Sample analysis device, sample analysis system, sample analysis method, and program

Abstract

We provide specimen analyzers, specimen analysis systems, specimen analysis methods, and programs that can contribute to highly accurate dementia diagnosis using dementia biomarkers. [Solution] The specimen analyzer 1 comprises a measuring device 2 that measures analytes related to dementia biomarkers contained in a specimen taken from a subject, a storage unit that stores reference values ​​for identifying outliers that may occur due to factors other than dementia, a control unit that determines whether the measured value is identified as an outlier based on the measured value of the analyte by the measuring device 2 and the reference value, and generates biomarker analysis results according to the result of the determination, and an output unit that outputs the biomarker analysis results.

Description

Technical Field

[0001] The present invention relates to a specimen analysis apparatus, a specimen analysis system, a specimen analysis method, and a program for analyzing a specimen.

Background Art

[0002] In the field of dementia, expectations for diagnosis using dementia biomarkers in specimens such as whole blood and plasma are increasing due to simplicity, low cost, and the possibility of early diagnosis. On the other hand, the measurement results of dementia biomarkers may vary due to the influence of other diseases other than dementia.

[0003] For example, Non-Patent Document 1 describes that the concentrations of p-Tau181 and p-Tau217, which are dementia biomarkers, are related to the severity of kidney disease.

Prior Art Documents

Patent Documents

[0004]

Non-Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0005] In the diagnosis of dementia using dementia biomarkers, it is required to perform a highly accurate diagnosis even when the measurement results of the dementia biomarkers are affected by factors other than dementia.

[0006] In view of these challenges, the present invention aims to provide a specimen analysis device, specimen analysis system, specimen analysis method, and program that can contribute to highly accurate dementia diagnosis using dementia biomarkers. [Means for solving the problem]

[0007] The specimen analyzer (1) of the present invention comprises: a measurement unit (2) that measures analytes related to dementia biomarkers contained in a specimen taken from a subject; a storage unit (102) that stores reference values ​​for identifying outliers that may occur due to factors other than dementia; a control unit (101) that determines whether a measurement value is identified as an outlier based on the measurement value of the analyte by the measurement unit (2) and the reference value, and generates biomarker analysis results according to the result of the determination; and an output unit (103) that outputs the biomarker analysis results.

[0008] According to the sample analyzer of the present invention, biomarker analysis results are generated based on the determination of whether or not a measured value is identified as an outlier, and these analysis results are output to the output unit. This makes it easier to understand the possibility that the analysis results may be influenced by factors other than dementia, and can contribute to a highly accurate diagnosis of dementia. [Effects of the Invention]

[0009] According to the present invention, it is possible to contribute to highly accurate dementia diagnosis using dementia biomarkers. [Brief explanation of the drawing]

[0010] [Figure 1] Figure 1 is a schematic diagram showing the configuration of a sample analyzer according to Embodiment 1. [Figure 2] Figure 2 is a block diagram showing the functional configuration of the control device according to Embodiment 1. [Figure 3] Figure 3 shows the configuration of the measurement order registration screen for which an operator registers a measurement order, according to Embodiment 1. [Figure 4] Figure 4 is a diagram showing the configuration of the job list screen for displaying the job list according to Embodiment 1. [Figure 5] Figure 5 is a schematic diagram showing the distribution range according to Embodiment 1. [Figure 6] Figure 6 is a diagram illustrating an example of a method for setting reference values ​​according to Embodiment 1. [Figure 7] Figure 7 is a schematic diagram showing the configuration of the reference value database and the measurement result database according to Embodiment 1. [Figure 8] Figure 8 shows the configuration of the reference value setting screen for setting reference values, according to Embodiment 1. [Figure 9] Figure 9 shows the configuration of the reference value setting screen for setting reference values ​​by the operator according to Embodiment 1. [Figure 10] Figure 10 is a diagram showing the configuration of the job list screen according to Embodiment 1, which displays the jobs after measurement. [Figure 11] Figure 11 is a flowchart showing the processing by the sample analyzer according to Embodiment 1. [Figure 12] Figure 12 is a flowchart showing the details of the measurement process according to Embodiment 1. [Figure 13] Figure 13 is a flowchart showing the details of the judgment process based on the reference value according to Embodiment 1. [Figure 14] Figure 14 is a schematic diagram showing the configuration of the measurement result database according to Embodiment 2. [Figure 15] Figure 15 is a diagram showing the configuration of the job list screen in the state where the jobs after measurement are displayed, according to Embodiment 2. [Figure 16] Figure 16 is a flowchart showing the details of the judgment process based on the reference value according to Embodiment 2. [Figure 17] Figure 17 is a schematic diagram showing the distribution range and measurement range according to Embodiment 3. [Figure 18]FIG. 18 is a diagram schematically showing the configuration of a measurement result database according to Embodiment 3. [Figure 19] FIG. 19 is a diagram showing the configuration of a job list screen showing a state in which a job after measurement is displayed according to Embodiment 3. [Figure 20] FIG. 20 is a flowchart showing details of determination processing based on a reference value according to Embodiment 3. [Figure 21] FIG. 21 is a diagram schematically showing the configuration of a measurement item database according to Embodiment 4. [Figure 22] FIG. 22 is a diagram showing the configuration of a job list screen showing a state in which a job after measurement is displayed according to Embodiment 4-1. [Figure 23] FIG. 23 is a diagram showing the configuration of a job list screen showing a state in which a job after measurement is displayed according to Embodiment 4-2. [Figure 24] FIG. 24 is a diagram showing the configuration of a clinical information input screen and a specimen information input screen according to Embodiment 5. [Figure 25] FIG. 25 is a diagram showing the configuration of a job list screen showing a state in which a job after measurement is displayed according to Embodiment 5. [Figure 26] FIG. 26 is a diagram showing the configuration of a detailed information screen according to Embodiment 5. [Figure 27] FIG. 27 is a diagram schematically showing the relationship between an inspection center and a facility according to Embodiment 6. [Figure 28] FIG. 28 is a diagram schematically showing the configuration of a reference value database according to Embodiment 6. [Figure 29] FIG. 29 is a block diagram showing the functional configuration of a specimen analysis system according to Embodiment 7.

MODE FOR CARRYING OUT THE INVENTION

[0011] <Embodiment 1> FIG. 1 is a diagram schematically showing the configuration of the specimen analysis apparatus 1.

[0012] For convenience, Figure 1 shows the housing 2a of the measuring device 2 in a transparent state, illustrating the internal configuration of the measuring device 2 in a plan view. Figure 1 also shows the left-right and front-back directions.

[0013] Specimen analyzer 1 is an immunoassay analyzer for analyzing at least dementia markers in a specimen. Specimen analyzer 1 may also analyze other items such as hepatitis B, hepatitis C, and thyroid hormones. Specimens include, for example, whole blood, plasma, serum, and cerebrospinal fluid (CSF).

[0014] The sample analyzer 1 comprises a measuring device 2 and a control device 3. The measuring device 2 comprises a sample transport unit 11, a sample dispensing unit 12, an emergency sample / tip transport unit 13, a pipette tip supply device 14, a tip detachment unit 15, reagent tables 16 and 17, a primary reaction unit 18, reagent dispensing units 19 and 20, a primary BF separation table 21, a primary BF separation unit 22, a transfer mechanism 23, a secondary reaction unit 24, a reagent dispensing unit 25, a secondary BF separation table 26, a secondary BF separation unit 27, an R4 reagent dispensing unit 28, an R5 reagent dispensing unit 29, a detection unit 30, a waste hole 31, and a temperature sensor 32.

[0015] In the measurement using measuring device 2, the sample and R1 reagent (a reagent containing capture antibodies that bind to proteins such as antigens and peptides contained in the sample) are mixed, and R2 reagent (a reagent containing magnetic particles that bind to the capture antibodies) is added to the resulting mixture. The capture antibodies and magnetic particles bound to the proteins are attracted to the magnet in the primary BF separation unit 22, and any impurities not attracted to the magnet are aspirated and removed, thereby removing the R1 reagent containing unreacted capture antibodies. After processing with the primary BF separation unit 22, R3 reagent (a reagent containing labeled antibodies) is added to the sample. The labeled antibodies and magnetic particles bound to the proteins are attracted to the magnet in the secondary BF separation unit 27, and any impurities not attracted to the magnet are aspirated and removed, thereby removing the R3 reagent containing unreacted labeled antibodies. After processing the sample in the secondary BF separation unit 27, reagent R4 (dispersion) and reagent R5 (luminescent substrate that emits light during the reaction with the labeled antibody) are added, and the amount of light emitted by the reaction between the labeled antibody and the luminescent substrate is measured. Through this process, the proteins contained in the sample are quantitatively measured.

[0016] When starting the measurement of a sample, the operator places the container T containing the sample in the rack R. The rack R has a holding section that can hold multiple containers T. The operator sets the rack R holding the containers T into the sample transport unit 11.

[0017] The sample transport unit 11 transports the rack R, set by the operator, to the aspiration position of the sample dispensing unit 12. At the aspiration position, the sample dispensing unit 12 sequentially aspirates the samples from the multiple containers T held in the rack R. In the sample analyzer 1, a disposable pipette tip is replaced each time a sample is aspirated and dispensed to prevent the samples aspirated and dispensed by the sample dispensing unit 12 from mixing with other samples.

[0018] The emergency specimen / tip transport unit 13 is equipped with a transport rack 13a that is movable in the left-right direction. The transport rack 13a has a container mounting section 13b and a tip mounting section 13c. The container mounting section 13b holds a container T containing an emergency specimen that needs to be examined in between the specimens transported by the specimen transport unit 11. The container T held in the container mounting section 13b is transported to the right and positioned to overlap with the rotational trajectory of the pipette 12c of the specimen dispensing unit 12.

[0019] The pipette tip supply device 14 sets the inserted pipette tips one by one into the tip placement section 13c. The pipette tips held in the tip placement section 13c are transported to the right and positioned to overlap with the rotational trajectory of the pipette 12c of the sample dispensing section 12. The tip removal section 15 is used to remove the pipette tip attached to the sample dispensing section 12.

[0020] The sample dispensing unit 12 comprises an arm 12a, a shaft 12b, and a pipette 12c. The arm 12a rotates around the shaft 12b and moves vertically. The pipette 12c is installed at the tip of the arm 12a and performs sample aspiration and dispensing. A pipette tip, transported by the tip mounting unit 13c, is attached to the lower end of the pipette 12c. The sample dispensing unit 12 aspirates the sample from container T transported by the sample transport unit 11 and the transport rack 13a.

[0021] Reagent tables 16 and 17 are equipped with tables that are driven to rotate. Reagent table 16 is fitted with a reagent container for reagent R1 and a reagent container for reagent R3. Reagent table 17 is fitted with a reagent container for reagent R2.

[0022] The primary reaction section 18 comprises a primary reaction table 18a and a container transfer section 18b. The primary reaction table 18a has a holding section 18c for holding the cuvette C.

[0023] The reagent dispensing unit 19 comprises an arm 19a, a shaft 19b, and a pipette 19c. The arm 19a rotates around the shaft 19b and moves vertically. The pipette 19c is mounted at the tip of the arm 19a. The reagent dispensing unit 19 aspirates the R1 reagent from a reagent container placed on the reagent table 16 and dispenses the aspirated R1 reagent into an empty cuvette C on the primary reaction unit 18. The sample dispensing unit 12 dispenses the aspirated sample into the cuvette C from which the R1 reagent was dispensed. Subsequently, in the primary reaction unit 18, a primary reaction process is performed in which the sample and R1 reagent in the cuvette C are heated to a predetermined temperature for a predetermined time.

[0024] The reagent dispensing unit 20 has the same configuration as the reagent dispensing unit 19 and includes an arm 20a, a shaft 20b, and a pipette 20c. The reagent dispensing unit 20 aspirates the R2 reagent from the reagent container placed on the reagent table 17 and dispenses the aspirated R2 reagent into the cuvette C on the primary reaction unit 18 from which the sample and R1 reagent have been dispensed.

[0025] The primary reaction unit 18 drives the primary reaction table 18a to rotate and transfer the cuvette C in the holding unit 18c, stirring the sample, R1 reagent, and R2 reagent inside the cuvette C. Subsequently, a secondary reaction process is performed in the primary reaction unit 18, in which the sample, R1 reagent, and R2 reagent inside the cuvette C are heated to a predetermined temperature for a predetermined time. This causes the R2 reagent, which has magnetic particles, to react with the proteins in the sample inside the cuvette C. The container transfer unit 18b transfers the cuvette C, after processing in the primary reaction unit 18, to the primary BF separation table 21.

[0026] The primary BF separation unit 22 performs primary BF separation to remove the R1 reagent containing unreacted capture antibodies from the sample in cuvette C of the primary BF separation table 21.

[0027] The transfer mechanism 23 comprises an arm 23a, a shaft 23b, and a gripping part 23c. The arm 23a rotates around the shaft 23b and moves vertically. The gripping part 23c is installed at the tip of the arm 23a and is configured to grip the cuvette C. The transfer mechanism 23 transfers the cuvette C on the primary BF separation table 21, which has been processed by the primary BF separation unit 22, to the secondary reaction unit 24.

[0028] The secondary reaction section 24 has the same configuration as the primary reaction section 18 and includes a secondary reaction table 24a and a container transfer section 24b. The secondary reaction table 24a has a holding section 24c for holding the cuvette C.

[0029] The reagent dispensing unit 25 has the same configuration as the reagent dispensing unit 19 and includes an arm 25a, a shaft 25b, and a pipette 25c. The reagent dispensing unit 25 aspirates the R3 reagent from the reagent container placed on the reagent table 16 and dispenses the aspirated R3 reagent into cuvette C on the secondary reaction unit 24 from which the sample, R1 reagent, and R2 reagent have been dispensed. Subsequently, in the secondary reaction unit 24, a tertiary reaction process is performed in which the sample, R1 reagent, R2 reagent, and R3 reagent in cuvette C are heated to a predetermined temperature for a predetermined time. The container transfer unit 24b of the secondary reaction unit 24 transfers cuvette C from which the R3 reagent has been dispensed to the secondary BF separation table 26.

[0030] The secondary BF separation unit 27 has the same configuration as the primary BF separation unit 22 and performs a secondary BF separation process to remove the R3 reagent containing unreacted labeled antibody from the sample in the cuvette C of the secondary BF separation table 26. The container transfer unit 24b of the secondary reaction unit 24 transfers the cuvette C on the secondary BF separation table 26, which has been processed by the secondary BF separation unit 27, back to the holding unit 24c of the secondary reaction unit 24.

[0031] The R4 reagent dispensing section 28 and the R5 reagent dispensing section 29 supply the R4 reagent and the R5 reagent, respectively, to the cuvette C on the secondary reaction section 24 by moving their nozzles parallel and vertically.

[0032] The secondary reaction unit 24 drives the secondary reaction table 24a to rotate and transfer the cuvette C in the holding unit 24c, stirring the sample and reagents R1 to R5 in the cuvette C. Subsequently, in the secondary reaction unit 24, a fourth reaction process is performed in which the sample and reagents R1 to R5 in the cuvette C are heated to a predetermined temperature for a predetermined time. As a result, reagent R3, which has a labeled antibody, reacts with the protein in the sample within the cuvette C, and reagent R5, which has a luminescent substrate, reacts with the labeled antibody of reagent R3.

[0033] The detection unit 30 includes a transfer mechanism 30a that transfers the cuvette C held in the holding section 24c of the secondary reaction unit 24 to the detection unit 30. The detection unit 30 detects the light generated during the reaction process between the labeled antibody bound to the protein of the sample and the luminescent substrate using a photodetector such as a photomultiplier tube.

[0034] Used cuvettes C are discarded into the waste hole 31 by the transfer mechanism 30a of the detection unit 30.

[0035] The temperature sensor 32 is installed inside the housing 2a of the measuring device 2, near the detection unit 30. The temperature sensor 32 is, for example, a thermistor. The temperature sensor 32 detects the temperature inside the housing 2a of the measuring device 2. The temperature detected by the temperature sensor 32 is used to determine whether the ambient temperature inside the measuring device 2 is suitable for measurement.

[0036] Figure 2 is a block diagram showing the functional configuration of the control device 3.

[0037] The control device 3 comprises a control unit 101, a storage unit 102, a display unit 103, an input unit 104, and a communication unit 105.

[0038] The control unit 101 is composed of, for example, a CPU. The control unit 101 performs sample analysis and controls the measuring device 2 and the control device 3 by executing program 102a stored in the storage unit 102. The storage unit 102 is composed of, for example, an HDD or SSD. The storage unit 102 stores program 102a, a reference value database DB1, and a measurement result database DB2. Program 102a is a computer program. The reference value database DB1 and the measurement result database DB2 will be explained later with reference to Figure 7.

[0039] The display unit 103 is composed of, for example, a liquid crystal display or an organic EL display. The input unit 104 is composed of, for example, a mouse or a keyboard. The input unit 104 accepts operations from the operator. The display unit 103 and the input unit 104 may be integrated, such as a touch panel type display. The communication unit 105 is, for example, a network card. The control unit 101 controls each part of the measuring device 2 and receives signals from the measuring device 2 via the communication unit 105.

[0040] The control unit 101 controls each part of the measuring device 2 via the communication unit 105 so that the operation described with reference to Figure 1 is performed for each of the multiple measurement items. As a result, the measuring device 2 dispenses the sample in one container T into cuvettes C according to the number of measurement items set for that sample. The measuring device 2 dispenses reagents according to the measurement items into cuvettes C to prepare a measurement sample for each measurement item as described above, and the detection unit 30 detects the light emitted from the measurement sample. The control unit 101 converts the amount of light detected by the detection unit 30 into a measurement value, which is the concentration of the protein to be measured.

[0041] Examples of dementia-related molecules associated with the onset and progression of dementia include β-amyloid (Aβ) protein, tau protein, phosphorylated tau (p-Tau) protein, neurofilament L (NfL) protein, α-synuclein protein, β-synuclein protein, TAR DNA-binding protein 43 (TDP-43), glial fibrillary acidic protein (GFAP), soluble TREM2 (sTREM2), neuron-pentraxin 2 (NPTX-2), chitinase-like protein 1 (YKL-40), and aggregates or peptide fragments thereof. These dementia-related molecules can be used as biomarkers for dementia. The sample analyzer 1 measures these dementia-related molecules as analytes related to dementia biomarkers and obtains measurement values. The measurement values ​​may be values ​​indicating the concentration of the analytes, or calculated values ​​calculated from multiple measurement values ​​corresponding to each of the multiple analytes.

[0042] The measurement items in this embodiment are, for example, Aβ40, Aβ42, Aβ42 / 40, Tau, p-Tau217, p-Tau181, p-Tau217 / Tau, p-Tau217 / Aβ42, p-Tau181 / Tau, p-Tau181 / Aβ42, and Tau212-221.

[0043] Furthermore, the sample analyzer 1 may be capable of measuring molecules other than the analytes related to the biomarkers of dementia. For example, it may be capable of measuring HBsAg, HBsAb, HBeAb, TSH, FT3, FT4, PSA, AFP, CEA, HBeAb, HBcAb, HCVAb, HIVAb, HTLV-I, TPAb, CA125, CA19-9, TM, TAT, PIC, tPAI-C, FRN, Insulin, and HIVAg+Ab.

[0044] The measured values ​​for Aβ40 and Aβ42 represent β-amyloid (Aβ) protein, the measured values ​​for Tau and Tau212-221 represent tau protein, and the measured values ​​for p-Tau217 and p-Tau181 represent phosphorylated tau (p-Tau) protein.

[0045] The measured values ​​for the measurement items Aβ42 / 40, p-Tau217 / Tau, p-Tau217 / Aβ42, p-Tau181 / Tau, and p-Tau181 / Aβ42 are calculated values ​​derived from multiple measured values ​​corresponding to each of the multiple aforementioned analytes. Specifically, the measured value for measurement item Aβ42 / 40 is obtained by dividing the measured value of measurement item Aβ42 by the measured value of measurement item Aβ40. The measured values ​​for p-Tau217 / Tau and p-Tau181 / Tau are obtained by dividing the measured values ​​of measurement items p-Tau217 and p-Tau181 by the measured value of measurement item Tau, respectively. The measured values ​​for p-Tau217 / Aβ42 and p-Tau181 / Aβ42 are obtained by dividing the measured values ​​of measurement items p-Tau217 and p-Tau181 by the measured value of measurement item Aβ42, respectively.

[0046] Furthermore, the calculated value obtained by combining the measured values ​​of multiple dementia-related molecules is not limited to the sum obtained by the ratio of the measured values ​​of multiple dementia-related molecules as described above, but may also be a sum obtained by weighting the measured values ​​of multiple dementia-related molecules. The weighted sum is, for example, obtained by adjusting the weight for each measured value of multiple dementia-related molecules and then calculating the ratio of the adjusted measured values.

[0047] Figure 3 shows the configuration of the measurement order registration screen 200 for operators to register measurement orders.

[0048] The measurement order registration screen 200 is displayed on the display unit 103 when the operator inputs a display instruction via the input unit 104. The measurement order registration screen 200 includes a measurement item display area 210, an OK button 201, and a Cancel button 202. The measurement item display area 210 displays the names of multiple measurement items, and a checkbox 211 is placed corresponding to each measurement item.

[0049] When the operator operates the input unit 104 to check the checkbox 211 and then presses the OK button 201, the control unit 101 stores the measurement items corresponding to the checked checkbox 211 in the storage unit 102 as a measurement order for the target sample. When the operator presses the cancel button 202, the checked state of checkbox 211 is discarded, and the measurement order registration screen 200 is closed.

[0050] Furthermore, the measurement order is not limited to being registered by an operator in the control device 3; it may also be obtained from a host computer located separately from the sample analyzer 1 and stored in the storage unit 102.

[0051] Figure 4 shows the configuration of the job list screen 300 for displaying the job list.

[0052] The job list screen 300 is displayed on the display unit 103 when the operator inputs a display instruction via the input unit 104. The job list screen 300 comprises a job list display area 310, a detail display area 320, a specimen information display area 330, a subject information display area 340, and a comment display area 350.

[0053] The job list display area 310 is an area that displays jobs, including measurement orders and various information associated with sample numbers, and the measured values ​​obtained as a result of the measurement, line by line. The job list display area 310 displays progress, measurement date and time, sample number, and information corresponding to multiple measurement items. The progress item shows the status of the job, such as "Pending" indicating that the measurement has not yet started, "Processing" indicating that the measurement is in progress, "Reported" indicating that the measurement is completed, "Validated" indicating that the measured values ​​have been approved, "Review" indicating that the measured values ​​need to be checked, and "Error" indicating that the measurement has failed. The sample number is a number that can individually identify the sample contained in container T.

[0054] In the job list display area 310, a check mark is displayed next to the information corresponding to the measurement item if measurement of that item is scheduled before measurement. The check marks for measurement items are displayed based on the measurement items pre-set in the measurement order corresponding to the sample using the measurement order registration screen 200 in Figure 3. In addition, after measurement, the measured value of that measurement item is displayed next to the information corresponding to the measurement item in the job list display area 310.

[0055] The operator can select a job corresponding to a row in the job list display area 310 by selecting a row via the input unit 104. In the example shown in Figure 4, the job with sample number N0001 at the top is selected, and this row is highlighted.

[0056] The detailed display area 320 is an area that displays information such as the measured values ​​of each measurement item for the job selected in the job list display area 310. In the example shown in Figure 4, the detailed display area 320 is blank because the measurement device 2 has not yet performed measurements for the job in the selected row.

[0057] The specimen information display area 330 is an area that displays the specimen number and measurement date and time of the job selected in the job list display area 310. The subject information display area 340 is an area that displays the subject ID and subject name of the job selected in the job list display area 310. The comment display area 350 is an area that displays various information about the job selected in the job list display area 310.

[0058] Next, we will explain the relationship between measured values ​​of analytes related to dementia biomarkers (hereinafter sometimes simply referred to as "measured values ​​of dementia biomarkers") and cognitive impairment.

[0059] The measured values ​​of dementia biomarkers are elevated when there is cognitive impairment, or when dementia-related molecules accumulate in the brain above a predetermined concentration (hereinafter, accumulation above a predetermined concentration is simply referred to as "accumulation in the brain"), even before cognitive impairment occurs (low values ​​for dementia-related molecules that decrease in the brain as the disease progresses). Conversely, low values ​​are observed when there is no cognitive impairment, or when dementia-related molecules do not accumulate in the brain (high values ​​for dementia-related molecules that decrease in the brain as the disease progresses). This allows for the determination of whether or not the subject has cognitive impairment, or whether or not dementia-related molecules are accumulating in the brain.

[0060] However, if the subject has kidney or liver problems, the measured values ​​of the specified dementia biomarker may be elevated (or elevated) regardless of the presence or absence of cognitive impairment or accumulation of dementia-related molecules in the brain. Furthermore, if the sample storage conditions before measurement were not appropriate, the measured values ​​of the specified dementia biomarker may be elevated (or elevated) regardless of the presence or absence of cognitive impairment or accumulation of dementia-related molecules in the brain. Thus, while the measured values ​​of dementia biomarkers may fluctuate due to factors other than dementia, there is a risk that these possibilities may not be considered when reporting the measured values.

[0061] In response, the inventors hypothesized that, assuming no influence from factors other than dementia, the measured values ​​of dementia biomarkers would fall within a certain distribution range, regardless of whether or not dementia was present.

[0062] Figure 5 is a schematic diagram showing the distribution range of measured values ​​for dementia biomarkers as a box plot.

[0063] As shown in Figure 5, assuming no influence from factors other than dementia, the inventors hypothesized that, in the absence of other factors, the measured values ​​of a given dementia biomarker would fall within a distribution range between a reference value X1 and a reference value X2, regardless of whether the subject was diagnosed with cognitive impairment (or a subject in which dementia-related molecules had accumulated in the brain even before cognitive impairment occurred) or whether the subject was diagnosed without cognitive impairment (or a subject in which dementia-related molecules had not accumulated in the brain). The inventors then considered identifying measured values ​​that fell outside this distribution range as outliers and displaying information suggesting that a measured value was identified as an outlier, associated with the measured value.

[0064] This prevents situations where measurement values ​​output from the sample analyzer are reported without considering the possibility of fluctuations due to factors other than dementia. Furthermore, by displaying information suggesting that a measurement value has been identified as an outlier, it becomes possible to understand the possibility that other factors are influencing the measurement value, leading to subsequent actions such as exploring other factors, measuring other dementia markers, or performing other dementia tests. This enables a more careful diagnosis based on comprehensive test results.

[0065] The lower-value reference value X1 and the higher-value reference value X2 are determined statistically from the measured values ​​of a predetermined dementia biomarker from multiple subjects. For example, reference values ​​X1 and X2 are set based on ±2SD or ±3SD relative to the median or mean of the measurement population. Alternatively, reference values ​​X1 and X2 are set based on the quantiles of the measurement population. In this case, for example, the lower-value reference value X1 is set based on the first quartile, and the higher-value reference value X2 is set based on the third quartile.

[0066] Furthermore, the reference values ​​X1 and X2 may be set so that a predetermined percentage (e.g., 60%, 70%, 80%, 90%) of the measured values ​​fall within the median or mean of the measured value population. Alternatively, reference values ​​X1 and X2 may be independently set by the user or vendor based on data from a predetermined cohort study. Additionally, reference values ​​X1 and X2 may be set according to predetermined criteria for dementia biomarkers (such as guidelines or national, regional, or institutional criteria).

[0067] Furthermore, if only measurements that fall below the distribution range are identified as outliers, the distribution range may be defined solely by the lower-value reference value X1. Similarly, if only measurements that fall above the distribution range are identified as outliers, the distribution range may be defined solely by the higher-value reference value X2.

[0068] When setting the reference values ​​X1 and X2 as described above, it is preferable that the subjects used to obtain the measurement data set include at least one of the following: multiple subjects diagnosed as having no cognitive impairment, and multiple subjects diagnosed as having cognitive impairment. Therefore, the subjects used to obtain the measurement data set may consist only of multiple subjects diagnosed as having no cognitive impairment, or only of multiple subjects diagnosed as having cognitive impairment. However, it is more preferable that the subjects used to obtain the measurement data set include both multiple subjects diagnosed as having no cognitive impairment and multiple subjects diagnosed as having cognitive impairment.

[0069] Furthermore, when setting the reference values ​​X1 and X2 as described above, the subjects used to obtain the measurement data set may include at least one of the following: multiple subjects diagnosed as having no cognitive impairment and therefore no dementia-related molecules accumulating in their brains, and multiple subjects in whom dementia-related molecules are accumulating in their brains, regardless of whether they have been diagnosed with cognitive impairment or not. Therefore, the subjects used to obtain the measurement data set may consist only of multiple subjects diagnosed as having no cognitive impairment, or only of multiple subjects in whom dementia-related molecules are accumulating in their brains. However, it is more preferable that the subjects used to obtain the measurement data set include both multiple subjects diagnosed as having no cognitive impairment and multiple subjects in whom dementia-related molecules are accumulating in their brains.

[0070] Figure 6 illustrates an example of how to set the reference values ​​X1 and X2.

[0071] The upper part of Figure 6 shows the group of measurements from subjects diagnosed with cognitive impairment, while the lower part of Figure 6 shows the group of measurements from subjects diagnosed without cognitive impairment.

[0072] In each graph in Figure 6, the vertical axis shows the measured value of Tau212-221, an example of an analyte related to dementia biomarkers, and the horizontal axis shows the estimated glomerular filtration rate (eGFR), an indicator of kidney function. A lower eGFR indicates decreased kidney function, and an eGFR of less than 60 suggests chronic kidney disease. In each graph in Figure 6, the horizontal lines show the reference value X1 on the lower side, the reference value X2 on the higher side, and the median. The median is the middle measurement value of a single group, which combines the measurements of multiple subjects diagnosed with cognitive impairment and the measurements of multiple subjects diagnosed without cognitive impairment. The reference value X1 is the measurement value corresponding to the first quartile + 1.5 × interquartile range in the aforementioned single group. The reference value X2 is the measurement value corresponding to the third quartile + 1.5 × interquartile range in the aforementioned single group.

[0073] In each graph in Figure 6, the measured values ​​enclosed by dashed and dotted lines are identified as outliers that fall outside the distribution range set by reference values ​​X1 and X2. Among the measured values ​​identified as outliers, those enclosed by dashed lines are those of subjects with eGFR values ​​less than 60, suggesting chronic kidney disease. As shown in the lower graph of Figure 6, measured values ​​enclosed by dashed lines are also present in subjects diagnosed as not having cognitive impairment, suggesting that the cause of the high measured values ​​may be renal dysfunction rather than cognitive impairment. The reason why renal dysfunction can lead to high measured values ​​of dementia biomarkers is that dementia-related molecules in the blood are constantly produced and excreted from the body, and when renal function declines, the excretion function decreases, leading to an increase in the concentration of dementia-related molecules in the blood.

[0074] It should be noted that elevated or low levels of dementia biomarkers can also be caused by factors other than impaired kidney function.

[0075] For example, "Associations of liver function with plasma biomarkers for Alzheimer's Disease" (Neurological Sciences (2024) 45:2625-2631) describes that ALP levels, an indicator of liver function, are positively correlated with Aβ42 and Aβ40 levels, which are dementia-related molecules; LDH levels, another indicator of liver function, are positively correlated with p-Tau181 levels, another dementia-related molecule; and AL, A / G, ALT, AST / ALT, and LDH levels, all indicators of liver function, are positively correlated with NfL levels, another dementia-related molecule. Therefore, decreased liver function can also lead to elevated levels of dementia biomarkers. Furthermore, the paper "Confounding factors of Alzheimer's disease plasma biomarkers and their impact on clinical performance" (https: / / doi.org / 10.1002 / alz.12787) describes that creatinine levels and body mass index (BMI) are positively correlated with measurements of dementia-related molecules NfL, GFAP, p-Tau217, and p-Tau181. Therefore, elevated creatinine and BMI may lead to elevated levels of dementia biomarkers.

[0076] Furthermore, the Preanalytical sample handling recommendations for Alzheimer's disease plasma biomarkers (Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring 11 (2019) 291-300) state that the levels of dementia-related molecules Aβ42 and Aβ40 are halved after 24 hours at room temperature. Therefore, if the storage conditions of the sample before measurement are not appropriate, the measured values ​​of dementia biomarkers may be low.

[0077] The upper and lower sections of Figure 7 schematically show the configurations of the reference value database DB1 and the measurement result database DB2, respectively. The reference value database DB1 and the measurement result database DB2 are stored in the storage unit 102 of the control device 3, as shown in Figure 2.

[0078] As shown in the upper part of Figure 7, the reference value database DB1 stores a reference value X1 for the lower limit (low value side) of the distribution range and a reference value X2 for the upper limit (high value side) for each measurement item. In this embodiment, reference values ​​X1 and X2 are stored for the measurement items Aβ40, Aβ42, Aβ42 / 40, Tau, p-Tau217, p-Tau181, p-Tau217 / Tau, p-Tau217 / Aβ42, p-Tau181 / Tau, p-Tau181 / Aβ42, and Tau212-221, respectively. The reference values ​​X1 and X2 for each measurement item are set statistically from a group of measured values ​​of the analyte for each measurement item, as explained with reference to Figures 5 and 6.

[0079] Furthermore, if the distribution range of a measurement item is defined only by the lower-end reference value X1, only the reference value X1 is stored in association with that measurement item. Similarly, if the distribution range of a measurement item is defined only by the higher-end reference value X2, only the reference value X2 is stored in association with that measurement item.

[0080] As shown in the lower part of Figure 7, the measurement results database DB2 stores the measured value and flag for each measurement item, associated with the sample number. The flag indicates whether the corresponding measured value is an outlier. In this embodiment, for measurement items included in the reference value database DB1, if the measured value falls within the distribution range defined by reference values ​​X1 and X2, that is, if it is greater than or equal to reference value X1 and less than or equal to reference value X2, the flag for that measurement item in the measurement results database DB2 is set to "0". On the other hand, for measurement items included in the reference value database DB1, if the measured value does not fall within the distribution range defined by reference values ​​X1 and X2, that is, if it is less than reference value X1 or greater than reference value X2, the flag for that measurement item in the measurement results database DB2 is set to "1". For measurement items not included in the reference value database DB1, the flag for that measurement item in the measurement results database DB2 is left blank.

[0081] Figures 8 and 9 show the configurations of the reference value setting screens 410 and 420, respectively, which are used by operators to set reference values.

[0082] As shown in Figure 8, the reference value setting screen 410 is displayed on the display unit 103 when the operator inputs a display instruction via the input unit 104. The reference value setting screen 410 includes a measurement item selection area 411 and a detailed setting button 412. The measurement item selection area 411 contains blocks 411a corresponding to all dementia biomarker measurement items that can be measured and analyzed by the sample analyzer 1. The name of the measurement item is displayed on block 411a.

[0083] The operator can select a measurement item corresponding to a block 411a by performing an operation to select a block 411a via the input unit 104. In the example shown in Figure 8, block 411a for measurement item Aβ42 is selected, and this block 411a is highlighted. When the operator operates the detailed setting button 412 with block 411a selected, the reference value setting screen 420 shown in Figure 9 is displayed for the measurement item corresponding to the selected block 411a.

[0084] As shown in Figure 9, the reference value setting screen 420 includes a measurement item display area 421, a distribution range setting area 422, an OK button 423, and a cancel button 424.

[0085] The measurement item display area 421 displays the name of the measurement item being set on the reference value setting screen 420. The distribution range setting area 422 includes a checkbox 422a and text boxes 422b and 422c. When the operator operates checkbox 422a and checks it, the settings in text boxes 422b and 422c become active. Text boxes 422b and 422c are for setting the lower limit reference value X1 and the upper limit reference value X2 of the distribution range, respectively.

[0086] When the operator presses the OK button 423, if checkbox 422a is checked, the control unit 101 stores the reference values ​​X1 and X2 entered in text boxes 422b and 422c in the reference value database DB1. After that, the control unit 101 closes the reference value setting screen 420. When the operator presses the Cancel button 424, the control unit 101 discards the settings made on the reference value setting screen 420 and closes the reference value setting screen 420.

[0087] Figure 10 shows the configuration of the job list screen 300, which displays jobs after measurement. In this state, the job list screen 300 displays the analysis results of dementia biomarkers. The analysis results of dementia biomarkers include the measured values ​​of the analytes related to the dementia biomarkers, and, if the measured values ​​are determined to be outliers, information associated with the measured values ​​that suggests the measured values ​​may have been influenced by factors other than dementia.

[0088] As exemplified in the first to fourth rows of the job list display area 310 (sample numbers N0001 to N0004), when the measurement corresponding to a job is completed, "Reported" is displayed in the progress display area of ​​the job list display area 310, and the measured value is displayed in the measurement item display area instead of a checkmark. Also, as exemplified in the fifth to eighth rows of the job list display area 310 (sample numbers N0005 to N0008), when the measurement related to a job is in progress, "Processing" is displayed in the progress display area of ​​the job list display area 310. Furthermore, in Figure 10, since the job for sample number N0004 shown in the fourth row is selected, the fourth row of the job list display area 310 is highlighted, the measured values ​​for each measurement item of that job are displayed in the detail display area 320, and the corresponding information is displayed in the sample information display area 330 and the subject information display area 340.

[0089] In the example shown in Figure 10, the measured values ​​of measurement item Aβ42 obtained in the jobs for sample numbers N0002 and N0004 in rows 2 and 4 were not included in the distribution range defined in the reference value database DB1. Therefore, icon M1 is displayed adjacent to the measured value of this measurement item. Icon M1 is information that suggests that the measurement may have been affected by factors other than dementia. Icon M1 is an image and is displayed in association with the measured value that was not included in the distribution range. In addition, the comment display area 350 displays information (string) that suggests that the measured value of measurement item Aβ42 obtained in the job for sample number N0004 in the selected row 4 may have been affected by factors other than dementia. As shown in Figure 10, this string may indirectly suggest that the measured value may have been affected by factors other than dementia by indicating that the measured value is outside the distribution range, or it may directly indicate that the measured value may have been affected by factors other than dementia.

[0090] Next, the processes performed by the sample analyzer 1 will be explained with reference to the flowcharts shown in Figures 11-13.

[0091] Figure 11 is a flowchart showing the processing performed by the sample analyzer 1. The processing in Figure 11 is performed by the control unit 101 executing program 102a (see Figure 2).

[0092] In step S1, the control unit 101 receives a measurement order via the measurement order registration screen 200 shown in Figure 3, associates it with the sample number, and stores the received measurement order and various information as a single job in the storage unit 102.

[0093] In step S2, the control unit 101 performs measurement processing on the target sample based on the measurement order. Through the measurement processing, the control unit 101 obtains measurement values ​​for the measurement items specified in the measurement order and stores the obtained measurement values ​​in the measurement result database DB2. The measurement processing will be explained later with reference to Figure 12. In step S3, the control unit 101 performs judgment processing based on reference values ​​based on the measurement values ​​of each measurement item obtained in step S2. In the judgment processing based on reference values, the control unit 101 stores a flag (0 or 1) in the measurement result database DB2 indicating whether the measurement value is outside the distribution range. The judgment processing based on reference values ​​will be explained later with reference to Figure 13.

[0094] Subsequently, when the control unit 101 receives a command from the operator to display the job list screen 300, in step S4, it displays the job list screen 300 on the display unit 103. The job list screen 300 displays the analysis results for each job. The analysis results include measured values ​​of dementia biomarkers and judgment results based on those measured values. For measurement items for which a flag of 1 is set in the measurement results database DB2, as shown in Figure 10, an icon M1 is displayed on the job list screen 300 in association with the measurement value, indicating that the measurement value may have been influenced by factors other than dementia.

[0095] In step S4, the output of the analysis results was achieved by displaying the job list screen 300, which includes the analysis results. However, the analysis results may also be printed, transmitted as data to another device, or announced to the operator by sound.

[0096] Figure 12 is a flowchart showing the details of the measurement process.

[0097] The measurement process shown in Figure 12 is performed for each measurement item. That is, once the measurement process starts, steps S101 to S114 are performed in parallel for each measurement item based on one sample in container T. Steps S101 to S112 are performed by the control unit 101 executing program 102a (see Figure 2) to control each part of the measuring device 2. Steps S113 and S114 are also performed by the control unit 101 executing program 102a.

[0098] In step S101, reagent R1 is dispensed into cuvette C set in the holding unit 18c. In step S102, the sample is dispensed into cuvette C. In step S103, the primary reaction unit 18 performs a primary reaction on the sample and reagent R1 in cuvette C. In step S104, reagent R2 is dispensed into cuvette C. In step S105, the primary reaction unit 18 performs a secondary reaction on the sample and reagents R1 and R2 in cuvette C. In step S106, the primary BF separation unit 22 performs a primary BF separation to remove unreacted R1 reagent containing the captured antibody from cuvette C.

[0099] In step S107, reagent R3 is dispensed into cuvette C. In step S108, the secondary reaction unit 24 performs a tertiary reaction on the sample and reagents R1 to R3 in cuvette C. In step S109, the secondary BF separation unit 27 performs a secondary BF separation process to remove the unreacted labeled antibody-containing reagent R3 from cuvette C. In step S110, reagents R4 and R5 are dispensed into cuvette C. In step S111, the secondary reaction unit 24 performs a quaternary reaction on the sample and reagents R1 to R5 in cuvette C. In step S112, the detection unit 30 performs an optical detection process to detect light emitted from the sample and reagents R1 to R5 in cuvette C.

[0100] In step S113, the control unit 101 converts the amount of light detected in step S112 into a measured value (concentration of the protein or peptide being measured). In step S114, the control unit 101 stores the measured value calculated in step S113 in the measurement result database DB2, associating it with the sample number.

[0101] Figure 13 is a flowchart detailing the judgment process based on the reference value. The process shown in Figure 13 is performed by the control unit 101 executing program 102a (see Figure 2).

[0102] The judgment process based on the reference values ​​shown in Figure 13 is performed for all measurement items for which measurement values ​​have been obtained by measuring one sample. That is, the control unit 101 sequentially executes steps S201 to S207 for all measurement items for which measurement values ​​have been obtained based on the measurement order.

[0103] In step S201, the control unit 101 determines whether the target measurement item is a measurement item for which a reference value has been registered in the reference value database DB1. If it is not a measurement item for which a reference value has been registered (S201: NO), the process proceeds to step S208. On the other hand, if it is a measurement item for which a reference value has been registered (S201: YES), in step S202, the control unit 101 reads the reference values ​​X1 and X2 corresponding to the target measurement item from the reference value database DB1 and reads the measured value of the target measurement item from the measurement result database DB2.

[0104] In step S203, the control unit 101 determines whether the measured value is smaller than the reference value X1 or larger than the reference value X2, based on the reference values ​​X1 and X2 read in step S202 and the measured value. If the measured value is smaller than the reference value X1 or larger than the reference value X2 (S203: YES), in step S204, the control unit 101 determines that the measured value is outside the distribution range. Then, in step S205, the control unit 101 stores a flag indicating that this measured value is outside the distribution range, associating it with this measured value. That is, the control unit 101 sets "1" to the flag item corresponding to this measured value in the measurement result database DB2.

[0105] On the other hand, if the measured value is greater than or equal to the reference value X1 and less than or equal to the reference value X2 (S203: NO), in step S206, the control unit 101 determines that the measured value is within the distribution range. Then, in step S207, the control unit 101 stores a flag indicating that this measured value is within the distribution range, associating it with this measured value. That is, the control unit 101 sets "0" to the flag item corresponding to this measured value in the measurement result database DB2.

[0106] In step S208, the control unit 101 determines whether the processing in steps S201 to S207 has been completed for all measurement items for which measurement values ​​have been acquired. If the processing for all measurement items has not been completed (S208: NO), the process returns to step S201. On the other hand, if the processing for all measurement items has been completed (S208: YES), the process shown in Figure 13 is completed.

[0107] <Effects of the specimen analyzer, program, and specimen analysis method according to Embodiment 1> The specimen analyzer 1 comprises a measuring device 2 (measuring unit) that measures analytes related to dementia biomarkers contained in a specimen collected from a subject, a storage unit 102 that stores reference values ​​for identifying outliers that may occur due to factors other than dementia, a control unit 101 that determines whether a measured value is identified as an outlier based on the measured value of the analyte by the measuring device 2 (measuring unit) and the reference value, and generates biomarker analysis results according to the result of the determination, and a display unit 103 (output unit) that outputs the biomarker analysis results.

[0108] In this configuration, the biomarker analysis results are generated based on the determination of whether or not the measured value is identified as an outlier, and these analysis results are displayed on the display unit 103. This allows the operator to easily understand the possibility that other factors may have influenced the measured value by referring to the analysis results.

[0109] Furthermore, understanding the possibility that other factors may be influencing the measurement values, as described above, can serve as a catalyst for exploring other factors, carefully interpreting the analysis results in conjunction with other dementia tests, and taking actions for accurate diagnosis, such as re-collecting and re-examining samples. As a result, the accuracy of dementia diagnosis can be improved.

[0110] As shown in Figure 10, when the control unit 101 identifies a measurement value as an outlier, it generates an analysis result that includes an icon M1 and a string in the comment display area 350 (information suggesting that the measurement value may have been influenced by factors other than dementia).

[0111] With this configuration, the operator can easily understand, based on the outputted analysis results, whether the measured values ​​may have been influenced by factors other than dementia.

[0112] As explained with reference to Figures 5 and 6, the reference values ​​X1 and X2 are values ​​based on the distribution of each measurement obtained from multiple samples, and the multiple samples include multiple samples from individuals with normal cognitive function and / or individuals with cognitive impairment, or multiple samples from individuals in whom dementia-related molecules have accumulated in the brain above a predetermined concentration and / or in whom the accumulation of dementia-related molecules in the brain is below a predetermined concentration.

[0113] According to this configuration, reference values ​​are set based on multiple samples from individuals with normal cognitive function and those with cognitive impairment, or from individuals in whom dementia-related molecules have accumulated in the brain at concentrations exceeding a predetermined level and those in whom the accumulation of dementia-related molecules in the brain is below a predetermined level. By using these set reference values, it is possible to determine with high accuracy whether a measured value is identified as an outlier.

[0114] As explained with reference to Figures 5 and 6, the reference values ​​X1 and X2 are statistical values ​​based on measurements from multiple samples.

[0115] With this configuration, for example, reference values ​​can be set based on upper and lower limits of ±2SD or ±3SD for the median or mean of multiple measurements obtained from multiple samples. Furthermore, with this configuration, reference values ​​can be set based on the quantiles of a population of multiple measurements obtained from multiple samples.

[0116] The reference values ​​may be those set based on guidelines for biomarkers related to dementia.

[0117] This configuration allows for the generation of analysis results that reflect the guidelines, using baseline values ​​set based on those guidelines.

[0118] The reference value includes at least one of two reference values: a reference value X1 (first reference value) corresponding to the lower limit of the distribution range of the measured values, and a reference value X2 (second reference value) corresponding to the upper limit.

[0119] With this configuration, it is easy to determine whether a measured value is identified as an outlier by comparing it with at least one of the reference values ​​X1 and X2.

[0120] Reference value X1 (first reference value) is used to determine whether the measured value is outside the distribution range on the lower side, and reference value X2 (second reference value) is used to determine whether the measured value is outside the distribution range on the higher side.

[0121] This configuration makes it easy to determine whether a measured value falls outside the distribution range on the lower or higher side.

[0122] The measurement values ​​of the analyte obtained by the measuring device 2 include the individual measurement values ​​of multiple dementia biomarkers, and the reference values ​​for identifying outliers that may occur due to factors other than dementia include multiple reference values ​​X1, X2 corresponding to each of the multiple dementia biomarkers.

[0123] With this configuration, for each measurement value, it is possible to determine whether or not each measurement value is an outlier using the corresponding reference value.

[0124] The measurements of multiple dementia biomarkers include multiple measurements corresponding to each of the multiple dementia-related molecules contained in the sample, as well as calculated values ​​derived from these multiple measurements. Specifically, the measurements for the measurement items Aβ40, Aβ42, Tau, p-Tau217, p-Tau181, and Tau212-221 are each measurements of a single dementia-related molecule, while the measurements for the measurement items Aβ42 / 40, p-Tau217 / Tau, p-Tau217 / Aβ42, p-Tau181 / Tau, and p-Tau181 / Aβ42 are each calculated values ​​derived from the measurements of multiple dementia-related molecules.

[0125] This configuration allows for the analysis of samples from various perspectives based on measured and calculated values.

[0126] In the sample analysis method, the analysis of analytes related to dementia biomarkers contained in the sample collected from the subject is measured to obtain measurement values ​​(step S2 in Figure 11), and based on the measurement values ​​and reference values ​​for identifying outliers that may occur due to factors other than dementia, it is determined whether or not the measurement values ​​are identified as outliers (step S3), and the biomarker analysis results according to the result of the determination are output (step S4).

[0127] This method achieves the same effect as the sample analyzer 1 described above.

[0128] Program 102a (see Figure 2), which causes a computer to perform the process of analyzing analytes related to dementia biomarkers, determines whether the measured values ​​are identified as outliers based on the measured values ​​obtained by measuring analytes contained in a sample taken from a subject and reference values ​​for identifying outliers that may occur due to factors other than dementia, and generates biomarker analysis results according to the result of the determination.

[0129] This program achieves the same effect as the sample analyzer 1 described above.

[0130] <Embodiment 2> In Embodiment 1, the flag in the measurement result database DB2 was set to "1" when the measured value was not within the distribution range. In contrast, in this embodiment, the flag in the measurement result database DB2 is set to "1-1" when the measured value is less than the reference value X1, and the flag in the measurement result database DB2 is set to "1-2" when the measured value is greater than the reference value X2.

[0131] Figure 14 is a schematic diagram showing the configuration of the measurement results database DB2.

[0132] In the example shown in Figure 14, the measured value of item Aβ42 in sample number N0002 was smaller than the reference value X1, so the corresponding flag was set to "1-1". Also, the measured value of item Aβ42 in sample number N0004 was larger than the reference value X2, so the corresponding flag was set to "1-2".

[0133] Figure 15 shows the configuration of the job list screen 300, which displays jobs after measurement.

[0134] In the example shown in Figure 15, the measured value of measurement item Aβ42 obtained in the job with sample number N0002 shown in the second row was smaller than the reference value X1 defined in the reference value database DB1 and was not included in the distribution range. Therefore, icon M21 is displayed adjacent to the measured value of this measurement item. Icon M21 is information that suggests the measured value may have been influenced by factors other than dementia. Icon M21 is also information that suggests the measured value is outside the distribution range on the lower side. Icon M21 is an image and is displayed in association with measured values ​​that are outside the distribution range on the lower side.

[0135] Furthermore, the measured value of measurement item Aβ42 obtained in the job for sample number N0004, shown on the fourth line, was greater than the reference value X2 defined in the reference value database DB1 and was not included in the distribution range. Therefore, icon M22 is displayed adjacent to the measured value of this measurement item. Icon M22 is information that suggests the measured value may have been influenced by factors other than dementia. Icon M22 is also information that suggests the measured value is outside the distribution range on the higher side. Icon M22 is an image and is displayed in association with the measured value that is outside the distribution range on the higher side. In addition, the comment display area 350 displays a string of text that suggests the measured value of measurement item Aβ42 for sample number N0004 may have been influenced by factors other than dementia, and that it is outside the distribution range on the higher side.

[0136] Figure 16 is a flowchart showing the details of the judgment process based on the reference value.

[0137] The process in Figure 16 is similar to that of Embodiment 1 shown in Figure 13, but with the addition of steps S211 to S216, replacing steps S203 to S205. The processes added from Embodiment 1 will be described below.

[0138] In step S211, the control unit 101 determines whether the measured value is smaller than the reference value X1 based on the reference value X1 read in step S202 (see Figure 13) and the measured value. If the measured value is smaller than the reference value X1 (S211: YES), in step S212, the control unit 101 determines that the measured value is outside the distribution range on the lower side. Then, in step S213, the control unit 101 stores a flag indicating that this measured value is outside the distribution range on the lower side, associating it with this measured value. That is, the control unit 101 sets "1-1" to the flag item corresponding to this measured value in the measurement result database DB2.

[0139] If the measured value is greater than or equal to the reference value X1 (S211: NO), in step S214, the control unit 101 determines whether the measured value is greater than the reference value X2 based on the reference value X2 read in step S202 (see Figure 13) and the measured value. If the measured value is greater than the reference value X2 (S214: YES), in step S215, the control unit 101 determines that the measured value is outside the distribution range on the higher side. Then, in step S216, the control unit 101 stores a flag indicating that this measured value is outside the distribution range on the higher side, associating it with this measured value. That is, the control unit 101 sets "1-2" to the flag item corresponding to this measured value in the measurement result database DB2.

[0140] If the measured value is greater than or equal to the reference value X1 and less than or equal to the reference value X2 (S211:NO, S214:NO), the control unit 101 executes steps S206 and S207, as in Figure 13.

[0141] Next, we will provide illustrative examples of what additional tests and diagnoses can be performed in a medical setting when the measured value of the dementia biomarker in the sample analyzer 1 falls below or above the distribution range. Note that the entities performing the tests and diagnoses described below are examples and may vary depending on the medical facility.

[0142] In the following explanation, among several biomarkers for dementia, one biomarker will be referred to as "Biomarker A," and the other biomarkers as "Biomarker B." For example, Biomarker A corresponds to the measurement item Tau212-221, and Biomarker B corresponds to the measurement item Aβ42 / 40. However, Biomarkers A and B are not limited to those mentioned above.

[0143] A laboratory technician or physician refers to the analysis results on the job list screen 300 for the subject in question to check whether the measured value of biomarker A is an outlier on the low or high side. As mentioned above, for example, Aβ42 and Aβ40 are known to have their amounts halved when measured at room temperature after 24 hours, and if the storage conditions of the sample before measurement are not appropriate, the measured value of the dementia biomarker may be low. Therefore, if the measured value of biomarker A is an outlier on the low side, there may have been a problem with the storage conditions of the sample, so a nurse will draw blood again from the subject and a laboratory technician will remeasure using the sample analyzer 1. This helps to prevent false negative results from being reported when cognitive impairment or the accumulation of dementia-related molecules in the brain is not present.

[0144] On the other hand, if the measured value of biomarker A is an outlier on the high side, the laboratory technician or physician will refer to the results of other tests performed on the subject to determine whether or not there is impaired renal function. For example, eGFR calculated from the creatinine level in the blood can be used to determine renal function. If there is impaired renal function, the laboratory technician will additionally measure biomarker B, which is less affected by impaired renal function than biomarker A, using the sample analyzer 1. The laboratory technician or physician will refer to the analysis results on the job list screen 300 to confirm whether or not the measured value of biomarker B is within the negative range. If the measured value of biomarker B is within the negative range, the physician can determine that the outlier on the high side of the measured value of biomarker A is due to impaired renal function, and will diagnose the subject as having no cognitive impairment or no accumulation of dementia-related molecules in the brain. This will help to reduce the need for further tests that are burdensome for the subject.

[0145] Furthermore, even when the measured value of biomarker A is within the distribution range, the laboratory technician may additionally measure biomarker B, which is less affected by renal function decline compared to biomarker A, using the sample analyzer 1 for this subject. The laboratory technician or physician then refers to the analysis results on the job list screen 300 to confirm whether the measured value of biomarker B is within the negative range. If the measured value of biomarker B is within the negative range, the physician diagnoses this subject as having no cognitive impairment or no accumulation of dementia-related molecules in the brain. This helps to reduce the need for further burdensome tests on the subject.

[0146] On the other hand, if the kidney function test shows no signs of impaired kidney function, the laboratory technician or nurse will perform additional tests on the patient, as this may be due to sample storage conditions or other factors besides impaired kidney function. These additional tests may include, for example, PET (positron emission tomography) scans or CSF (cerebrospinal fluid) scans. The physician will then refer to the results of these additional tests to diagnose the presence or absence of underlying pathological causes for cognitive impairment. Similarly, if the measurement of biomarker B is outside the normal range, the laboratory technician or nurse will perform additional tests on the patient, as this may also indicate other contributing factors. These additional tests allow for an accurate diagnosis of dementia in the patient.

[0147] By implementing the diagnostic flow described above, false positives and false negatives in determining the presence or absence of cognitive impairment or dementia-related molecules in the brain can be suppressed compared to determining the presence or absence of cognitive impairment based on whether the measured value of biomarker A is within the normal range. This allows for a proper determination of whether additional tests are necessary and enables a more accurate diagnosis of the presence or absence of cognitive impairment or dementia-related molecules in the brain.

[0148] <Effects of the specimen analyzer, program, and specimen analysis method according to Embodiment 2> The control unit 101 determines, based on the reference value X1 (first reference value) and reference value X2 (second reference value), whether the measured value identified as an outlier is outside the distribution range on the lower side or on the higher side (steps S211 and S214 in Figure 16). The display unit 103 (output unit) outputs an analysis result, including either icon M21 (information suggesting that the measured value may have been influenced by factors other than dementia and is outside the distribution range on the lower side) or icon M22 (information suggesting that the measured value may have been influenced by factors other than dementia and is outside the distribution range on the higher side), as shown in Figure 15 (step S11 in Figure 11).

[0149] This configuration allows the operator to easily determine whether the measured value falls below or above the distribution range.

[0150] <Embodiment 3> In Embodiments 1 and 2, it was determined whether the measured value of the dementia biomarker was within the distribution range. In contrast, in this embodiment, it is further determined whether the measured value of the dementia biomarker is within the measurement range, and if the measured value is outside the measurement range, the flag "2" is set in the measurement result database DB2.

[0151] Figure 17 schematically shows the distribution range and measurement range. Figure 17 shows the distribution range for each group of measured values ​​shown in Figure 5, along with the measurement range.

[0152] The measurement range is the range in which the sample analyzer 1 can ensure the reliability of the measurement values, or in other words, the range in which the measurement values ​​can be considered accurate. The measurement range is defined by range values ​​Y1 and Y2, which are the lower and upper limits of the measurement range, respectively. Typically, range value Y1 is smaller than the reference value X1, and range value Y2 is larger than the reference value X2. The range values ​​Y1 and Y2 of the measurement range are set by the operator via the input unit 104 for each dementia biomarker and are stored in the memory unit 102 beforehand.

[0153] The measurement range may be set as the recommended values ​​from the manufacturer or vendor of the sample analyzer 1 and the reagents used in the sample analyzer 1. The measurement range may also be set by the lower and upper limits of the measured values ​​obtained when the sample analyzer 1 is properly operated.

[0154] Figure 18 is a schematic diagram showing the configuration of the measurement results database DB2.

[0155] In the example shown in Figure 18, the measured value of item Aβ42 in sample number N0001 was outside the measurement range, so the corresponding flag was set to "2".

[0156] Figure 19 shows the configuration of the job list screen 300, which displays jobs after measurement.

[0157] In the example shown in Figure 19, the measured value of measurement item Aβ42 obtained by the job with sample number N0001, shown in the first selected row, was outside the measurement range for that measurement item (range Y1 or greater and range Y2 or less). Therefore, the string M3 is displayed next to the measured value of this measurement item. The string M3 indicates that the measured value is outside the measurement range that can guarantee the reliability of the measurement. The string M3 is, for example, [+++]. In addition, the comment display area 350 displays a string indicating that the measured value of measurement item Aβ42 for sample number N0001 is outside the measurement range. Alternatively, instead of the string M3, an icon image indicating that the measurement is outside the measurement range may be displayed along with the measured value.

[0158] Figure 20 is a flowchart showing the details of the judgment process based on the reference value.

[0159] The process in Figure 20 is similar to that of Embodiment 1 shown in Figure 13, with steps S221 to S224 added in place of step S202. Furthermore, if the determination in step S222 is NO, steps S211 to S216, S206, and S207 of Embodiment 2 shown in Figure 16 are executed. The processes added from Embodiments 1 and 2 will be described below.

[0160] In step S221, the control unit 101 reads reference values ​​X1 and X2 corresponding to the target measurement item from the reference value database DB1, and reads the measured value of the target measurement item from the measurement result database DB2, similar to step S202 in Embodiment 1. Furthermore, in step S221, the control unit 101 reads range values ​​Y1 and Y2 of the measurement range corresponding to the target measurement item from the storage unit 102.

[0161] In step S222, the control unit 101 determines whether the measured value is less than range value Y1 or greater than range value Y2, based on the measured value and range values ​​Y1 and Y2 read in step S221. If the measured value is less than range value Y1 or greater than range value Y2 (S222: YES), in step S223, the control unit 101 determines that the measured value is outside the measurement range. Then, in step S224, the control unit 101 stores a flag indicating that this measured value is outside the measurement range, associating it with this measured value. That is, the control unit 101 sets "2" to the flag item corresponding to this measured value in the measurement result database DB2.

[0162] If the flag item corresponding to the measurement value is set to "2", when the measurement value is displayed on the job list screen 300, the string "M3" will be displayed in the display area of ​​the measurement value, as shown in Figure 19, and a string indicating that the measurement value is outside the measurement range will be displayed in the comment display area 350.

[0163] On the other hand, if the measured value is greater than or equal to range value Y1 and less than or equal to range value Y2 (S222:NO), steps S211 to S216, S206, and S207 of Embodiment 2 shown in Figure 16 are executed.

[0164] <Effects of the sample analyzer, program, and sample analysis method according to Embodiment 3> The control unit 101 further determines whether the measured value is outside the measurement range based on the range values ​​Y1 and Y2, which are used to identify a measurement range that can ensure the reliability of the measured value (step S222 in Figure 20), and the display unit 103 (output unit) outputs the biomarker analysis results as shown in Figure 19, according to the first determination result based on the reference values ​​X1 and X2 and the second determination result based on the range values ​​Y1 and Y2 (step S11 in Figure 11).

[0165] This configuration allows for the determination of whether a measured value is identified as an outlier, as well as whether the measured value is within a reliable measurement range. This enables the operator to refer to the job list screen 300, which includes the analysis results as shown in Figure 19, and determine whether it can be used in post-analysis review. For example, if the string M3 is displayed, the operator can understand that the judgment results based on the reference values ​​X1 and X2 are unreliable.

[0166] As shown in Figure 19, if the control unit 101 determines that the measured value is outside the measurement range, it generates an analysis result that includes the string M3 (information suggesting that the measured value is outside the measurement range).

[0167] With this configuration, the operator can easily determine, based on the outputted analysis results, if the measured value is outside the measurement range.

[0168] <Embodiment 4> In Embodiment 1, if the measured value of a dementia biomarker fell outside the distribution range, the flag in the measurement results database DB2 was set to "1" only for that measured value. In contrast, in this embodiment, if the measured value of a dementia biomarker falls outside the distribution range, the flag in the measurement results database DB2 is also set to "1" for the measured values ​​of other dementia biomarkers corresponding to that measured value.

[0169] Figure 21 is a schematic diagram showing the configuration of the measurement item database DB3. The measurement item database DB3 is stored in the storage unit 102 of the control device 3.

[0170] The measurement item database DB3 stores a dementia marker flag and a calculated value flag for each measurement item. The measurement item database DB3 stores all measurement items that can be measured by the sample analyzer 1.

[0171] The dementia marker flag stores "1" if the corresponding measurement item is a dementia biomarker, and "0" if it is not a dementia biomarker. The calculated value flag stores "C" if the corresponding measurement item is a dementia biomarker and is calculated from the measured values ​​of multiple other measurement items. When "C" is stored in the calculated value flag, the names of the other measurement items used to calculate the measured value of that measurement item are also stored. The dementia marker flag and the calculated value flag are set in advance by the manufacturer or operator of the sample analyzer 1.

[0172] Furthermore, the measurement item database DB3 does not necessarily need to store a dementia marker flag for each measurement item but not a calculated value flag, or it does not necessarily need to store a calculated value flag for each measurement item but not a dementia marker flag.

[0173] <Embodiment 4-1> In this embodiment, if the measured value of any dementia biomarker falls outside the distribution range, a flag of 1 is set in the measurement result database DB2 for all other dementia biomarker measured values ​​as well. If the measured value of any of the dementia biomarker measured items falls outside the distribution range, the control unit 101 refers to the measurement item database DB3 and outputs the measured values ​​of all measured items for which the dementia marker flag is set to "1", along with information (icons M1, M21, etc.) suggesting that the measured value may have been influenced by factors other than dementia.

[0174] In the example shown in Figure 22, the measured value of measurement item Aβ40 obtained in the job with sample number N0004 shown in the fourth row did not fall within the distribution range defined in the reference value database DB1. In this case, the control unit 101 refers to the measurement item database DB3 and displays icon M1, regardless of whether the measured values ​​of all measurement items for which the dementia marker flag is set to "1" fall within the distribution range defined in the reference value database DB1.

[0175] <Embodiment 4-2> In this embodiment, if the measured value of any dementia biomarker falls outside the distribution range, a flag of 1 is set in the measurement result database DB2 for the measured value of the dementia biomarker calculated using that measured value. If the measured value of any of the dementia biomarker measurement items (first measurement item) falls outside the distribution range, the control unit 101 refers to the measurement item database DB3 and determines whether the measurement item (second measurement item) stored with the calculated value flag "C" matches the measured value of the first measurement item whose measured value fell outside the distribution range. If the two measurement items match, the control unit 101 outputs the measured value of the first measurement item whose measured value fell outside the distribution range, the measured value of the second measurement item whose corresponding calculated flag is "C", and information (icons M1, M21, etc.) suggesting that the measured value may have been influenced by factors other than dementia.

[0176] In the example shown in Figure 23, the measured value of measurement item Aβ42 obtained in the job with sample number N0004 shown in the fourth row was not included in the distribution range defined in the reference value database DB1. In this case, the control unit 101 refers to the measurement item database DB3 and associates the measured value of measurement item Aβ42 with the measured values ​​of measurement items Aβ42 / 40, p-Tau217 / Aβ42, and p-Tau181 / Aβ42, in which measurement item Aβ42 is stored along with the calculated value flag "C". Regardless of whether the measured values ​​of measurement items Aβ42 / 40, p-Tau217 / Aβ42, and p-Tau181 / Aβ42 are included in the distribution range defined in the reference value database DB1, the control unit 101 displays icon M1.

[0177] The control unit 101 may also accept a setting to output the analysis results by referring to the dementia marker flag or by referring to the calculated value marker (i.e., a setting to output the analysis results exemplified in Figure 22 or the analysis results exemplified in Figure 23).

[0178] Furthermore, in Figures 22 and 23, the comment display area 350 displays a string indicating that the measured value is outside the distribution range only for measurement items where the measured value was actually determined to be outside the distribution range. However, for related measurement items, a string such as "Please check the measured value." may also be displayed in the comment display area 350.

[0179] <Effects of the specimen analyzer, program, and specimen analysis method according to Embodiment 4> If the display unit 103 (output unit) determines that the measured value is identified as an outlier in the determination result of at least one of the multiple dementia biomarkers, it outputs the analysis results for the other biomarkers as well, regardless of the determination result, including the icon M1 for the other biomarkers (information suggesting that the measured value may have been influenced by factors other than dementia), as shown in Figures 22 and 23.

[0180] If a measurement value for one biomarker is identified as an outlier, other factors may be influencing the analysis results of other biomarkers. In such cases, the above configuration indicates the possibility that the measurement values ​​for other biomarkers may also be outliers, thus preventing the situation where the measurement values ​​of other biomarkers output from the sample analyzer 1 are reported without modification.

[0181] <Embodiment 5> As described above, if the subject has kidney or liver function problems, or if the sample storage conditions before measurement were not appropriate, the measured values ​​of predetermined biomarkers for dementia will fluctuate regardless of whether there is cognitive impairment or accumulation of dementia-related molecules in the brain. In contrast, in this embodiment, the sample analyzer 1 is configured to display information such as the subject's medical history and sample storage conditions when displaying the analysis results. This makes it possible for the operator to infer the factors causing the fluctuations in the measured values.

[0182] Figure 24 shows the configurations of the clinical information input screen 510 and the specimen information input screen 520, respectively.

[0183] The clinical information input screen 510 and the specimen information input screen 520 are displayed on the display unit 103 when the operator inputs a display instruction via the input unit 104. The operator performs the input using the clinical information input screen 510 and the specimen information input screen 520 before or after the measurement of the specimen.

[0184] As shown on the left side of Figure 24, the clinical information input screen 510 includes text boxes 511 into which the subject ID, subject name, subject medical history, subject's underlying disease name, and subject measurement data related to disease indicators other than dementia can be entered. Disease indicators other than dementia include, for example, creatinine and GFR related to renal function, and CK-MB related to heart disease. The operator enters the information into each text box 511 via the input unit 104.

[0185] The clinical information input screen 510 also includes an OK button 512 and a Cancel button 513. When the operator presses the OK button 512, the control unit 101 stores the information entered in each text box 511 of the clinical information input screen 510 in the storage unit 102. When the operator presses the Cancel button 513, the information entered in each text box 511 is discarded.

[0186] As shown on the right side of Figure 24, the specimen information input screen 520 includes text boxes 521 for inputting the specimen number, the facility from which the specimen was collected, the facility from which the specimen was processed, the date and time the specimen was collected, the date and time the specimen was processed, the specimen processing conditions, and the specimen storage conditions. The operator inputs the information into each text box 521 via the input unit 104. In addition to the facility name and condition details being entered as strings for the collection facility, processing facility, processing conditions, and storage conditions, a facility ID that can identify the facility and a condition ID that can identify the condition details may also be entered.

[0187] The sample information input screen 520 also includes an OK button 522 and a Cancel button 523. When the operator presses the OK button 522, the control unit 101 stores the information entered in each text box 521 of the sample information input screen 520 in the storage unit 102. When the operator presses the Cancel button 523, the information entered in each text box 521 is discarded.

[0188] Figure 25 shows the configuration of the job list screen 300, which displays jobs after measurement.

[0189] In Figure 25, compared to Embodiment 1 in Figure 10, the job list screen 300 includes a detailed information button 360. When an operator wants to view detailed information, they select the job to be viewed in the job list display area 310 via the input unit 104 and operate the detailed information button 360. As a result, the control unit 101 displays the detailed information screen 530 shown in Figure 26 on the display unit 103.

[0190] Figure 26 shows the configuration of the detailed information screen 530.

[0191] The detailed information screen 530 includes a clinical information display area 531, a specimen information display area 532, and a close button 533.

[0192] The clinical information display area 531 displays the subject ID, subject name, subject medical history, subject's underlying disease name, and subject measurement data related to disease indicators other than dementia. The specimen information display area 532 displays the facility where the specimen was collected, the facility where the specimen was processed, the date and time the specimen was collected, the date and time the specimen was processed, the specimen processing conditions, and the specimen storage conditions. The content displayed in the clinical information display area 531 and the specimen information display area 532 is displayed based on information entered via the clinical information input screen 510 and specimen information input screen 520 shown in Figure 24 and stored in the storage unit 102. By referring to the content displayed on the detailed information screen 530, the operator can smoothly grasp the clinical information and specimen information of the target specimen. When the operator operates the close button 533, the detailed information screen 530 is closed.

[0193] Furthermore, clinical information and specimen information are not limited to being displayed on a detailed information screen 530 separate from the job list screen 300, as shown in Figure 26; they may also be displayed on the job list screen 300 along with the analysis results.

[0194] Furthermore, the clinical information input screen 510 in Figure 24 may include text boxes for inputting age and MMSE (Mini-Mental State Examination) test results. In this case, this information will be displayed in the clinical information display area 531 of the detailed information screen 530 in Figure 26.

[0195] Furthermore, the sample information input screen 520 in Figure 24 may include text boxes for inputting operator information, blood collector information, and temperature conditions within the measuring device 2 based on the temperature detected by the temperature sensor 32. In this case, this information will be displayed in the sample information display area 532 of the detailed information screen 530 in Figure 26.

[0196] <Effects of the specimen analyzer, program, and specimen analysis method according to Embodiment 5> As shown in Figure 26, the display unit 103 (output unit) further outputs the subject's clinical information and / or information related to the handling of the specimen.

[0197] This configuration allows the operator to consider other factors that may be influencing the measurement.

[0198] As shown in the clinical information display area 531 of Figure 26, the clinical information includes the subject's medical history, the name of the underlying disease, and at least one measurement data related to disease indicators other than dementia.

[0199] This configuration allows the operator to easily consider whether other factors influencing the measurement are related to the subject's illness or other conditions.

[0200] As shown in the specimen information display area 532 of Figure 26, the information related to the handling of the specimen includes at least one of the following: facility information where the specimen was collected and / or processed, date of specimen collection and / or processing, time of specimen collection and / or processing, and information regarding the processing conditions and / or storage conditions of the specimen.

[0201] This configuration allows for a smooth examination of whether other factors influencing the measurement are related to sample handling.

[0202] <Embodiment 6> In embodiments 1 to 5, each reference value in the reference value database DB1 was used commonly for all specimens. In contrast, in this embodiment, each reference value in the reference value database DB1 is set for each medical facility, and the corresponding reference value is used for specimens obtained at each medical facility.

[0203] Figure 27 is a schematic diagram showing the relationship between testing center 4 and facility 5.

[0204] The testing center 4 is equipped with a sample analyzer 1 according to one of the embodiments 1 to 5. Facility 5 is, for example, a medical facility such as a hospital, where samples are collected from subjects. The samples collected at facility 5 are transported to the testing center 4 and measured and analyzed by the sample analyzer 1 at the testing center 4. In this case, each sample collected at facility 5 is assigned a facility number along with a sample number.

[0205] Figure 28 is a schematic diagram showing the configuration of the reference value database DB1.

[0206] In this embodiment, the reference value database DB1 has an additional item for facility number compared to the reference value database DB1 of Embodiment 1 shown in Figure 7.

[0207] In this embodiment, the reference value database DB1 stores reference values ​​X1 and X2 associated with the measurement item and facility number. As a result, the control unit 101 of the sample analyzer 1 reads the reference values ​​X1 and X2 for the corresponding measurement item and facility number from the reference value database DB1 during the judgment process based on the reference values, and determines whether the measured value is within the distribution range.

[0208] <Effects of the sample analyzer, program, and sample analysis method according to Embodiment 6> The standard value is a value set for each of the 5 facilities.

[0209] This configuration allows for the determination of whether a measured value is identified as an outlier using reference values ​​corresponding to the operational policies of each of the five facilities.

[0210] <Embodiment 7> In embodiments 1 to 6, as shown in Figure 2, the reference value database DB1 was stored in the storage unit 102 of the control device 3. In contrast, in this embodiment, the reference value database DB1 is stored in a server 6 outside the sample analyzer 1.

[0211] Figure 29 is a block diagram showing the functional configuration of the sample analysis system 7.

[0212] The sample analysis system 7 comprises a sample analyzer 1 according to any of embodiments 1 to 6, and a server 6 for managing reference values. The sample analyzer 1 and the server 6 are connected via a communication network N.

[0213] Server 6 comprises a control unit 601, a storage unit 602, and a communication unit 603. The control unit 601 is composed of, for example, a CPU. The storage unit 602 is composed of, for example, an HDD or SSD. The storage unit 602 stores the reference value database DB1 shown in Figure 7. The communication unit 603 is, for example, a network card.

[0214] The control unit 101 of the sample analyzer 1 sends a request to the server 6 to transmit a reference value in step S202 of Figure 13 or step S221 of Figure 20. When the control unit 601 of the server 6 receives the request to transmit a reference value from the sample analyzer 1 via the communication unit 603, it reads the reference value corresponding to the transmission request from the reference value database DB1 and transmits the read reference value to the sample analyzer 1 via the communication unit 603. As a result, the control unit 101 of the sample analyzer 1 obtains the reference value.

[0215] Furthermore, instead of storing the reference value itself as reference value information, server 6 may store information necessary to calculate the reference value. For example, server 6 may store multiple measurement values ​​of a predetermined dementia biomarker as reference value information, or it may store statistical values ​​(such as median or mean) calculated from multiple measurement values. In this case, the control unit 101 of the sample analyzer 1 calculates the reference value based on the received reference value information.

[0216] Furthermore, the measurement range of Embodiment 3 and the measurement item database of Embodiment 4 may also be stored in the storage unit 602 of the server 6 instead of the storage unit 102 of the control device 3.

[0217] <Effects of the sample analysis system, program, and sample analysis method according to Embodiment 7> The specimen analysis system 7 includes a specimen analyzer 1 equipped with a control unit 101 and a display unit 103 (output unit) (see Figure 2) that measures analytes related to dementia biomarkers contained in specimens collected from subjects, and a server 6 connected to the specimen analyzer 1 via a communication network N that stores reference value information for identifying outliers that may occur due to factors other than dementia. The control unit 101 determines whether a measured value is identified as an outlier based on the measured value of the analyte and a reference value set based on the reference value information, generates biomarker analysis results according to the result of the determination, and the display unit 103 (output unit) outputs the biomarker analysis results.

[0218] This configuration provides the same effects as embodiments 1 to 6. Furthermore, since the sample analyzer 1 does not need to manage reference value information, the processing and configuration of the sample analyzer 1 can be simplified.

[0219] Embodiments of the present invention can be modified in various ways as appropriate within the scope of the technical idea set forth in the claims. [Explanation of symbols]

[0220] 1. Sample analyzer 2. Measuring device (measuring unit) 5 facilities 6 servers 7. Sample Analysis System 101 Control Unit 102 Storage section 102a Program 103 Display Unit (Output Unit) 602 Storage section M1, M21, M22 Icons (Information, Image Information) N Communication Network

Claims

1. A measurement unit that measures analytes related to dementia biomarkers contained in a sample taken from a subject, A memory unit that stores reference values ​​for identifying outliers that may occur due to factors other than dementia, A control unit that determines whether the measured value is identified as an outlier based on the measured value of the analyte by the measuring unit and the reference value, and generates the analysis result of the biomarker according to the result of the determination, The system includes an output unit that outputs the analysis results of the biomarker. Sample analysis device.

2. The specimen analyzer according to claim 1, wherein the control unit, when it identifies the measured value as an outlier, generates the analysis result which includes information suggesting that the measured value may have been influenced by factors other than dementia.

3. The control unit further determines whether the measured value falls outside the measurement range based on a range value for identifying a measurement range that can ensure the reliability of the measured value. The specimen analyzer according to claim 1, wherein the output unit outputs the analysis results of the biomarker according to the first determination result based on the reference value and the second determination result based on the range value.

4. The specimen analyzer according to claim 3, wherein the control unit determines that the measured value is outside the measurement range, and generates the analysis result including information indicating that the measured value is outside the measurement range.

5. The aforementioned reference value is a value based on the distribution of the respective measured values ​​obtained from multiple samples. The specimen analyzer according to claim 1, wherein the plurality of specimens include a plurality of specimens from persons with normal cognitive function and / or persons with cognitive impairment, or a plurality of specimens from persons in whom dementia-related molecules have accumulated in the brain above a predetermined concentration and / or in whom the accumulation of dementia-related molecules in the brain is below the predetermined concentration.

6. The specimen analyzer according to claim 5, wherein the reference value is a statistical value based on the measured values ​​of the plurality of specimens.

7. The specimen analyzer according to claim 1, wherein the reference value is a value set based on the guidelines for biomarkers for dementia.

8. The specimen analyzer according to claim 1, wherein the reference value is a value set based on reference value information transmitted from a server that manages reference value information.

9. The specimen analyzer according to claim 1, wherein the aforementioned reference value is a value set for each facility.

10. The specimen analyzer according to claim 1, wherein the reference value includes at least one first reference value corresponding to the lower limit of the distribution range of the measured value and a second reference value corresponding to the upper limit.

11. The first reference value is a value used to determine whether the measured value deviates from the distribution range towards the lower end. The sample analyzer according to claim 10, wherein the second reference value is a value for determining whether the measured value deviates from the distribution range towards the higher side.

12. The control unit determines, based on the first reference value and the second reference value, whether the measured value identified as an outlier deviates from the distribution range to the lower side or to the higher side. The sample analyzer according to claim 11, wherein the output unit outputs the analysis result, which includes information suggesting that the value is outside the distribution range on the lower side or on the higher side, depending on the determination result.

13. The aforementioned measurement includes the measurement values ​​of each of several dementia biomarkers, The specimen analyzer according to claim 1, wherein the reference value includes a plurality of reference values ​​corresponding to each of the plurality of dementia biomarkers.

14. The sample analyzer according to claim 13, wherein the output unit, when it is determined that the measured value of at least one of the plurality of dementia biomarkers is identified as an outlier in the determination result of that biomarker, outputs the analysis result including information that suggests that the measured value of the other biomarker may have been influenced by factors other than dementia, regardless of the determination result of that biomarker.

15. The specimen analyzer according to claim 1, wherein the measurement value includes a plurality of measurement values ​​corresponding to each of the plurality of analytes contained in the specimen, and a calculated value calculated from the plurality of measurement values.

16. The specimen analyzer according to claim 1, wherein the analyte related to the dementia biomarker comprises β-amyloid (Aβ) protein, tau protein, phosphorylated tau (p-Tau) protein, neurofilament L (NfL) protein, α-synuclein protein, β-synuclein protein, TAR DNA-binding protein 43 (TDP-43), glial fibrillary acidic protein (GFAP), soluble TREM2 (sTREM2), neuron pentraxin 2 (NPTX-2), chitinase-like protein 1 (YKL-40), or aggregates or peptide fragments thereof.

17. The specimen analyzer according to claim 1, wherein the output unit further outputs the clinical information of the subject and / or information related to the handling of the specimen.

18. The specimen analyzer according to claim 17, wherein the clinical information includes at least one of the subject's medical history, the name of the underlying disease, and measurement data relating to disease indicators other than dementia.

19. The specimen analyzer according to claim 17, wherein the information relating to the handling of the specimen includes at least one of the following: facility information where the specimen was collected and / or processed; date of collection and / or processing of the specimen; time of collection and / or processing of the specimen; and information relating to the processing conditions and / or storage conditions of the specimen.

20. A sample analyzer equipped with a control unit and an output unit measures analytes related to dementia biomarkers contained in a sample collected from a subject, and The sample analyzer comprises a server connected via a communication network, which stores reference value information for identifying outliers that may occur due to factors other than dementia, The control unit determines whether the measured value of the analyte is identified as an outlier based on the measured value of the analyte and the reference value set based on the reference value information, and generates the biomarker analysis results according to the result of the determination. The output unit outputs the analysis results of the biomarker. Sample analysis system.

21. The analysis of analytes related to dementia biomarkers contained in the sample collected from the subject was measured to obtain the measurement value. Based on the measured value and a reference value for identifying outliers that may occur due to factors other than dementia, it is determined whether the measured value is identified as an outlier, and the analysis results of the biomarker according to the result of the determination are output. Method for analyzing specimens.

22. A program that causes a computer to perform a process of analyzing analytes related to biomarkers for dementia, Based on the measured value obtained by measuring the analyte contained in the sample taken from the subject and a reference value for identifying outliers that may occur due to factors other than dementia, it is determined whether the measured value is identified as an outlier, and the analysis results of the biomarker are generated according to the result of the determination. program.

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