JP2015010939A - Specimen analysis device, specimen analysis system, anomaly detection device, and method of detecting anomaly of specimen analysis device - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide: a specimen analysis device which allows for controlling accuracy without requiring an accuracy control sample and a healthy specimen; a specimen analysis system; an anomaly detection device; and a method of detecting anomaly of the specimen analysis device.SOLUTION: A specimen analysis device selects measurement results for a subject with a specific disease from measurement results obtained thereby. Measurement results outside a predetermined sporadic value exclusion range are excluded from measurement results for anomaly detection, and only measurement results within an accidental value exclusion range are stored as the measurement results for anomaly detection. When the number of the measurement results for anomaly detection reaches a predetermined level, the specimen analysis device computes average values of measurement items that exhibit small deviation from values in healthy condition in the disease, and compares the average values with predetermined control ranges to detect anomaly.

Description

  The present invention relates to a sample analyzer that analyzes a sample collected from a subject, a sample analysis system including the sample analyzer, an abnormality detection device, and an abnormality detection method for the sample analyzer.

  In order to ensure that the sample analyzer outputs an accurate measurement result, an abnormality of the sample analyzer is detected. As one of the abnormality detection methods, so-called accuracy management is known. A general quality control method is to measure a quality control sample containing a predetermined component at a known concentration based on a sample analysis result and confirm that the measurement result falls within a predetermined error range.

  Since some of the quality control samples used for the quality control described above are expensive, some quality control methods use a sample collected from a subject (hereinafter referred to as “subject sample”) instead of a quality control sample in some facilities. It is used. Quality control methods using test specimens include repeated measurement method, number plus method, normal person average value method, cross check method, item correlation method, delta check method, application results zone method, real-time latent reference value moving average value method Are known (Patent Document 1, Non-Patent Document 1, etc.).

JP 2007-248090 A

Sadao Hayashi, “Development of Real-time Accuracy Control Method Using Latent Reference Value Moving Average Method Using Daily Patient Data” [online], July 2004, Osaka University, Internet <http://ir.library.osaka -u.ac.jp/dspace/bitstream/11094/982/1/f_2004-18978h.pdf>

  In the accuracy control method using the subject specimen, the result obtained by measuring specimens collected from a plurality of subjects in the specimen analyzer is used. For this reason, if a sample collected from a subject suffering from a disease is included, the measurement results vary, making it difficult to accurately control the accuracy. Therefore, in the accuracy control method using the above-described subject specimens, particularly the normal person average value method that is generally used among them, normal specimens collected from healthy persons (specimens whose test results show normal values). It is assumed that a considerable number of “normal specimens” are included.

  However, in countries where medical institutions are insufficiently developed, health status deteriorates due to reasons such as living in a remote area from the urban area where medical institutions are installed and lack of economic margin for taking examinations. There are many subjects who do not go to the examination unless they are new. For this reason, medical institutions in such a country have little accumulation of test results indicating normal values (that is, measurement results of healthy persons), and it is difficult to detect anomalies using the above-described accuracy management method.

  The present invention has been made in view of such circumstances, and a sample analyzer, a sample analysis system, an abnormality detector, and a sample analyzer that can detect an abnormality in a measurement unit without requiring a quality control sample and a normal sample. An object of the present invention is to provide an abnormality detection method.

  In order to solve the above-described problem, a sample analyzer according to one aspect of the present invention is obtained by measuring a sample collected from a subject for at least one measurement item, and measuring the sample by the measurement unit. A plurality of disease measurement results, which are measurement results for a sample collected from a subject having a specific disease, from among the measurement results obtained by measuring the sample by the measurement unit Control for outputting abnormality information relating to abnormality of the measurement unit when a value statistically derived from a plurality of measurement values for a predetermined measurement item in a plurality of selected disease measurement results deviates from a predetermined reference A section.

  Thereby, since abnormality detection of the sample analyzer can be performed using the disease measurement results of a plurality of subjects having a common disease, a quality control sample and a normal sample are not required for abnormality detection.

In this aspect, the sample analyzer further includes an input unit that receives designation of the disease measurement result among the measurement results obtained by the measurement unit, and the control unit receives the designation by the input unit It may be configured to select the disease measurement result.
Thereby, it becomes possible to select the disease measurement information designated by the user for use in outputting the abnormality information.

In the above aspect, the control unit is configured to estimate a subject's disease related to the measurement result based on the measurement result obtained by the measurement unit, and to select a disease measurement result based on the estimation result. It may be.
Thereby, it becomes possible to select the measurement result of the subject estimated to have a specific disease for use in the output of abnormality information.

In the above aspect, the control unit selects a plurality of disease measurement results satisfying a predetermined condition from the plurality of disease measurement results, and performs a plurality of measurements on the predetermined measurement items in the selected plurality of disease measurement results. The abnormality information may be output when a value statistically derived from the value deviates from the predetermined reference.
Thereby, it is possible to select a disease measurement result to be used for outputting abnormality information by excluding those not suitable for outputting abnormality information from among the disease measurement results.

In the above aspect, the control unit selects, as the predetermined condition, a plurality of disease measurement results whose measurement values for the predetermined measurement items are within a predetermined range, and the predetermined values in the selected plurality of disease measurement results The abnormality information may be output when a value statistically derived from a plurality of measurement values for the measurement item deviates from the predetermined reference.
As a result, a disease measurement result in which a measurement value for a predetermined measurement item deviates from a predetermined range due to reasons such as having a disease other than a specific disease is regarded as not suitable for use in abnormality detection. It becomes possible to exclude from disease measurement results for use in output.

In the above aspect, the control unit selects, as the predetermined condition, a plurality of disease measurement results whose measurement values for the first measurement item are within a predetermined range, and the first of the plurality of selected disease measurement results. The abnormality information may be output when a value statistically derived from a plurality of measurement values of the second measurement item different from the measurement item is out of the predetermined reference. .
As a result, a disease measurement result in which the measurement value for the first measurement item is out of the predetermined range due to reasons such as having a disease other than a specific disease is regarded as unsuitable for use in abnormality detection. It becomes possible to exclude from the disease measurement results for use in the output.

In the above aspect, when a predetermined number of the disease measurement results are selected, the control unit is statistically derived from a plurality of measurement values for the predetermined measurement item in the selected predetermined number of disease measurement results. If the value deviates from the predetermined reference, the abnormality information may be output.
Accordingly, when a predetermined number of statistically sufficient disease measurement results used for abnormality detection are collected, abnormality detection of the sample analyzer can be performed using these disease measurement results.

In the above aspect, the measurement unit is configured to measure at least one type of particles contained in a specimen collected from a subject, and the control unit is configured to select predetermined particles in a plurality of selected disease measurement results. When a value statistically derived from a plurality of measured values for a value deviates from a predetermined reference, abnormality information relating to abnormality of the measurement unit may be output. In this case, it is preferable that the measurement unit is configured to measure at least one blood cell contained in a sample collected from the subject.
Thereby, for example, an abnormality detection of a blood cell analyzer or a urine analyzer can be performed by using a measurement value for a predetermined particle in a disease measurement result of a plurality of subjects having a common disease.

In the above aspect, the predetermined measurement item may be a measurement item in which a variation in a measured value from a normal state is small in a subject having the specific disease compared to other measurement items.
Thereby, it is possible to accurately detect abnormality of the measurement item using the measurement result of the subject having a specific disease.

In the above aspect, the control unit further selects, from among the measurement results obtained by the measurement unit, a healthy person measurement result that is a measurement result for a sample collected from a healthy person, and the selected healthy person measurement result and A configuration is provided to output abnormality information related to abnormality of the measurement unit when a value statistically derived from a plurality of measurement values for a predetermined measurement item in the selected disease measurement result deviates from a predetermined reference May be.
Thereby, since abnormality detection can be performed using not only a disease measurement result but also a healthy person measurement result, it is possible to perform abnormality detection on the measurement item with higher accuracy.

In the above aspect, the control unit is configured to select a disease measurement result for a sample collected from a subject whose specific disease is in a specific stage among the measurement results obtained by the measurement unit. May be.
Thereby, even if it is a measurement result of the test subject who has the disease for which the tendency of a measured value changes for every stage, it becomes possible to use for abnormality detection.

In the above aspect, the specific stage may be a stage in which variations in the measurement values for the predetermined measurement item are less than other stages among a plurality of subjects having the specific disease.
Thereby, it becomes possible to accurately detect abnormality of the measurement item using the measurement result of the subject whose specific disease is in the specific stage.

The said aspect WHEREIN: The said control part may be comprised so that the said predetermined | prescribed reference | standard may be changed according to the specific disease corresponding to the selected some disease measurement result. Furthermore, the control unit may be configured to change the predetermined item in accordance with a specific disease corresponding to a plurality of selected disease measurement results.
Thereby, since the predetermined reference or the predetermined item is automatically changed according to the specific disease, it becomes possible to easily detect the abnormality according to various diseases.

  In the above aspect, the specific disease may be any one of HIV infection, tuberculosis, malaria, and dengue fever.

  In the above aspect, the specific disease may be an HIV infection, and the predetermined measurement item may be a red blood cell count.

  In the above aspect, the specific disease may be dengue fever, and the predetermined measurement item may be at least one of a red blood cell count and a white blood cell count.

  In the above aspect, the specific disease may be an HIV infection, and the predetermined measurement item may be a platelet count.

  In the above aspect, the value statistically derived from the plurality of measurement values may be an average value of the plurality of measurement values or a median value of the plurality of measurement values.

  A sample analysis system according to another aspect of the present invention includes a sample analyzer including a measurement unit that measures a sample collected from a subject for at least one measurement item, and a measurement result obtained by measuring the sample by the measurement unit. A plurality of disease measurement results, which are measurement results for a sample collected from a subject having a specific disease, are selected and selected from the measurement results obtained by the measurement of the sample by the storage unit and the measurement unit. An anomaly detection device that outputs anomaly information related to anomalies of the measurement unit when a value statistically derived from a plurality of measurement values for a predetermined measurement item in a plurality of disease measurement results deviates from a predetermined reference; Is provided.

  An abnormality detection apparatus according to another aspect of the present invention is an abnormality detection apparatus that outputs abnormality information related to an abnormality in the measurement unit of a sample analyzer including a measurement unit that measures a sample collected from a subject for at least one measurement item. An acquisition unit that acquires a measurement result obtained by measuring the sample by the measurement unit; a storage unit that stores the measurement result acquired by the acquisition unit; and a measurement result acquired by the acquisition unit , Select multiple disease measurement results that are measurement results for a sample collected from a subject with a specific disease, and statistically derive from multiple measurement values for a given measurement item in the selected multiple disease measurement results And a control unit that outputs abnormality information regarding abnormality of the measurement unit when the measured value deviates from a predetermined reference.

  An abnormality detection method for a sample analyzer according to another aspect of the present invention is an abnormality detection method for a sample analyzer including a measurement unit that measures a sample collected from a subject with respect to at least one measurement item. A step of obtaining a measurement result obtained by measuring the sample, a step of storing the obtained measurement result, and a measurement result of a sample collected from a subject having a specific disease among the obtained measurement results. The step of selecting a plurality of disease measurement results and the measurement statistically derived from a plurality of measurement values for a predetermined measurement item in the selected plurality of disease measurement results deviating from a predetermined standard Outputting abnormality information relating to abnormalities in the department

  According to the sample analyzer, the sample analysis system, the abnormality detection device, and the abnormality detection method of the sample analysis device according to the present invention, it is possible to detect the abnormality of the sample analysis device without requiring a quality control sample and a normal sample.

FIG. 3 is a perspective view showing the appearance of the sample analyzer according to the first embodiment. The block diagram which shows the structure of a measurement unit. FIG. 3 is a block diagram showing the configuration of the information processing unit 5. 5 is a flowchart showing a procedure of an abnormality detection operation performed by the sample analyzer according to the first embodiment. The figure which shows a disease designation | designated dialog. The figure which shows a management screen. 10 is a flowchart showing a procedure of an abnormality detection operation by the sample analyzer according to the second embodiment. 10 is a flowchart showing a procedure of an abnormality detection operation by the sample analyzer according to the third embodiment. The figure which shows a disease stage designation | designated dialog. FIG. 6 is a schematic diagram showing a configuration of a sample analysis system according to a fourth embodiment. The block diagram which shows the structure of an abnormality detection apparatus. 10 is a flowchart showing an abnormality detection operation in the abnormality detection device according to the fourth embodiment.

  Hereinafter, preferred embodiments of the present invention will be described with reference to the drawings.

(Embodiment 1)
[Sample analyzer configuration]
FIG. 1 is a perspective view showing the appearance of the sample analyzer according to the present embodiment. The sample analyzer 1 according to the present embodiment is a multi-item blood cell analyzer that detects white blood cells, red blood cells, platelets, and the like contained in a blood sample and counts each blood cell. As shown in FIG. 1, a sample analyzer 1 includes a measurement unit 2, a sample transport unit 4 disposed on the front side of the measurement unit 2, and an information processing unit 5 that can control the measurement unit 2 and the sample transport unit 4. And.

  A blood sample, which is peripheral blood collected from a subject (patient), is stored in a sample container (collecting blood vessel). A plurality of sample containers are held in the sample rack L, the sample rack L is transported by the sample transport unit 4, and the blood sample is supplied to the measurement unit 2.

<Configuration of measurement unit>
The configuration of the measurement unit will be described. FIG. 2 is a block diagram showing the configuration of the measurement unit. As shown in FIG. 2, the measurement unit 2 prepares a sample suction unit 21 that sucks blood as a sample from a sample container (collection tube) T, and a measurement sample used for measurement from the blood sucked by the sample suction unit 21. And a detection unit 23 that detects blood cells from the measurement sample prepared by the sample preparation unit 22. In addition, the measurement unit 2 includes an intake (see FIG. 1) for taking the sample container T accommodated in the sample rack L conveyed by the rack conveyance unit 43 of the sample conveyance unit 4 into the measurement unit 2. The apparatus further includes a sample container transport unit 25 that takes the sample container T from the sample rack L into the measurement unit 2 and transports the sample container T to the suction position by the sample suction unit 21.

  As shown in FIG. 2, the sample suction unit 21 has a suction tube 211. The sample aspirating unit 21 includes a syringe pump. The suction tube 211 is movable in the vertical direction, and is configured to suck the blood in the sample container T transported to the suction position by moving downward.

  The sample preparation unit 22 includes a plurality of reaction chambers (not shown). The sample preparation unit 22 includes a reagent container containing a reagent (diluent) for detecting RBC (red blood cells) and PLT (platelets), a reagent container containing a reagent for detecting HGB (hemoglobin), and white blood cell classification (DIFF). It is connected to a plurality of reagent containers such as a reagent container containing a reagent for use, and it is possible to supply reagents such as a staining reagent, a hemolytic agent, and a diluent to the reaction chamber. The sample preparation unit 22 is also connected to the suction tube of the sample suction unit 21, and can supply the blood sample sucked by the suction tube to the reaction chamber. The sample preparation unit 22 mixes and agitates the specimen and the reagent in the reaction chamber to prepare a sample (measurement sample) for measurement by the detection unit 23.

  The detection unit 23 can perform RBC (red blood cell) detection and PLT (platelet) detection by a sheath flow DC detection method. In detection of RBC and PLT by this sheath flow DC detection method, a measurement sample in which a specimen and a diluent are mixed is measured, and the information processing unit 5 analyzes and processes the obtained measurement data. Acquisition of numerical data of RBC and PLT is performed. The detection unit 23 can perform HGB (hemoglobin) detection by the SLS-hemoglobin method, and includes WBC (leukocyte), NEUT (neutrophil), LYMPH (lymphocyte), EO (eosinophil), Detection of BASO (basophils) and MONO (monocytes) (white blood cell classification 5) can be performed by a flow cytometry method using a semiconductor laser. In the white blood cell 5 classification, the measurement sample obtained by mixing the specimen, the white blood cell 5 classification staining reagent, the hemolytic agent, and the diluent is measured, and the information processing unit 5 analyzes the measurement data obtained thereby. By processing, numerical data of NEUT, LYMPH, EO, BASO, MONO, and WBC is acquired.

  The detection unit 23 includes a flow cell (not shown), generates a liquid flow in the flow cell by sending a measurement sample to the flow cell, and irradiates blood cells included in the liquid flow passing through the flow cell with semiconductor laser light. Thus, forward scattered light, side scattered light, and side fluorescence are detected.

  Light scattering is a phenomenon that occurs when particles such as blood cells exist as obstacles in the traveling direction of light and the light changes its traveling direction. By detecting this scattered light, information on the size or material of the particles can be obtained. In particular, information on the size of the particles (blood cells) can be obtained from the forward scattered light. Moreover, the information inside the particles can be obtained from the side scattered light. When blood cells are irradiated with laser light, the side scattered light intensity depends on the complexity of the cell (the shape, size, density, and amount of granules). Therefore, by utilizing this characteristic of the side scattered light intensity, measurement of the classification of white blood cells and other measurements can be performed.

  When a fluorescent material such as a stained blood cell is irradiated with light, light having a wavelength longer than the wavelength of the irradiated light is emitted. The intensity of fluorescence becomes stronger if it is well stained, and information on the degree of staining of blood cells can be obtained by measuring the intensity of fluorescence. Therefore, the measurement of the classification of white blood cells and other measurements can be performed according to the difference in (side) fluorescence intensity.

  The sample container transport unit 25 includes a hand unit 25a that can hold the sample container T, and the sample container accommodated in the sample rack L by moving the hand unit 25a in the vertical direction and the front-rear direction (Y direction). T is gripped by the hand portion 25a and extracted from the sample rack L. The sample container transport unit 25 includes a sample container setting unit 25b having a hole part into which the sample container T can be inserted. The sample container T is set in the sample container setting unit 25b, and the sample container setting unit 25b moves to be taken into the measurement unit 2.

  The sample container setting unit 25b can be moved to a suction position by the sample suction unit 21. When the sample container setting unit 25b moves to the aspirating position, the sample is aspirated from the set sample container T by the sample aspirating unit 21.

<Configuration of information processing unit>
Next, the configuration of the information processing unit 5 will be described. The information processing unit 5 can analyze the measurement data output from the measurement unit 2, generate an analysis result of the sample, and display the analysis result.

  The information processing unit 5 is configured by a computer. FIG. 3 is a block diagram showing the configuration of the information processing unit 5. The information processing unit 5 is realized by a computer 5a. As shown in FIG. 3, the computer 5 a includes a main body 51, a display unit 52, and an input unit 53. The main body 51 includes a CPU 51a, ROM 51b, RAM 51c, hard disk 51d, reading device 51e, input / output interface 51f, communication interface 51g, and image output interface 51h. The CPU 51a, ROM 51b, RAM 51c, hard disk 51d, reading device 51e, input The output interface 51f, the communication interface 51g, and the image output interface 51h are connected by a bus 51j.

  The reading device 51e can read a computer program 54a for causing the computer to function as the information processing unit 5 from the portable recording medium 54, and install the computer program 54a on the hard disk 51d.

  The input / output interface 51f includes, for example, a serial interface such as USB, IEEE1394, or RS-232C, a parallel interface such as SCSI, IDE, or IEEE1284, and an analog interface including a D / A converter, an A / D converter, and the like. It is configured. An input unit 53 including a keyboard and a mouse is connected to the input / output interface 51f, and the user can input data to the computer 5a by using the input unit 53. The input / output interface 51f can be connected to the measurement unit 2 and the sample transport unit 4 via a communication cable. Thereby, the information processing unit 5 can control each of the measurement unit 2 and the sample transport unit 4.

[Abnormality detection operation of sample analyzer]
Hereinafter, the operation of the sample analyzer 1 according to the present embodiment will be described.

  Some of the above-described measurement items have little variation in measurement results from normal conditions. That is, there is a measurement item in which the measurement result of a subject having a specific disease shows the same value as the measurement result in a normal state. For example, in HIV (human immunodeficiency virus) infections, including acquired immunodeficiency syndrome (AIDS), the number of white blood cells (WBC) may decrease, so the WBC value of patients with HIV infection Fluctuation is large. However, such an effect is not recognized on the number of red blood cells (RBC), and the fluctuation of RBC values in patients with HIV infection from normal is smaller than that of WBC values. That is, the RBC value of HIV-infected patients is almost the same as normal. Moreover, in HIV infection, the variation in the WBC value between patients is large, but the variation in the RBC value between patients is small (same as that of a healthy person). Further, even in the RBC value and the WBC value in dengue fever, there is little variation in the measured value from the normal state. The sample analyzer 1 performs abnormality detection using the result of measuring a sample collected from a subject having a specific disease by using the nature of such a disease. In the present embodiment, a case will be described in which abnormality detection is performed on the RBC measurement of the sample analyzer 1 using the RBC measurement result of an HIV-infected patient.

  FIG. 4 is a flowchart showing a procedure of an abnormality detection operation by the sample analyzer 1 according to the present embodiment. First, a sample collected from a subject is measured by the sample analyzer 1, and a measurement result including an RBC value is obtained (step S101).

  Specifically, the measurement of the specimen is performed as follows. When the information processing unit 5 controls the measurement unit 2, the measurement unit 2 takes in the sample container, sucks the sample from the sample container, and measures the measurement sample for the measurement item specified in the measurement order given in advance. Prepare and measure the specimen. The measurement data obtained as a result is given from the measurement unit 2 to the information processing unit 5 and analyzed by the information processing unit to generate a measurement result of the specimen.

  The obtained measurement result is displayed on the display unit 52 of the sample analyzer 1 (step S102). At this time, the CPU 51a causes the display unit 52 to display, together with the measurement result, a disease designation dialog for inquiring whether the sample is a sample of a subject suffering from HIV infection.

  FIG. 5 is a diagram showing a disease designation dialog. The disease designation dialog D1 includes a message “Is this subject subject suffering from HIV infection?” And two buttons C11 and C12. The buttons C <b> 11 and C <b> 12 are button controls that are GUI (Graphical User Interface) objects, and can be selected by operating the input unit 53. The button C11 includes characters “Yes”, and the user can designate the sample as a sample of an HIV-infected patient by selecting the button C11. Further, the button C12 includes a character “No”, and the user can select the button C12 so that the sample is not designated as a sample of an HIV-infected patient.

  In the present embodiment, the user designates a specimen of an HIV-infected patient using the disease designation dialog D1 as described above, and the CPU 51a selects the measurement result of the HIV-infected patient from the measurement results by the user's input. To do.

  The CPU 51a determines which of the buttons C11 and C12 has been selected (step S103). When button C12 is selected, that is, when the sample is not designated as a sample of an HIV-infected patient (NO in step S103), CPU 51a returns the process to step S101 to measure the next sample. Run.

  On the other hand, when button C11 is selected, that is, when the sample is designated as a sample of an HIV-infected patient (YES in step S103), CPU 51a determines the RBC value included in the measurement result and a predetermined burst. It is compared with the value exclusion range to determine whether or not the RBC value is within the sudden value exclusion range (step S104).

  The sudden value exclusion range is a numerical range that is provided corresponding to the RBC value of an HIV-infected patient and that is expected to fall within the RBC value of an HIV-infected patient. That is, when the RBC value is out of the sudden value exclusion range, the subject of the sample has a disease other than HIV infection, and therefore the RBC value is based on the RBC value of a standard HIV-infected patient. It is suspected that it has deviated greatly. In this case, it is considered that the subject suffers from both HIV infection and a disease other than HIV infection.

  If the RBC value is out of the sudden value exclusion range (NO in step S104), the CPU 51a returns the process to step S101 without putting the measurement result in the target for abnormality detection, and the next sample Perform the measurement.

  On the other hand, when the RBC value is within the sudden value exclusion range (YES in step S104), the CPU 51a stores the measurement result in the hard disk 51d as a target to be used for abnormality detection (step S105). By doing in this way, the measurement result used for abnormality detection is selected from the measurement results of the specimen.

  Next, the CPU 51a determines whether or not the number of measurement results used for abnormality detection stored in the hard disk 51d has reached a predetermined number of data (for example, 20) (step S106). When the number of measurement results for abnormality detection stored in the hard disk 51d has not reached the number of data (NO in step S106), the CPU 51a returns the process to step S101 and executes the measurement of the next sample. .

  On the other hand, when the number of measurement results for abnormality detection stored in hard disk 51d reaches the number of data (YES in step S106), CPU 51a obtains the RBC value of the measurement result for abnormality detection from hard disk 51d. Reading and calculating the average value (step S107), this average value is stored in the hard disk 51d as abnormality detection history data (step S108).

  Further, the CPU 51a compares the average value of the RBC values (hereinafter referred to as “RBC average value”) with a predetermined management range (step S109). This management range guarantees that the RBC measurement can be normally performed for the apparatus state and the reagent of the sample analyzer 1 if the RBC average value is included therein. Such a management range may be given by a statistical method such as an average value of ± 2 SD (standard deviation) of RBC values measured in the sample analyzer 1 in the past, or may be predetermined by the user. May be.

  When the RBC average value is within the management range (YES in step S109), the CPU 51a displays a management screen based on the RBC average value on the display unit 52 (step S110). FIG. 6 is a diagram showing a management screen. As shown in FIG. 6, the management screen D2 is a management in which a line TL indicating the center of the management range, a line UL indicating the upper limit, a line LL indicating the lower limit, the current RBC average value, and the past RBC average value are shown. Includes graph G. By checking the display unit 52 of the sample analyzer 1, the user can know that the sample analyzer 1 is guaranteed that the RBC measurement can be normally performed. Thereafter, the CPU 51a returns the process to step S101, and executes the measurement of the next sample.

  On the other hand, when the RBC average value is out of the management range (NO in step S109), the CPU 51a displays error information on the display unit 52 (step S111), and the user is abnormal in at least one of the apparatus and the reagent. Is generated, that is, it is not guaranteed that the RBC measurement can be normally performed in the sample analyzer 1, and the process is terminated.

  By configuring as described above, it is possible to detect abnormality in the RBC measurement of the sample analyzer 1 using the RBC values of a plurality of subjects having a common disease (HIV infection). For this reason, a quality control sample and a healthy subject's sample (normal sample) are not required for anomaly detection about RBC measurement.

  In patients with HIV infection, the RBC value is an item with less fluctuations in the measured value from normal than the WBC value, so it is easy to obtain a stable value, and abnormality detection with high accuracy is possible by using such RBC. Can be performed.

  In the present embodiment, the configuration has been described in which abnormality detection is performed for the RBC measurement of the sample analyzer using the RBC value of only the HIV-infected patient, but the present invention is not limited to this. As described above, since the RBC value has little fluctuation in the measured value from the normal state, abnormality detection for the RBC measurement may be performed using the RBC value of the HIV-infected patient and the healthy person. In this case, in the above embodiment, instead of the disease designation dialog D1, a dialog (not shown) for designating a specimen of an HIV-infected patient and a specimen of a healthy person is displayed, and an abnormality detection is performed. What is necessary is just to memorize | store and use the RBC value of several HIV infection patients, and the RBC value of several healthy persons as a measurement result.

  Moreover, since there is little fluctuation | variation in the measured value from a normal state also in the RBC value of a malaria infection patient, abnormality detection about RBC measurement may be performed using the RBC value of an HIV infection patient and a malaria infection patient. Further, it may be performed using the RBC values of HIV-infected patients, malaria-infected patients, and healthy individuals.

  In the present embodiment, the configuration has been described in which abnormality detection is performed on the RBC measurement of the sample analyzer using the RBC value of an HIV-infected patient. However, the present invention is not limited to this. Since the RBC value and WBC value of Dengue patients are also less variable than normal, the RBC value or WBC value of the sample analyzer can be obtained using the RBC values or WBC values of Dengue patients. It is also possible to adopt a configuration for detecting the abnormality.

  Moreover, since it was set as the structure which makes a user specify the measurement result of a HIV infection patient, it becomes possible to select reliably the measurement result of a HIV infection patient which can be utilized for abnormality detection. The user can select an HIV-infected patient accurately by confirming not only the measurement result by the sample analyzer 1 but also other test results such as immunological test and biochemical test, and the inquiry result.

(Embodiment 2)
[Sample analyzer configuration]
In the sample analyzer according to the present embodiment, a reagent container containing a reagent for detecting malaria is further attached to the sample preparation unit 22, and this reagent container is connected to the reaction chamber. The blood sample sucked by the suction tube and the reagent for detecting malaria are supplied to the reaction chamber, and these are mixed and stirred in the reaction chamber to prepare a measurement sample for detecting malaria.

  The detection unit 23 is configured so that malaria parasites can be detected by a flow cytometry method using a semiconductor laser. In detecting malaria parasites, a measurement sample in which a specimen, a staining reagent for detecting malaria, a hemolytic agent, and a diluent is mixed is measured, and the information processing unit 5 analyzes the measurement data obtained thereby. By processing, numerical data such as the number of malaria parasites and the number of RBCs are obtained. Specifically, in the measurement sample, red blood cells in the specimen are hemolyzed by the action of the hemolytic agent, and malaria parasites parasitized in the red blood cells are released in the liquid. The measurement sample is passed through the flow cell, and the measurement sample flowing through the flow cell is irradiated with laser light. Malaria parasite in the specimen by analyzing the detected electrical signal by receiving and photoelectrically converting the forward scattered light and side scattered light emitted from the measurement sample irradiated with the laser light with a light receiving element. Is detected and counted. As the malaria parasite detection method described above, for example, the method described in JP-A-2005-333868 can be used.

  Since the other configuration of the sample analyzer according to the present embodiment is the same as the configuration of the sample analyzer 1 according to the first embodiment, the same components are denoted by the same reference numerals and the description thereof is omitted.

[Operation of sample analyzer]
In the present embodiment, the sample analyzer estimates whether the subject suffers from malaria infection based on the measurement result of the sample, and when the subject is estimated to suffer from malaria infection, The measurement result is automatically selected by the sample analyzer, and abnormality detection for the RBC measurement of the sample analyzer is performed using the selected measurement result. As for the RBC value of malaria infection patients, the fluctuation from normal is smaller than the WBC value and the PLT value. That is, the RBC value of malaria infection patients is not substantially different from normal. Moreover, although the variation of the WBC value and PLT value between malaria infection patients becomes large, the variation of the RBC value between patients is small (as much as a healthy person). For this reason, the abnormality detection about RBC measurement of a sample analyzer can be accurately performed by utilizing the RBC value of a malaria infection patient.

  FIG. 7 is a flowchart showing a procedure of an abnormality detection operation by the sample analyzer according to the present embodiment. First, a sample collected from a subject is measured by the sample analyzer 1, and a measurement result including the number of malaria parasites, an RBC value, and the like is obtained (step S201). In addition, the measurement result includes an estimation result regarding malaria infection by the information processing unit 5. That is, in the measurement data analysis process, the CPU 51a of the information processing device 5 estimates whether or not the subject suffers from a malaria infection based on the number of malaria parasites. In this estimation process, when the number of malaria parasites included in the measurement result is equal to or greater than a predetermined number, it is estimated that the subject is suffering from malaria infection, and when the number of malaria parasites is less than the predetermined number, Presumed not to have malaria infection. When it is estimated that the subject suffers from a malaria infection, malaria infection estimation information indicating that the subject is suspected of suffering from a malaria infection is added to the measurement result.

  The measurement result obtained as described above is displayed on the display unit 52 of the sample analyzer 1 (step S202). The CPU 51a determines whether malaria infection estimation information is included in the measurement result (step S203). When the malaria infection estimation information is not included in the measurement result (NO in step S203), the CPU 51a returns the process to step S201 and executes the measurement of the next sample.

  On the other hand, when malaria infection estimation information is included in the measurement result, that is, when the sample is estimated as a sample of a patient with malaria infection (YES in step S203), the CPU 51a is included in the measurement result. The RBC value is compared with a predetermined sudden value exclusion range to determine whether or not the RBC value is within the sudden value exclusion range (step S204).

  The sudden value exclusion range is a numerical range that is provided corresponding to the RBC value of a malaria infection patient and that is expected to fall within the RBC value of a malaria infection patient. That is, when the RBC value is out of the sudden value exclusion range, the subject of the sample has a disease other than malaria infection, and therefore the RBC value is based on the RBC value of a standard malaria infection patient. It is suspected that it has deviated greatly. In this case, it is considered that the subject suffers from both malaria infection and a disease other than malaria infection.

  If the RBC value is out of the sudden value exclusion range (NO in step S204), the CPU 51a returns the process to step S201 without putting the measurement result in the target for abnormality detection, and the next sample Perform the measurement.

  On the other hand, when the RBC value is within the sudden value exclusion range (YES in step S204), the CPU 51a stores the measurement result in the hard disk 51d as a target to be used for abnormality detection (step S205). By doing in this way, the measurement result used for abnormality detection is selected from the measurement results of the specimen.

  Using the RBC value included in the measurement result selected as described above, the CPU 51a of the information processing unit 5 performs abnormality detection for the RBC measurement of the sample analyzer. Since the processing in steps S206 to 211 is the same as that in steps S106 to 111 described in the first embodiment, the description thereof is omitted.

  By configuring as described above, it is possible to detect an abnormality in the RBC measurement of the sample analyzer using the RBC values of a plurality of subjects having a common disease (malaria infection). For this reason, a quality control sample and a healthy subject's sample (normal sample) are not required for anomaly detection about RBC measurement.

  In addition, since the sample analyzer automatically estimates that the patient is suffering from a malaria infection, the user determines whether the patient has a malaria infection and does not input the determination result to the sample analyzer. However, it becomes possible to automatically select the measurement result of the malaria infection patient used for abnormality detection.

(Embodiment 3)
[Sample analyzer configuration]
Since the configuration of the sample analyzer according to the present embodiment is the same as the configuration of the sample analyzer 1 according to the first embodiment, the same components are denoted by the same reference numerals and the description thereof is omitted.

[Operation of sample analyzer]
Some diseases show different tendencies for each stage of the disease for some of the measurement items described above. For example, in the case of HIV infection, the PLT value shows a tendency to decrease compared to normal, but the degree differs depending on the stage. That is, in stage 1 (the first stage), although the PLT value shows a decreasing tendency as compared with the normal state, the degree is small, and the variation in the PLT value between stage 1 patients is relatively small. As the stage progresses, the tendency for the PLT value to decrease increases, and the variation in the PLT value among patients at the same stage also increases. In stage 4, which is the terminal stage, the PLT value tends to decrease most, and the variation in the PLT value among stage 4 patients is the largest. The sample analyzer according to the present embodiment uses such a property of a disease, and performs abnormality detection using a result obtained by measuring a sample collected from a subject at a specific stage of a specific disease. In the present embodiment, a case will be described in which abnormality detection is performed on the PLT measurement of the sample analyzer using the PLT value of the stage 1 HIV infection patient.

  FIG. 8 is a flowchart showing a procedure of an abnormality detection operation by the sample analyzer according to the present embodiment. First, a sample collected from a subject is measured by the sample analyzer 1, and a measurement result including a PLT value is obtained (step S301).

  The obtained measurement result is displayed on the display unit 52 of the sample analyzer 1 (step S302). The CPU 51a causes the display unit 52 to display a disease stage designation dialog for inquiring whether the sample is a sample of a stage 1 patient with HIV infection along with the measurement result.

  FIG. 9 is a diagram showing a disease stage designation dialog. The disease stage designation dialog D3 includes a message “Is this subject a subject of HIV infection stage 1?” And two buttons C31 and C32. The buttons C31 and C32 are button controls that are GUI objects, and can be selected by operating the input unit 53. The button C31 includes the characters “Yes”, and the user can designate the sample as a sample of a stage 1 patient with HIV infection by selecting the button C31. Further, the button C32 includes a character “No”, and the user can select the button C32 so that the sample is not designated as a sample of a stage 1 patient with HIV infection.

  In the present embodiment, the disease stage designation dialog D3 as described above causes the user to designate a specimen of an HIV infection stage 1 patient, and the CPU 51a inputs the HIV infection disease stage from the measurement results by the user input. Select the measurement result of one patient.

  The CPU 51a determines which of the buttons C31 and C32 has been selected (step S303). When button C32 is selected, that is, when the sample is not designated as a sample of a stage 1 patient with HIV infection (NO in step S303), the CPU 51a returns the process to step S301, and the next sample Perform measurements.

  On the other hand, when button C31 is selected, that is, when the sample is designated as a sample of a stage 1 patient with HIV infection (YES in step S303), CPU 51a determines the PLT value included in the measurement result and It is compared with a predetermined sudden value exclusion range to determine whether or not the PLT value is within the sudden value exclusion range (step S304).

  The sudden value exclusion range is provided corresponding to the PLT value of stage 1 patients with HIV infection, and it is expected that the PLT value of stage 1 patients with HIV infection will fall within that range. A numerical range. That is, when the PLT value is out of the sudden value exclusion range, the subject of the sample has either a disease other than HIV infection or stages 2 to 4 of HIV infection, and therefore the PLT value Is suspected to be significantly deviated from the PLT values of standard HIV infection stage 1 patients.

  If the PLT value is out of the sudden value exclusion range (NO in step S304), the CPU 51a returns the process to step S301 without putting the measurement result in the target for abnormality detection, and the next sample Perform the measurement.

  On the other hand, when the PLT value is within the unexpected value exclusion range (YES in step S304), the CPU 51a stores the measurement result in the hard disk 51d as a target to be used for abnormality detection (step S305). By doing in this way, the measurement result used for abnormality detection is selected from the measurement results of the specimen.

  Using the PLT value included in the measurement result selected as described above, the CPU 51a of the information processing unit 5 performs abnormality detection for the PLT measurement of the sample analyzer. The processing in steps S306 to S311 is the same as that in steps S106 to S111 described in the first embodiment except that a PLT value is used instead of the RBC value, and thus the description thereof is omitted.

  By configuring as described above, it is possible to detect an abnormality in the PLT measurement of the sample analyzer using the PLT values of a plurality of subjects having a common stage disease. For this reason, a quality control sample and a healthy subject's sample (normal sample) are not required for abnormality detection about PLT measurement.

  In the present embodiment, a configuration has been described in which abnormality detection is performed on the PLT measurement of the sample analyzer using the PLT value of the stage 1 patient with HIV infection. However, the present invention is not limited to this. . Although the PLT value, HGB value, and hematocrit value of malaria infection patients show a decreasing tendency as compared with the normal state, the degree and the variation of the numerical value between patients differ depending on the stage. Moreover, although the WBC value of a tuberculosis patient shows the tendency to increase rather than normal, the dispersion | variation in the numerical value between the grades and patients differs for every stage. Furthermore, although the PLT value of a dengue fever patient also shows the tendency to decrease rather than normal, the degree and the dispersion | variation in a numerical value also differ between patients for every stage. For this reason, PLT values, HGB values, RBC values, hematocrit values, WBC values of tuberculosis patients, and dengue fever patients in the stages of malaria infection patients in a stage where the degree of variation from normal is small and the variation in numerical values is relatively small It is also possible to employ a configuration in which abnormality detection is performed on the HGB value, RBC value, hematocrit value, WBC value, and PLT value of the sample analyzer using the PLT value.

(Embodiment 4)
The present embodiment relates to a sample analysis system including a plurality of sample analyzers and an abnormality detection device that manages the sample analyzers.

[Sample analysis system configuration]
FIG. 10 is a schematic diagram showing the configuration of the sample analysis system according to the present embodiment. As shown in FIG. 10, the sample analysis system 400 includes a plurality of sample analyzers 1, 1,... The sample analyzers 1, 1,... And the abnormality detector 410 are installed in the same medical facility (hospital, examination center, etc.). The sample analyzers 1, 1,... And the abnormality detection device 410 are connected to each other via a communication network 420 such as a LAN so that data communication is possible.

  Since the configuration of the sample analyzer 1 is the same as that of the sample analyzer 1 according to Embodiment 1, the description thereof is omitted.

  The configuration of the abnormality detection device 410 will be described. FIG. 11 is a block diagram illustrating a configuration of the abnormality detection device 410.

  The abnormality detection device 410 is realized by a computer 410a. As illustrated in FIG. 11, the computer 410 a includes a main body 411, a display unit 412, and an input unit 413. The main body 411 includes a CPU 411a, a ROM 411b, a RAM 411c, a hard disk 411d, a reading device 411e, an input / output interface 411f, a communication interface 411g, and an image output interface 411h. The output interface 411f, the communication interface 411g, and the image output interface 411h are connected by a bus 411j.

  The reading device 411e can read a computer program 414a for causing the computer to function as the abnormality detection device 410 from the portable recording medium 414, and install the computer program 414a on the hard disk 411d.

[Operation of sample analysis system]
Next, the operation of the sample analysis system according to this embodiment will be described.

  The sample analysis system according to the present embodiment performs abnormality detection (internal accuracy management) of the sample analyzer using the measurement result of the sample of the subject having a specific disease measured by each sample analyzer. .

  FIG. 12 is a flowchart showing an abnormality detection operation in abnormality detection device 410 according to the present embodiment. First, a sample collected from a subject is measured by the sample analyzer 1, and a measurement result including an RBC value is obtained. The CPU 51a of the sample analyzer 1 transmits the measurement result to the abnormality detection device 410 via the communication interface 51g.

  The abnormality detection device 410 receives the measurement result transmitted from the sample analyzer 1 (step S401). The CPU 411a of the abnormality detection device 410 displays the received measurement result on the display unit 412 (step S402). At this time, the CPU 411a causes the display unit 52 to display a disease designation dialog for inquiring whether the sample is a sample of a subject suffering from HIV infection along with the measurement result. Since this disease designation dialog is the same as the disease designation dialog D1 described in the first embodiment, the description thereof is omitted.

  The CPU 411a determines which of the buttons C11 and C12 has been selected (step S403). When button C12 is selected, that is, when the sample is not designated as a sample of an HIV-infected patient (NO in step S403), CPU 411a returns the process to step S401 and receives the next measurement result. Wait.

  On the other hand, when button C11 is selected, that is, when the sample is designated as a sample of an HIV-infected patient (YES in step S403), CPU 411a determines the RBC value included in the measurement result and a predetermined burst. It is compared with the value exclusion range to determine whether or not the RBC value is within the sudden value exclusion range (step S404). Since this sudden value exclusion range is the same as that described in the first embodiment, the description thereof is omitted.

  When the RBC value is out of the sudden value exclusion range (NO in step S404), the CPU 411a returns the process to step S401 without putting the measurement result in the target for abnormality detection, and the next measurement result Wait for receipt of.

  On the other hand, when the RBC value is within the sudden value exclusion range (YES in step S104), the CPU 411a sends the measurement result to the sample analyzer 1 (that is, the sample analyzer 1 that has transmitted the measurement result). Is stored in the hard disk 411d as a target to be used for abnormality detection (step S405). By doing in this way, the measurement result used for abnormality detection is selected from the measurement results of the specimen.

  Next, the CPU 411a determines whether or not the number of measurement results used for abnormality detection of the same sample analyzer stored in the hard disk 411d has reached a predetermined number of data (for example, 20) ( Step S406). When the number of measurement results for abnormality detection of the same sample analyzer stored in the hard disk 411d has not reached the above-mentioned number of data (NO in step S406), the CPU 411a returns the process to step S401, and next Wait for reception of the measurement result.

  On the other hand, when the number of measurement results for abnormality detection of the same sample analyzer 1 stored in the hard disk 411d reaches the number of data described above (YES in step S406), the CPU 411a receives the same data from the hard disk 411d. The RBC value of the measurement result for abnormality detection of the sample analyzer 1 is read, the average value is calculated (step S407), and the sample analyzer 1 (that is, the sample analyzer 1 that is the transmission source of the above measurement result) is stored in the hard disk 411d. ) As abnormality detection history data (step S408).

  Furthermore, the CPU 411a compares the average RBC value (hereinafter referred to as “RBC average value”) with a predetermined management range (step S408). This management range guarantees that the RBC measurement can be normally performed for the apparatus state and the reagent of the sample analyzer 1 if the RBC average value is included therein. Such a management range is given by a statistical method, such as an average RBC value ± 2SD (standard deviation) of RBC values measured in the past in the sample analyzers 1, 1,. This is a management range for internal accuracy management of this medical facility. Note that the management range may be set in advance by the user.

  When the RBC average value is within the management range (YES in step S408), the CPU 411a causes the display unit 412 to display the management screen including the management graph and also displays the management result information indicating the management graph as described above. The measurement result is transmitted to the sample analyzer 1 that is the transmission source (step S409). In the sample analyzer 1, when management result information is received, a management screen including a management graph is output to the display unit 52. Since the management screen is the same as the management screen D2 described in the first embodiment, the description thereof is omitted. By checking the display unit 412 of the abnormality detection device 410 or the display unit 52 of the sample analyzer 1, the user can know that the RBC measurement can be normally performed in the sample analyzer 1. . Thereafter, the CPU 411a returns the process to step S401 and waits for reception of the next measurement result.

  On the other hand, when the RBC average value is out of the management range (NO in step S408), the CPU 411a causes the display unit 412 to display error information and also to the sample analyzer 1 that is the transmission source of the measurement result described above. Transmit (step S410). In the sample analyzer 1, when error information is received, the same information is output to the display unit 52. The user checks the display unit 412 of the abnormality detection device 410 or the display unit 52 of the sample analyzer 1 to confirm that an abnormality has occurred in at least one of the apparatus state and the reagent, that is, in the sample analyzer 1 normally. It can be seen that RBC measurements are not guaranteed to be possible. After the process of step S409, the CPU 411a ends the process.

(Other embodiments)
In Embodiments 1 to 4 described above, after selecting a measurement result of a subject having a specific disease, the measurement result is compared with the up-and-down range of the sudden value to measure a subject having a disease other than the specific disease. Although the configuration in which the result is excluded from the measurement result for abnormality detection has been described, the configuration is not limited to this. For example, when a user specifies a measurement result of a subject having a specific disease, the measurement result of a subject judged to be suspected of having another disease together with the specific disease is not specified, and only the specific disease is present. If only the measurement result of the subject judged to be specified is specified, the specified measurement result may be directly selected as the measurement result for abnormality detection without comparing the measurement result with the unexpected value exclusion range. Good.

  In the first to fourth embodiments described above, in order to select a measurement result to be used for abnormality detection, a measurement item to be compared with the sudden value exclusion range (RBC value in the first embodiment) and abnormality detection are performed. Although the case where the measurement item (the RBC value in the first embodiment) is the same has been described, the present invention is not limited to this. In dengue fever patients, both the red blood cell count and the white blood cell count have little change in the measured value from normal. For example, in order to select a measurement result to be used for abnormality detection, the measurement item to be compared with the sudden value exclusion range is the WBC value. It is also possible to make both measurement items different, for example, the measurement item for detecting an abnormality is an RBC value.

  In the above-described fourth embodiment, the configuration in which the internal accuracy management of the sample analyzer in the medical facility is performed as abnormality detection of the sample analyzer has been described. However, the present invention is not limited to this. It is also possible to connect the abnormality detection device to the sample analyzers of other medical facilities so that data communication is possible, and to perform abnormality detection (external accuracy management) of the sample analysis devices by a plurality of medical facilities.

  In the first to fourth embodiments described above, the sample analyzer 1 is a multi-item blood cell analyzer. However, the present invention is not limited to this, and the present invention can be applied to various sample analyzers. . For example, the present invention can be applied to other sample analyzers such as a urine analyzer.

  In Embodiments 1 to 4 described above, a sample is obtained by using only the measurement result of a specific measurement item (for example, RBC value) obtained from a subject having a specific disease (for example, HIV infection). Although abnormality detection was performed for the RBC measurement of the analyzer, the present invention is not limited to such an embodiment. For example, the measurement result of the subject is stored for each of various diseases, and when the measurement result of one disease reaches a predetermined number of data, a plurality of measurement results corresponding to the one disease are used. Anomaly detection may be performed.

  In this case, for example, when the one disease is an HIV infection, the CPU of the information processing unit sets an RBC value as a measurement item for abnormality detection, and when the one disease is dengue fever. Depending on the one disease, the measurement item and the number of measurement items that are the targets of abnormality detection are automatically changed and set, such as setting the RBC value and the WBC value as the measurement items of the abnormality detection target. Is preferred.

  Furthermore, when the one disease is an HIV infection, the CPU of the information processing unit sets a predetermined management range corresponding to the RBC value of the HIV infected patient, and the one disease is a malaria infection. In some cases, it is preferable to automatically change and set the predetermined management range according to the one disease, such as setting a predetermined management range corresponding to the RBC value of the malaria infection patient. In this case, the predetermined management range is preferably stored in advance in a storage device such as a hard disk.

  In the first to fourth embodiments described above, an average value of a plurality of measurement values such as RBC values is used as a comparison target with a predetermined management range, but the present invention is limited to such an embodiment. Not. For example, it is statistically derived from multiple measured values, such as the median, mode, and average value of the remaining measured values excluding the highest and lowest values among multiple measured values. Any value is acceptable.

DESCRIPTION OF SYMBOLS 1 Sample analyzer 2 Measurement unit 22 Sample preparation part 23 Detection part 5 Information processing unit 5a Computer 51a CPU
51d Hard disk 54a Computer program 400 Sample analysis system 410 Abnormality detection device 410a Computer 411a CPU
414a computer program

Claims (23)

A measurement unit for measuring a sample collected from a subject with respect to at least one measurement item;
A storage unit for storing a measurement result obtained by measuring the sample by the measurement unit;
Among the measurement results obtained by the measurement of the sample by the measurement unit, select a plurality of disease measurement results that are measurement results for a sample collected from a subject having a specific disease, and in the selected plurality of disease measurement results When a value statistically derived from a plurality of measurement values for a predetermined measurement item deviates from a predetermined reference, a control unit that outputs abnormality information regarding abnormality of the measurement unit;
Comprising
Sample analyzer. Of the measurement results obtained by the measurement unit, further comprising an input unit for accepting designation of the disease measurement result,
The control unit is configured to select the disease measurement result that has been designated by the input unit.
The sample analyzer according to claim 1. The control unit is configured to estimate a subject's disease related to the measurement result based on the measurement result obtained by the measurement unit, and to select a disease measurement result based on the estimation result.
The sample analyzer according to claim 1. The control unit selects a plurality of disease measurement results satisfying a predetermined condition from the plurality of disease measurement results, and statistically determines a plurality of measurement values for the predetermined measurement item in the selected plurality of disease measurement results. Is configured to output the abnormality information when the value derived in the above is out of the predetermined reference.
The sample analyzer according to any one of claims 1 to 3. The control unit selects, as the predetermined condition, a plurality of disease measurement results whose measurement values for the predetermined measurement item are within a predetermined range, and for the predetermined measurement items in the selected plurality of disease measurement results Configured to output the abnormality information when a value statistically derived from a plurality of measured values deviates from the predetermined reference;
The sample analyzer according to claim 4. The control unit selects, as the predetermined condition, a plurality of disease measurement results whose measurement values for the first measurement item are within a predetermined range, and the first measurement item in the selected plurality of disease measurement results and Is configured to output the abnormality information when a value statistically derived from a plurality of measurement values for different second measurement items deviates from the predetermined reference,
The sample analyzer according to claim 4. When a predetermined number of the disease measurement results are selected, the control unit obtains a value statistically derived from a plurality of measurement values for the predetermined measurement item in the selected predetermined number of disease measurement results. It is configured to output the abnormality information when it deviates from a predetermined standard.
The sample analyzer according to any one of claims 1 to 6. The measurement unit is configured to measure at least one kind of particles contained in a specimen collected from a subject,
The control unit, when a value statistically derived from a plurality of measurement values for a predetermined particle in a plurality of selected disease measurement results deviates from a predetermined reference, abnormal information regarding the abnormality of the measurement unit Configured to output,
The sample analyzer according to any one of claims 1 to 7. The measurement unit is configured to measure at least one blood cell contained in a sample collected from a subject.
The sample analyzer according to claim 8. In the subject having the specific disease, the predetermined measurement item is a measurement item with less variation in the measured value from normal compared to other measurement items.
The sample analyzer according to any one of claims 1 to 9. The control unit further selects, from among the measurement results obtained by the measurement unit, a healthy person measurement result that is a measurement result for a sample collected from a healthy person, the selected healthy person measurement result, and the plurality of the healthy person measurement results When a value statistically derived from a plurality of measurement values for a predetermined measurement item in a disease measurement result deviates from a predetermined reference, it is configured to output abnormality information regarding the abnormality of the measurement unit,
The sample analyzer according to claim 10. The control unit is configured to select a disease measurement result for a sample collected from a subject whose specific disease is in a specific stage among the measurement results obtained by the measurement unit,
The sample analyzer according to any one of claims 1 to 9. The specific stage is a stage in which variations in measurement values for the predetermined measurement item are less than other stages among a plurality of subjects having the specific disease.
The sample analyzer according to claim 12. The control unit is configured to change the predetermined reference according to a specific disease corresponding to a plurality of selected disease measurement results.
The sample analyzer according to any one of claims 1 to 13. The control unit is configured to change the predetermined item according to a specific disease corresponding to a plurality of selected disease measurement results.
The sample analyzer according to any one of claims 1 to 14. The specific disease is any one of HIV infection, tuberculosis, malaria, and dengue fever.
The sample analyzer according to any one of claims 1 to 15. The specific disease is HIV infection, and the predetermined measurement item is a red blood cell count.
The sample analyzer according to any one of claims 1 to 11. The specific disease is dengue fever, and the predetermined measurement item is at least one of red blood cell count and white blood cell count.
The sample analyzer according to any one of claims 1 to 11. The specific disease is HIV infection, and the predetermined measurement item is platelet count.
The sample analyzer according to claim 12 or 13. The value statistically derived from the plurality of measurement values is an average value of the plurality of measurement values or a median value of the plurality of measurement values.
The sample analyzer according to any one of claims 1 to 19. A sample analyzer including a measurement unit for measuring a sample collected from a subject with respect to at least one measurement item;
A storage device for storing a measurement result obtained by measuring the sample by the measurement unit;
Among the measurement results obtained by the measurement of the sample by the measurement unit, select a plurality of disease measurement results that are measurement results for a sample collected from a subject having a specific disease, and in the selected plurality of disease measurement results An anomaly detection device that outputs anomaly information related to anomalies of the measurement unit when a value statistically derived from a plurality of measurement values for a given measurement item deviates from a predetermined reference;
Comprising
Sample analysis system. An abnormality detection device that outputs abnormality information related to abnormality of the measurement unit of a sample analyzer including a measurement unit that measures a sample collected from a subject with respect to at least one measurement item,
An acquisition unit for acquiring a measurement result obtained by measuring the sample by the measurement unit;
A storage unit for storing the measurement result acquired by the acquisition unit;
Among the measurement results acquired by the acquisition unit, a plurality of disease measurement results that are measurement results for a sample collected from a subject having a specific disease are selected, and predetermined measurement items in the selected plurality of disease measurement results When a value statistically derived from a plurality of measured values for deviates from a predetermined standard, a control unit that outputs abnormality information regarding abnormality of the measurement unit;
Comprising
Anomaly detection device. A method for detecting an abnormality in a sample analyzer including a measurement unit that measures a sample collected from a subject for at least one measurement item,
Obtaining a measurement result obtained by measuring the specimen by the measurement unit;
Storing the acquired measurement results;
A step of selecting a plurality of disease measurement results that are measurement results for a sample collected from a subject having a specific disease from among the stored measurement results;
Outputting abnormality information relating to abnormality of the measurement unit when a value statistically derived from a plurality of measurement values for a predetermined measurement item in a plurality of selected disease measurement results deviates from a predetermined standard; ,
An abnormality detection method for a sample analyzer.

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