulation reaction curve used for parameter calculation, it is preferable to perform smoothing processing for noise removal and zero-point adjustment. In zero-point adjustment, for example, the data is corrected so that the value at the start of the measurement is 0. Furthermore, it is preferable to perform correction so that the maximum value of the coagulation reaction curve is a predetermined value (e.g., 1 or 100).

[0146] Differentiate the modified solidification reaction curve described above to create first-order and second-order differential curves. These represent the rate of change of the solidification reaction curve.

[0147] Using a first-order differential curve as the object curve, the steps for extracting parameters related to the waveform or weighted average point are explained. In a second-order differential curve, the same steps can be used to extract parameters related to the waveform or weighted average point. First, determine the operand region value x% of the object curve F(t) (t is time). This operand region value x% is the value of F(t) relative to its maximum value (100%) as x%. The region where F(t) ≥ x% is called the operand region. It is also possible to set two or more operand region values ​​for an object curve. Next, calculate the weighted average point (vT, vH) of F(t) with respect to the operand region value (i.e., the weighted average value of F(t) in the operand region). First, calculate the maximum value of F(t) as Vmax, the time t [t1, ..., t2] (t1 < t2) when the operand region value is x (relative to Vmax), and calculate the product value M using the following formula (1). The weighted average time vT and weighted average height vH are calculated using equations (2) and (3) respectively. The weighted average point is derived based on the calculated vT and vH.

[0148] [Equation 2]

[0149]

[0150]

[0151]

[0152] As the operand region value x changes, the position of the weighted average point changes. To identify vT and vH from different operand region values, there are cases where they are named vTx and vHx respectively according to the operand region value x from which they originate (see Table 3). For example, the vT and vH of the operand region where x is 5% are vT5% and vH5%.

[0153] Next, the peak width vB, weighted average peak width vW, flattening rates vAB and vAW, time rates vTB and vTW, and peak region vAUC will be explained. For these parameters, to identify those derived from different operand regions, they are referred to as vBx, vWx, vABx, vAWx, vTBx, vTWx, and vAUCx respectively, based on the operand region value x from which they originate (see Table 3). x varies depending on the operand region value. For example, vBx, vWx, vABx, vAWx, vTBx, vTWx, and vAUCx for an operand region where x is 5% are vBx5%, vWx5%, vAB5%, vAW5%, vTB5%, vTW5%, and vAUC5%.

[0154] When the maximum and minimum values ​​of the time t satisfying F(t)≥x are set as vTex and vTsx respectively, the time length of F(t)≥x from vTsx to vTex is set as the peak width vBx of F(t) with respect to x. In addition, the peak width satisfying F(t)≥vH is set as the weighted average peak width vWx (refer to Table 3).

[0155] Calculate the flattening ratio vABx[vABx=vHx / vBx] of F(t) with respect to x, the flattening ratio vAWx[vAWx=vHx / vWx] based on the weighted average peak width, the time rate vTBx[vTBx=vTx / vBx], and the time rate vTWx[vTWx=vTx / vWx] based on the weighted average peak width. Alternatively, the flattening ratio can also be vABx=vBx / vHx or vAWx=vWx / vHx. Similarly, the time rate can also be vTBx=vBx / vTx or vTWx=vWx / vTx. These ratios can also be multiplied by a constant K. Furthermore, the peak region (area under the curve, vAUCx) in the operand region of F(t) can be obtained (see Table 3).

[0156] The above are the parameters of F(t) related to the weighted average point.

[0157] As waveform-related parameters of F(t), the maximum value Vmax of F(t) and the time VmaxT to reach Vmax are given as examples. Furthermore, when the amount of scattered light at the moment when the change in the amount of scattered light in the solidification reaction curve meets a specified condition is set to 100%, the reaction time elapsed at which the amount of scattered light is equivalent to c% can be included as the solidification time Tc in the waveform-related parameters used in this invention. c can be any value, for example, Tc is T50.

[0158] 1.2.3 Other

[0159] The above-described steps for calculating parameters related to waveforms or weighted average points, and the methods for estimating APTT prolongation factors (diagnosis of hemophilia A, quantification of factor VIII, and APTT cross-mixing test) using these parameters, can be used in this invention as an APTT prolongation factor estimation algorithm that can be utilized in a computer.

[0160] 1.3 Machine Learning for the APTT Extension Factor Estimation Algorithm

[0161] The APTT prolongation factor estimation system can also include an algorithm learning unit that uses data on coagulation-related factors of blood samples and data on APTT prolongation factors stored in the database to perform machine learning on the APTT prolongation factor estimation algorithm. This algorithm learning unit can be integrated into the computer or set up separately. For example, the algorithm learning unit can construct a machine learning model for estimating APTT prolongation factors of blood samples using machine learning, where data on coagulation-related factors of the blood samples are used as explanatory variables, and data on APTT prolongation factors of the blood samples are used as target variables. As explanatory variables related to coagulation response, at least one is selected from the group consisting of the aforementioned measurement data (coagulation response curves or their first or second derivative curves and parameters related to the waveform or weighted average points of these curves, such as coagulation time, weighted average time, weighted average height, peak width, flatness, and time rate, more specifically Tc, vTx, vHx, vBx, vWx, vABx, vAWx, vTBx, vTWx, Vmax, VmaxT, vTsx, vTex, vAUCx, pTx, pHx, pBx, pWx, pABx, vAWx, pTBx, pTWx, Amax, AmaxT, and pAUCx (x is any operand range value)). This machine learning model provides an algorithm for estimating APTT prolongation factors (e.g., coagulation factor deficiency, LA, coagulation factor inhibitors, etc.) of the subject by taking at least one input from a group consisting of the above-mentioned measurement data about the subject.

[0162] The algorithm constructed by the algorithm learning unit can be replaced in a timely manner with previous APTT prolongation factor estimation algorithms in the computer (such as template matching using the aforementioned regression line, or machine learning models previously constructed by the algorithm learning unit). That is, the computer can estimate the APTT prolongation factor of the subject by utilizing the APTT prolongation factor estimation algorithm constructed using machine learning in the algorithm learning unit.

[0163] Example

[0164] The present invention will now be described in detail through embodiments, but the present invention is not limited to the following embodiments.

[0165] (Example 1) Number of templates (sample data in the database) and judgment accuracy in template matching

[0166] 1) The subject of the examination

[0167] The following 78 samples were used: 70 samples of hemophilia A; 2 samples of acquired hemophilia A; 1 sample of von Willebrand disease (vWD) Type 1; 3 samples of hemophilia B; and 2 samples of lupus anticoagulant (LA) positive.

[0168] 2) Template Specimen

[0169] 143 template specimens were used (Table 5: Ver.1), or 158 template specimens were used after adding 15 more specimens to them (Table 5: Ver.3).

[0170] Table 5

[0171]

[0172] *1): CRYOcheck Pooled Normal Plasma (Precision BioLogic Incorporated)

[0173] *2): Add 5–20 (w / v)% barium sulfate to normal plasma and mix for 10–30 minutes at room temperature.

[0174] Then, barium sulfate is removed to prepare the product.

[0175] 3) Solidification reaction meter measurement

[0176] The coagulation reaction of the test sample and the template sample was measured. The coagulation reaction was measured using the APTT assay reagent COAGPIA APTT-N (manufactured by Sekisui Medical Co., Ltd.) and a CP3000 automated coagulation analyzer (manufactured by Sekisui Medical Co., Ltd.). The coagulation reaction curve was obtained by measuring 90-degree side-scattered light. The first-order differential curve was calculated based on the obtained coagulation reaction curve.

[0177] 4) Parameter Calculation

[0178] For both the tested specimen and the template specimen, calculate the parameters related to the weighted average point of the first-order differential curve. Based on the first-order differential curve, the calculation area is divided into 10 stages from 5% to 90% (with the maximum value Vmax of the first-order differential curve set to 100%, and the calculation area value x at 5%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, and 90%). For each calculation area, calculate the peak width vB, weighted average time vT, weighted average height vH, flattening ratio vAB = (vH / vB), and time rate vTB = (vT / vB).

[0179] Through the above steps, the parameters for the 10 operand regions (x = 5%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, and 90%) are calculated as follows: vB [vB5%, vB10%, vB20%, vB30%, vB40%, vB50%, vB60%, vB70%, vB80%, vB90%], vT [vT5%, vT10%, vT20%, vT30%, vT40%, vT50%, vT60%, vT70%, vT80%, vT90%], vH [vH ...20%, vT30%, vT40%, vT50%, vT60%, vT70%, vT80%, vT90%], vH [vH5%, vT10%, vT20%, vT20%, vT30%, vT40%, vT50%, vT60%, vT70%, vT80%, vT90%], vH [vH5%, vT10%, vT20%, vT20%, vT30%, vT40%, vT50%, vT60%, vT70%, vT80%, vT90%], v [vH10%, vH20%, vH30%, vH40%, vH50%, vH60%, vH70%, vH80%, vH90%], vAB [vAB5%, vAB10%, vAB20%, vAB30%, vAB40%, vAB50%, vAB60%, vAB70%, vAB80%, vAB90%], and vTB [vTB5%, vTB10%, vTB20%, vTB30%, vTB40%, vTB50%, vTB60%, vTB70%, vTB80%, vTB90%]. A parameter group consisting of these 50 parameters is created for each template specimen and the specimen being examined.

[0180] 5) Template matching

[0181] The APTT prolongation factors for each tested specimen were estimated using template matching. A linear regression equation for the parameter set was calculated between each tested specimen and each of all template specimens. Template specimens with a regression equation slope of 1.25–0.75 were extracted, and the specimen with the highest correlation coefficient was selected from the extracted template specimens. The coagulation ability information of this template specimen was set as the coagulation ability information of the tested specimen. Based on the analysis results, the coagulation ability information (FVIII concentration) of the tested specimen was classified into any of the following categories: FVIII < 1% (FVIII activity less than 1%), FVIII 1–5% (FVIII activity greater than 1% to less than 5%), FVIII 5–40% (FVIII activity greater than 5% to less than 40%), or other (FVIII > 40% (FVIII activity greater than 40%), or APTT prolongation due to factors other than FVIII deficiency.

[0182] Figure 2 A represents the first-order differential curve of the tested specimen (specimen no. 57) and the template specimen with the highest correlation coefficient (#027, FVIII activity 0.6%, hemophilia A), as well as the regression line of the parameter groups among these specimens. Figure 2B represents the first-order differential curve of specimen no. 57 and the template specimen (#134, FVIII activity 0.25%, hemophilia A) with the second-highest correlation coefficient, as well as the regression line of the parameter set between these specimens. Specimen no. 57 was determined to be a specimen with the coagulation ability information of template #027 according to this method and was classified as FVIII < 1%. In the actual test, this specimen was hemophilia A with FVIII activity of 0.3%.

[0183] The template matching results for the 78 subjects are shown in Tables 6 and 7. Table 6 shows the measured values ​​of FVIII activity for each subject and the estimated results based on this method, while Table 7 shows the agreement rate between the estimated and measured values. Increasing the number of templates resulted in an increase in agreement rate, sensitivity, specificity, and FVIII concentration classification agreement rate.

[0184] Table 6

[0185]

[0186] Table 7

[0187]

[0188] (Example 2) Correction for batch-to-batch variation of reagents used in managing specimens

[0189] 1) Specimen

[0190] In normal specimens, plasma supplemented with citric acid obtained from healthy individuals is used.

[0191] In hemophilia specimens, plasma from hemophilia A patients with added citrate was used.

[0192] 2) Determination of coagulation reaction of the specimen

[0193] APTT assay was performed using a CP3000 automated coagulation analyzer (manufactured by Sekisui Medical Co., Ltd.). After dispensing 50 μL of the test sample into the reservoir (reaction vessel), it was heated to 37°C for 45 seconds. Then, 50 μL of APTT reagent heated to approximately 37°C was dispensed into the reservoir, followed by a further 171 seconds, after which 50 μL of 25 mM calcium chloride solution was dispensed into the reservoir to initiate the coagulation reaction. Two Lots of APTT reagent (Lot A and Lot B) were used at this time. The coagulation reaction was carried out while maintaining the reservoir at approximately 37°C. For detection of the coagulation reaction, light was emitted from a 660 nm LED source, and the amount of scattered light from a 90-degree side scattering was measured at 0.1-second intervals. The measurement time was set to 360 seconds.

[0194] 3) Methods for analyzing photometric data

[0195] APTT (Average Per Second) measurements were performed to obtain raw photometric data for the solidification reaction based on APTT measurements. After smoothing this raw photometric data to remove noise, zero-point adjustment was performed to ensure that the amount of scattered light at the start of photometric measurement was 0, resulting in the solidification reaction curve. Next, a correction process was performed to ensure that the maximum height of the solidification reaction curve was 100, resulting in the corrected solidification reaction curve. This corrected solidification reaction curve was set as a zero-order waveform, and the waveform after first differentiation is the first-order waveform reflecting the solidification rate.

[0196] The weighted average parameters are obtained from a single waveform.

[0197] 4) Correction for APTT batch differences

[0198] The correlation between the weighted average waveform parameters obtained from the determination of Lot A and Lot B of the APTT reagent was analyzed, and the obtained regression equation (y = 0.965x + 0.13) was used to approximate the weighted average parameter of Lot B to the value of Lot A.

[0199] 5) Confirmation of the correction effect of APTT Lot difference

[0200] Eight patient samples were used to compare the template matching results using the weighted average waveform parameters of Lot A, Lot B, and the corrected Lot B. As shown in Table 8, without correction for the differences between Lots, inconsistencies were observed, but the corrected Lot B was consistent with Lot A. The judgment result "Other" in Table 8 indicates samples judged as having prolonged APTT due to factors other than hemophilia A.

[0201] Table 8

[0202] Template matching result

[0203]

Claims

1. An APTT prolongation factor estimation system, comprising: Multiple facilities, each having an analysis device for measuring the coagulation reaction of a test blood specimen; A database that stores data related to the coagulation reaction of blood specimens and data on APTT prolongation factors, and can share the stored data among the multiple facilities; and A computer that estimates the APTT prolongation factor of the test blood specimen based on data related to the coagulation reaction from the analysis device and data stored in the database. Each of the multiple facilities further comprises: a data transmission unit that transmits data related to the coagulation reaction of the test blood specimen obtained by the analysis device to the computer; and a data reception unit that receives the estimation result of the APTT prolongation factor of the test blood specimen measured by each analysis device estimated by the computer. The data related to the coagulation reaction of the test blood specimen sent to the above computer and the estimation result of the APTT prolongation factor of the test blood specimen obtained by the computer are stored in the above database, and the stored data and the estimation result can be shared among the multiple facilities.

2. The system according to claim 1, wherein The above facility further comprises: An analysis device control unit that controls the above analysis device; and / or A data display unit that displays the data received by the above data reception unit.

3. The system according to claim 1 or 2, wherein An algorithm learning unit is further provided, which performs machine learning on the APTT prolongation factor estimation algorithm using data related to the coagulation reaction of blood specimens and data on APTT prolongation factors stored in the above database.

4. The system according to claim 1 or 2, wherein The data related to the above coagulation reaction includes one or more parameters related to the waveform or weighted average point of the coagulation reaction curve or the first or second differential curve of the coagulation reaction curve.

5. The system according to claim 4, wherein Parameters related to the above waveform are selected from the coagulation time, the maximum peak height of the above first or second differential curve, and the time to reach the maximum peak height, and parameters related to the above weighted average point are selected from the weighted average time, weighted average height, peak width, flatness ratio, time rate, and peak area of the operation object region of the above first or second differential curve.

6. The system according to claim 5, wherein The parameters related to the above weighted average point include one or more parameters selected from the weighted average time, weighted average height, peak width, flatness ratio, time rate, and peak area obtained for each operation object region of two or more operation object regions of the above first differential curve.

7. The system according to claim 6, wherein When the above first differential curve is set as F(t), and the times when F(t) is a specified value x are set as t1 and t2, the operation object region is the region where F(t)≥x, t is time, t1<t2, and the above weighted average time and weighted average height are respectively represented as vT and vH shown in the following formula: [Equation 1] Here, The peak width mentioned above is expressed as the time length from t1 to t2 when F(t) ≥ x, i.e., vB. The aforementioned flatness ratio is expressed as the ratio of vH to vB, vAB. The aforementioned time rate is expressed as the ratio of vT to vB, vTB. The aforementioned peak region is represented as the area under the curve, vAUC.

8. The system according to claim 7, wherein, The specified value x is 5 to 90% of the maximum value of F(t).

9. The system according to claim 4, wherein, The APTT prolongation factor of the tested blood sample is estimated by selecting one or more samples whose parameters related to the waveform or weighted average point are similar to those of the tested blood sample from the blood samples stored in the database, and by estimating the APTT prolongation factor of the selected sample as the APTT prolongation factor of the tested blood sample.

10. The system according to claim 9, wherein, Regression analysis was used to implement the above selection of specimens.

11. The system according to claim 9, wherein, The selection of the above-mentioned specimens was carried out using the APTT extension factor estimation algorithm obtained through machine learning.

12. The system according to claim 1 or 2, wherein, The data on APTT prolongation factors mentioned above pertain to coagulation factor deficiency, lupus anticoagulant positivity, presence or absence of coagulation factor inhibitors, or coagulation factor concentration.

13. The system according to claim 12, wherein, The presumption of APTT prolongation factors for the above-mentioned blood samples includes presuming coagulation factor deficiency, lupus anticoagulant positivity, or the presence or absence of coagulation factor inhibitors as APTT prolongation factors for the blood samples tested.

14. The system according to claim 12, wherein, The estimation of APTT prolongation factors in the above-mentioned blood samples includes the estimation of the coagulation factor concentration of the blood sample.

15. The system according to claim 1 or 2, wherein, The aforementioned data transmission unit sends data related to the coagulation reaction of the managed specimens to the aforementioned computer. The computer will compare data related to the coagulation reaction of the managed specimen with data related to the coagulation reaction of past managed specimens obtained using reagents from different batches. The measurement data from the aforementioned analytical device are corrected based on this comparison result. The managed specimen is a blood specimen with known APTT prolongation factors, and the previous managed specimen is the same specimen as the managed specimen or a specimen with the same APTT prolongation factors as the managed specimen.

16. One method is the APTT prolongation factor estimation method, which includes: The coagulation reaction of the tested blood samples was measured at each of the multiple facilities; Data related to the coagulation reaction of the blood sample being examined, obtained from each of the multiple facilities, is sent from each facility to the computer. The computer estimates the APTT prolongation factor of the tested blood sample based on data related to the coagulation reaction of the tested blood sample, data related to the coagulation reaction of the blood sample stored in a database and shareable among the multiple facilities, and data on the APTT prolongation factor. The estimated results of the APTT prolongation factor of the tested blood sample obtained by the computer are sent to the facility that measures the coagulation reaction of the tested blood sample; as well as The data related to the coagulation reaction of the blood sample sent to the computer and the estimated results of the APTT prolongation factor of the blood sample obtained by the computer are stored in the database, and the stored data and the estimated results can be shared among the multiple facilities.

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