WO2022270267A1 - 診断システム、自動分析装置及び診断方法 - Google Patents
診断システム、自動分析装置及び診断方法 Download PDFInfo
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Definitions
- the present invention provides a diagnostic system for diagnosing whether various sensors mounted in an automatic analyzer for measuring trace components contained in biological samples such as blood and urine are normal or abnormal, and this diagnostic system.
- the present invention relates to a mounted automatic analyzer and a diagnostic method.
- An automatic analyzer generally consists of more than 1,000 types of parts. When some parts fail, it is necessary to repair or replace the failed parts as soon as possible in order to reduce the downtime of the autoanalyzer. Further, the parts of the automatic analyzer include a plurality of types of periodic replacement parts that require periodic replacement. It is also important to prevent downtime due to failures by timely replacing these periodic replacement parts.
- Patent Document 1 a failure prediction algorithm is generated from data related to the occurrence of failures in an automatic analyzer, and this algorithm is used to detect failures such as breakage of parts based on at least one data of calibration and quality control of the automatic analyzer. is disclosed.
- Automatic analyzers generally measure micromoles/liter ( ⁇ mol/L) or less of trace components, so the sensors are designed to be highly sensitive, and there may be differences in the response tendency of the sensor depending on the situation. There is room for improvement in terms of accuracy in diagnosing the status of various sensors involved in the operation control and status monitoring of each part of automatic analyzers that require high measurement accuracy, including sensors that measure such target components.
- An object of the present invention is to provide a diagnostic system, an automatic analyzer, and a diagnostic method that can improve the diagnostic accuracy of the sensors of the automatic analyzer.
- the present invention provides a diagnostic system for diagnosing a sensor that is provided in an automatic analyzer and outputs an analog electric signal, comprising data of the electric signal output by the sensor and a replacement history of the sensor. and a processing device for processing the data recorded in the memory, wherein the processing device is set among data of electric signals output by past sensors used in the automatic analysis device
- the data of the reference period is read from the memory, the statistical value of the data of the reference period is calculated, and the electrical signal data output from the diagnosis target sensor being used in the automatic analyzer is recorded during the set evaluation period.
- read the obtained data from the memory calculate the statistical value of the data in the evaluation period, and determine the abnormality of the sensor to be diagnosed based on the difference obtained from the statistical value in the reference period and the statistical value in the evaluation period.
- FIG. 1 is a plan view schematically showing one configuration example of an automatic analyzer to which the diagnostic system according to the first embodiment of the present invention is applied; Schematic diagram of the sensor used for sample measurement in the automatic analyzer shown in Figure 1 Block diagram showing the data processing flow in the automatic analyzer shown in FIG. Diagram showing an example of time-series data of luminescence intensity measured during sample measurement A diagram showing an example of time-series data of voltage values measured during sample measurement A diagram showing an example of time-series data of current values measured during sample measurement Diagram showing an example of time-series data of resistance values measured during sample measurement Block diagram showing the sensor diagnosis processing flow in the server Flowchart showing detailed procedures of determination calculation processing and replacement necessity determination processing in the flow of FIG.
- FIG. 4 is a block diagram showing the data processing flow in the diagnostic system according to the second embodiment of the present invention.
- Diagnostic target sensor In the specification of the present application, a sensor that is currently used in an automatic analyzer and that is to be diagnosed as being abnormal or normal is referred to as a diagnostic target sensor. Diagnosis target sensors include all sensors that are mounted in automatic analyzers and have electrical behavior. In other words, in addition to sensors that measure measurement items (for example, concentrations of target components), sensors related to the operation control of automatic analyzers, sensors used for monitoring the state of each part of automatic analyzers, etc. are also examples of diagnostic target sensors. . Therefore, for example, a pressure sensor installed in a fluid flow path and used to check the flow path state by voltage also corresponds to the diagnostic target sensor. Other examples of sensors to be diagnosed include a pressure sensor used to convert the liquid feed pressure of the liquid feed pump into an electrical signal and check it, and an impedance meter used to check the agitation strength of the agitation mechanism using impedance. .
- Past sensors A used sensor that has already been removed from the automatic analyzer due to replacement after being used in the automatic analyzer is referred to as a past sensor. It goes without saying that the past sensor may correspond to a sensor that was used in the past in an automatic analyzer that uses the sensor to be diagnosed, but it is a separate body from the automatic analyzer that uses the sensor to be diagnosed. Sensors previously used in automated analyzers can be included. In other words, sensor data of other automatic analyzers can also serve as a basis for failure diagnosis indicators.
- reference period A set period during which it is estimated that the sensor was in a normal state in the past is referred to as a reference period.
- Reference sample Samples such as standard samples (calibration samples), QC samples (quality control samples), dummy samples, etc., which are measured by an automatic analyzer prior to measurement of patient specimens, are referred to as reference samples.
- Reference samples include at least one (one or more) of standard samples, QC samples, and dummy samples.
- a standard sample is a prepared sample that is measured to create a calibration curve during calibration.
- a QC sample is a prepared sample that is measured during QC (Quality Control).
- a dummy sample is a predetermined sample that is measured as a preparatory operation before measurement of a patient specimen. Standard samples, QC samples, dummy samples, etc. are all produced in lots, and a plurality of samples belonging to the same lot can be sequentially measured with the same sensor of the automatic analyzer.
- analog electrical signal data output from a sensor used in the past in an automatic analyzer is statistically used as an index to diagnose a sensor currently in use.
- the sensors to be diagnosed are various sensors used for measuring analysis items of biological samples, such as biochemical analyzers, immunoanalyzers, blood coagulation time measuring devices, and ISE measuring devices. type sensor and the like.
- the analog electrical signal output by the sensor is not limited to the measured value of the sample analysis item (concentration of the component to be analyzed). response values are also included. If the object to be controlled is current or resistance instead of voltage, the voltage value measured in response to the current or resistance also corresponds to the electric signal output by the sensor.
- memory storage media such as RAM, ROM, HHD, SSD, etc.
- processing device CPU, etc.
- This functionality can also reside in a computer that is either embedded in the automated analyzer or directly connected by cable.
- the same function can be provided to a computer (server or the like) communicably connected to the computer of the automatic analyzer via a global network or a local area network.
- the data obtained by A/D converting the analog electric signal output by the sensor of the automatic analyzer and the data of the replacement history of the sensor are recorded in memory (for example, HHD or SSD).
- the data recorded in the memory are accumulated, and both the data obtained from the diagnosis target sensor and the data obtained from the past sensors are recorded.
- a diagnostic program is stored in a memory (for example, ROM), and the data recorded in the memory is processed by a processing device (for example, CPU) to diagnose the state of the sensor.
- the processes executed by the processor at that time are roughly divided into the following first to third processes.
- the first process is a process of reading from the memory the data of the set reference period among the data of the electrical signals output by the past sensors.
- the second process is the process of calculating the statistics of the reference period data as a diagnostic indicator of the sensor.
- the third process is a process of diagnosing the state of the diagnosis target sensor based on the index calculated in the second process. In this third process, among the data of the electrical signal output by the diagnosis target sensor, the data recorded during the set evaluation period is read from the memory, and the statistical value of the data during the evaluation period is calculated. Next, it is determined whether the sensor to be diagnosed is normal or abnormal based on the difference obtained from the statistical value of the reference period and the statistical value of the evaluation period. Preferred forms of each of the first to third processes are exemplified below.
- the reference period it is important how to specify the reference period. It is essential to set a period during which the sensor was in good condition in the past as the reference period in order to generate a diagnostic index for the sensor to be diagnosed based on data acquired by the sensor in the past during the reference period. For example, if it is clear from the inspection history data of a service person that the sensor was actually operating without any problems, that period can be set as the reference period.
- One preferred example is to use the period set by the reference period as the reference period.
- the predetermined period as an exclusion period to be excluded from the reference period and the length (or start and end) of the reference period depend on the settings.
- the predetermined period as the exclusion period may be set arbitrarily within a period excluding the normal operation period of the sensor based on inspection history data, but the past sensor is replaced with a new sensor.
- a reasonable example is to set the immediately preceding fixed period as the predetermined period. This is because when a sensor is replaced due to deterioration or failure, there is a possibility that the sensor has already become abnormal for a certain period immediately before the replacement. If the length of the predetermined period is appropriately set, the specified reference period will have a certain degree of validity as the time period during which the sensor was operating normally.
- the processing unit calculates the point in time a predetermined period before the past sensor replacement based on the past sensor replacement history. It is possible to specify a set period ending at that time point as a reference period. For example, if the length of the predetermined period is 30 days (January) and the length of the reference period is 1 week, then 7 days from 37 days to 31 days before the end of the usage period of the past sensor (at the time of replacement). Days are automatically identified as the reference period.
- the timing and intervals of sensor replacement may differ depending on the automatic analyzer, so the reference period may differ for each automatic analyzer.
- multiple reference periods can be set according to the number of past sensors that have been used.
- Data Extraction Data extracted from the identified reference period constitutes a basic data group of diagnostic indices for the diagnosis target sensor. All the data during the reference period can be used as the basic data group, or a set number of data extracted randomly or according to a set rule (for example, at regular time intervals) can be used as the basic data group. If the past sensor and diagnostic target sensor are used to measure multiple measurement items (TSH, CEA, etc.), the basic data group is a set number of data extracted for each of the selected two or more measurement items. can also be
- the basic data group to be extracted from the reference period it is possible to preferably use the data obtained by past sensors during the measurement of at least one of the patient specimen and the reference sample.
- past data obtained by sensors during QC measurement is particularly preferable as a group of basic data, compared to, for example, measurement of patient specimens in which the concentration of coexisting components other than measurement items varies.
- QC samples to be measured by QC are ready-made products with stable components, and QC measurements are often performed at least once a day for each automatic analyzer, so there is no bias in the timing of data generation and a large number of data is required. This is because it can be sufficiently secured.
- analysis of sensor response data during measurement of patient specimens revealed that there were cases where the electrical conductivity was affected by the amount of lipid contained in the patient specimen.
- the above-mentioned exclusion period It is also possible to utilize the data at the time of measurement of the reference sample for the period. For example, if this predetermined period is set as the confirmation period, and the fact that the sensor was in a normal state in the past during the reference period can be denied from the data related to the measurement of the reference sample during the predetermined period, the past sensor data is Avoid extraction as basic data.
- the history of calibrations, QC measurements, or dummy measurements performed during a given period of time contains failures or anomalies, sensor malfunctions will cause calibration or QC failures or dummy measurement anomalies. could have done.
- the processing device reads out from the memory the history of the measurements of the reference sample performed in a predetermined period, and if all the measurements of the reference sample are successful or normal, the past allow the extraction of the basic data group for the sensors of
- the reference period is set only for past sensors for which there were no errors in QC or the like executed during a predetermined period, and the data of the reference period obtained by the sensor is used as a basic data group (or part thereof). extracted. Data of sensors with errors in QC or the like are filtered and excluded from the basic data because it is doubtful whether the sensors were in a normal state.
- the diagnostic target sensor when used to measure a plurality of measurement items, from the data of the reference period of the past sensor, a set number of data are extracted for each of the selected two or more measurement items, It is desirable to aggregate all the extracted data and make statistics.
- the plurality of items to be selected can be all measurement items, or a plurality of some measurement items can be selected.
- a statistical value calculated based on the data obtained by the measurement of the measurement items selected in this manner serves as an index for diagnosis of the sensor to be diagnosed. Examples of the statistical value calculated here include an average value, a moving average value, a median value, and at least one of the standard deviation values, or a value based on at least one of these values (Z score, etc.). .
- the size of the electrical signal data (raw data) obtained from the sensor will be affected by differences in the production lot, manufacturer, etc. of the analysis reagent kit installed in each automatic analyzer.
- the data of the diagnosis target sensor (hereinafter abbreviated as target data) during the set evaluation period are statistically obtained in the same manner as in the second process.
- the statistical value of the target data is a value reflecting the current state of the sensor to be diagnosed, and the current state of the sensor to be diagnosed is diagnosed by comparing this with the statistical value of the basic data calculated in the second process.
- the evaluation period is a set period ending at the present, such as the most recent week or January.
- the first process and the second process are different except that the target period of data extraction is the evaluation period, in other words, the sensor that is the output source of the data to be extracted is the diagnosis target sensor.
- a method similar to the process can be adopted. For example, as with data from past sensors, it is preferable to convert individual data from sensors to be diagnosed into deviation data before using the statistics.
- all data during the evaluation period can be statistically collected as a target data group, or a set number of data extracted randomly or according to a set rule (for example, at regular time intervals) can be statistically collected as a target data group. can.
- a set number of data can be extracted as a target data group for each of the selected two or more measurement items. .
- the measurement item selected as the basic data of the index in the first process it is preferable to select the measurement item selected as the basic data of the index in the first process.
- candidates for the data to be extracted as the target data group the data obtained by the diagnostic target sensor during the measurement of at least one of the patient specimen and the reference sample can be adopted, and the data obtained during QC measurement is particularly preferable. It is the same as the first process.
- a statistical value based on the target data at least one of the average value, moving average value, median value, and standard deviation, or a value based on at least one of these values (Z score, etc.) is exemplified. 2 process.
- the statistical values calculated in the second process are indicators obtained by statistically analyzing past sensor data during a reference period (that is, basic data) in which normality is estimated. Therefore, if the difference (magnitude of the absolute value of the difference) between the statistical value of the target data calculated in this process and the statistical value of the basic data is large, it is suspected that the diagnosis target sensor is abnormal.
- a first judgment value for abnormality judgment is set for the difference between the statistical value of the basic data and the statistical value of the target data.
- an algorithm that simply compares the difference between the statistical values with the first judgment value can be applied.
- a significant difference test based on the statistical value of the basic data and the statistical value of the target data determines whether the difference between the data in the reference period and the data in the evaluation period is greater than the first judgment value, that is, the sensor to be diagnosed is abnormal.
- a diagnostic algorithm can also be applied to determine whether As a specific example, for example, the average value and standard deviation of the basic data are calculated as the statistical values of the data in the reference period.
- the deviation from the average value of the basic data is divided by the standard deviation of the basic data to calculate the Z score, and the diagnosis is made based on the comparison between the average value of the calculated Z score and the first judgment value.
- the magnitude (absolute value) of the average value of the Z-scores is less than or equal to the first judgment value, the hypothesis that the sensor to be diagnosed is abnormal has a significant difference level (for example, a probability of 5%). Rejected value.
- the magnitude of the average value of the Z-scores is greater than the first determination value, it is estimated that the sensor to be diagnosed is abnormal. If the magnitude of the average value of the Z-scores is equal to or less than the first determination position, it is estimated that the sensor to be diagnosed is not abnormal.
- the diagnosis result of such a diagnosis target sensor (including the diagnosis of normality and the diagnosis of neither abnormality nor normal, which will be described later) is displayed and output by the processing device on a UI (user interface) such as a monitor connected to a computer, and the user etc. will be notified.
- a UI user interface
- a setting screen is displayed on which the determination conditions of the diagnosis target sensor can be set, and the setting of the determination conditions can be changed from the preset value on the setting screen so that the diagnostic sensitivity of the diagnosis target sensor by the processing device can be adjusted.
- Judgment conditions set on the setting screen include, for example, the length of the reference period and the predetermined period, measurement items (TSH, CEA, etc.), first judgment value, second judgment value (described later), data type (voltage, current, resistance, etc.), measurement type (patient sample measurement, calibration, QC measurement, etc.).
- TSH measurement items
- CEA CEA
- second judgment value described later
- data type voltage, current, resistance, etc.
- measurement type patient sample measurement, calibration, QC measurement, etc.
- the processing device determines that the sensor to be diagnosed is normal when the difference of the target data group with respect to the basic data group is smaller than the second determination value. be able to.
- the determination of estimating the normality of the sensor to be diagnosed can also apply the significance test in the same manner as the determination of estimating abnormality.
- the basic data is added to convert the individual target data into a Z score, and it is determined whether the diagnostic target sensor is normal based on the comparison between the average value of the Z scores and the second determination value.
- the magnitude (absolute value) of the average value of the Z-score is greater than or equal to this value, the hypothesis that the sensor to be diagnosed is normal is at a significant difference level (for example, a probability of 5%). Rejected value.
- the magnitude of the average value of the Z-scores is smaller than the second determination value, it is estimated that the sensor to be diagnosed is normal. If it can be concluded that the sensor to be diagnosed is in a normal state and does not need to be replaced, it is useful for sensor maintenance planning and inventory management.
- the operating concept of the automatic analyzer is to determine that replacement is unnecessary (normal) from the viewpoint of avoiding stoppage of the automatic analyzer due to maintenance as much as possible, or that replacement is required (abnormal) from the viewpoint of emphasizing measurement accuracy.
- the handling can be flexibly set according to the situation.
- the cause may be an abnormality in the sensor to be diagnosed, or a sensor other than the sensor to be diagnosed. It is also possible that the component parts of the automatic analyzer are defective. Therefore, instead of concluding that the sensor to be diagnosed is abnormal or normal, the possibility of defects in various components related to measurement including the sensor to be diagnosed is notified, and the cause of the malfunction is identified. It is conceivable to urge the user, the service person, or the like to make the determination.
- the processing device when the difference between the statistical value of the basic data and the statistical value of the target data is equal to or less than the first determination value and the second determination value or more, the factors of the components other than the diagnosis target sensor are determined.
- the diagnosis target sensor is determined by the processing device.
- the processing device when the difference between the statistical value of the basic data and the statistical value of the target data is equal to or less than the first judgment value and equal to or more than the second judgment value, the processing device notifies the user or the like through the UI. can.
- the abnormality and normality of the diagnostic target sensor were each determined by the one-sided t-test, but it is also conceivable to apply the two-sided t-test.
- the significance test by comparison with the basic data as described above is useful.
- automated analyzers used to measure patient specimens are not operated with abnormal sensor conditions. Therefore, it is difficult to collect sensor data in abnormal conditions. difficult to define.
- the state of the sensor to be diagnosed can be preferably diagnosed. can.
- the present invention can be applied to automatic analyzers.
- Analysis units mounted on automatic analyzers include, for example, biochemical analyzers and immunological analyzers.
- this is only an example, and the present invention is not limited to the examples described below, and can be widely applied to automatic analyzers equipped with analysis units that analyze samples based on the reaction results with reagents.
- applications may include an automatic analyzer equipped with a mass spectrometer used for clinical examination, a coagulation analyzer for measuring blood coagulation time, and the like.
- the present invention can also be applied to a compound type automatic analyzer equipped with a plurality of types of these various analysis units, and an automatic analysis system including at least one automatic analyzer.
- a specific embodiment in which the present invention is applied to diagnosis of a sensor for measuring measurement items of an automatic analyzer will be described below with reference to the drawings.
- FIG. 1 is a plan view schematically showing one configuration example of an automatic analyzer to which a diagnostic system according to a first embodiment of the present invention is applied.
- the automatic analyzer 1 shown in FIG. 1 includes a rack transport line 2, an incubator disk 3, a first transport mechanism 4, a holding member 5, a sample dispensing nozzle 6, a reagent disk 7, a reagent dispensing nozzle 8, and a second transport mechanism. 9, an analysis unit 10 and a control device 20;
- the rack transport line 2 is a unit that transports the rack R, and transports the rack R to the sample dispensing position by the sample dispensing nozzle 6 .
- a plurality of sample containers C1 holding samples can be placed on the rack R.
- FIG. 1 a configuration for line-conveying the sample is exemplified, but a disk-shaped conveying unit that rotates to convey the sample may be provided.
- the incubator disk 3 is a disk on which reaction containers C2 are installed, and a plurality of reaction containers C2 can be installed in a ring.
- the incubator disk 3 is driven to rotate by a driving device (not shown), and can move any reaction container C2 to a plurality of predetermined positions including the pipetting position by the sample pipetting nozzle 6 .
- the first transport mechanism 4 is a unit that transports the sample pipetting chip and the reaction container C2, and is operable in the three axial directions of XYZ.
- a sample pipetting tip and a reaction container are transported between the position P and the holding member 5 .
- the stirring mechanism M is a unit that stirs the sample contained in the reaction container C2.
- a discard hole D is a hole for discarding a used sample pipetting tip or reaction container.
- the tip attachment position P is the position where the sample dispensing tip is attached to the sample dispensing nozzle 6 .
- the holding member 5 is a member that holds the sample pipetting tip and the reaction container C2, and a plurality of unused reaction containers C2 and sample pipetting tips are installed.
- An unused reaction vessel C2 held by the holding member 5 is transferred to the first transfer mechanism 4 and installed at a predetermined position on the incubator disk 3.
- the unused sample dispensing tip held by the holding member 5 is transferred to the first transfer mechanism 4 and installed at the tip mounting position P. As shown in FIG.
- the sample dispensing nozzle 6 is a unit that aspirates and discharges the sample.
- the sample pipetting nozzle 6 is configured to be rotatable and vertically movable. and put it on.
- a sample pipetting nozzle 6 equipped with a sample pipetting tip moves and descends above the sample container C1 placed on the rack R to suck a predetermined amount of the sample held in the sample container C1.
- the sample pipetting nozzle 6 moves and descends above the incubator disk 3 and discharges the sample into the unused reaction container C2 held by the incubator disk 3 .
- the sample pipetting nozzle 6 moves above the disposal hole D, and discards the used sample pipetting tip from the disposal hole D.
- the reagent disk 7 is a disk-shaped unit in which a plurality of reagent containers C3 are installed.
- a disk cover 7a (the left portion of which is partially broken in FIG. 1) is provided on the reagent disk 7 to keep the inside of the reagent disk 7 at a predetermined temperature.
- An opening 7b is provided in a portion near the incubator disk 3 in the disk cover 7a.
- the reagent dispensing nozzle 8 is a unit that aspirates and discharges the reagent.
- This reagent pipetting nozzle 8 can rotate and move up and down like the sample pipetting nozzle 6, and can be rotated and moved downward above the opening 7b of the disk cover 7a, and the tip of the reagent pipetting nozzle 8 can be moved to a predetermined position. to aspirate a predetermined amount of reagent.
- the reagent pipetting nozzle 8 rises and rotates above a predetermined position on the incubator disk 3 to discharge the reagent into the reaction container C2 containing the sample.
- the reaction container C2 into which the sample and reagent have been discharged is moved to a predetermined position by the rotation of the incubator disk 3, and is transported to the stirring mechanism M by the first transport mechanism 4.
- the stirring mechanism M stirs and mixes the sample and the reagent in the reaction container C2 by rotating the reaction container C2.
- the stirred reaction container C2 is returned to a predetermined position on the incubator disk 3 by the first transport mechanism 4. As shown in FIG.
- the second transport mechanism 9 is a unit that transfers the reaction container C2 between the incubator disk 3 and the analysis unit 10, and is configured to be rotatable and vertically movable.
- the second transport mechanism 9 grips and lifts the reaction container C2 that has been returned to the incubator disk 3 after the sample and reagent have been mixed and has passed a predetermined reaction time, and transfers it to the analysis unit 10 by rotational movement.
- the analysis unit 10 is a unit that measures measurement items such as specific biological components and chemical substances contained in the reaction liquid in the reaction container C2.
- a diagnosis target sensor to be diagnosed in this embodiment is used in this analysis unit 10 . Sensors are described later.
- the control device 20 is a computer including a memory 21 such as RAM, ROM, HDD, SSD, etc., a processing device 22 such as a CPU, a timer, and the like.
- This control device 20 controls each device mounted on the automatic analyzer 1, and records and processes data input from the analysis unit 10 and the like. Further, the control device 20 is, for example, housed in the exterior of the automatic analyzer, or installed outside the exterior and directly connected to the main body of the automatic analyzer by wire or wirelessly.
- the control device 20 is connected to the server 30 via the communication interface 23, the network NW, and the communication interface 33.
- the server 30 is also a computer including a memory 31 such as a RAM, a ROM, an HDD, and an SSD, a processing device 32 such as a CPU, a timer, and the like.
- the diagnostic function of the diagnostic target sensor used in the automatic analyzer 1 is installed in the server 30 in this embodiment.
- the server 30 records data acquired by the automatic analyzer 1 or a plurality of automatic analyzers including the automatic analyzer 1 in the memory 31, and processes the data recorded in the memory 31 by the processor 32 to 1 diagnostic object sensor is diagnosed (described later).
- FIG. 2 is a schematic diagram of a sensor used for sample measurement in the automatic analyzer shown in FIG.
- An analysis unit 10 of the automatic analyzer 1 is equipped with a flow cell type sensor 11 .
- the sensor 11 includes a flow cell 12 and three electrodes (a reference electrode 13, a counter electrode 14, and a working electrode 15) provided inside the flow cell 12.
- the three electrodes are controlled by a potentiostat 16 to have a target voltage. When a specific voltage is applied between the reference electrode 13 and the working electrode 15 by the potentiostat 16 while the reaction product RP of the sample and the reagent is collected by the reference electrode 13, the reaction product RP emits light.
- the emission intensity of the reaction product RP is detected by a photomultiplier tube 17 placed on the opposite side (upper side in FIG. 2) of the reference electrode 13 across the flow cell 12 .
- the luminescence intensity detected by the photomultiplier tube 17 is digitized by the A/D converter 18, and is measured in the memory 21 (or the memory of the sensor 11) as raw data of the measured value of the measurement item through the raw data recording process P1. Recorded with date and time. At that time, the current value, voltage value and resistance value generated between the counter electrode 14 and the working electrode 15 by applying a voltage between the reference electrode 13 and the working electrode 15 are measured by the potentiostat 16 .
- the automatic analyzer 1 is started by turning on the power.
- the reagent container C3 is installed and the reagent is initially filled, the temperature inside the reagent disk 7 is adjusted, the internal standard solution is continuously measured by applying a constant voltage to the electrode, and the potential of the electrode of the sensor 11 is stabilized. and perform maintenance as necessary.
- the sensor 11 in the measurement of standard samples, QC samples, dummy samples, etc., including patient samples, the sensor 11 repeatedly performs the steps of washing the flow cell 12, conditioning, and detecting the luminescence intensity. Since the proper value of the voltage applied to the electrodes differs in each of these steps, the voltage applied to the electrodes by the potentiostat 16 is complicatedly controlled in one measurement. For example, in the cleaning process, a specific pattern of voltage is applied to the electrodes for a certain period of time while the cleaning solution is being supplied to the flow cell 12 so that the reaction product RP from the previous measurement does not remain in the sensor 11 .
- the conditioning step a voltage pattern different from that in the washing step is applied for a certain period of time while an auxiliary reagent is being supplied to the flow cell 12 in order to make the electrodes suitable for measurement.
- a voltage necessary for the luminescence reaction of the reaction product RP captured by the reference electrode 13 is applied.
- a complex voltage pattern is continuously and accurately repeatedly applied to the electrodes as the sample is measured.
- FIG. 3 is a block diagram showing the processing flow in the automatic analyzer shown in FIG. 1; Data from the sensor 11 , mechanism section 91 , sample data reader 92 , reagent data reader 93 , and UI (user interface) 94 are input to the controller 20 of the automatic analyzer 1 .
- Data recorded in the raw data recording process P1 is input from the sensor 11 to the control device 20 at any time.
- the data input from the sensor 11 to the control device 20 includes not only data at the time of patient sample measurement, but also each type of measurement, such as QC measurement and calibration, as well as dummy data that is performed as a preparatory operation immediately before the patient sample measurement. It also includes data from measurements.
- the mechanical unit 91 is a general term for each piece of hardware (sample dispensing nozzle 6, incubator disk 3, etc.) installed in the automatic analyzer 1.
- the data input from the mechanism unit 91 to the control device 20 include, for example, the operation timing, operation amount and current value of each motor, signals from sensors used for controlling each motor, and logs such as opening/closing timing and current values of fluid valves. Data.
- the sample data reader 92 is a device (for example, a barcode or RFID reader) that reads sample registration data, and is provided in the automatic analyzer 1 .
- a storage medium such as a bar code or RFID is attached to the sample container C1, and the sample data reader 92 reads sample data recorded in the storage medium.
- Data read by the sample data reader 92 and input to the controller 20 is, for example, a sample ID.
- the reagent data reader 93 is a device (for example, a bar code or RFID reader) that reads reagent registration data, and is provided in the automatic analyzer 1 .
- a storage medium such as a bar code or RFID is attached to the reagent container C3, and the reagent data reader 93 reads reagent data recorded in the storage medium.
- the data read by the reagent data reader 93 and input to the controller 20 are, for example, reagent IDs, lot numbers, expiration dates, and the like.
- the UI 94 consists of a monitor and an input device, and is provided in the automatic analyzer 1 so that the user can browse data and input data to the control device 20 .
- Various data are input to the UI 94.
- reagent IDs, lot numbers, expiration dates, on-board expiration dates, required remaining amounts, etc. of reagents used for sample measurement are input to the controller 20 as data related to reagents.
- a sample ID, a measurement type for the sample ID, measurement items, and the like are input to the control device 20 .
- Various data input to the control device 20 as described above are processed in real time by the processing device 22 and sent to the server 30 via the communication interface 23 as a log file.
- the processing executed by the processing device 22 includes, for example, measurement data conversion processing P2 and measurement data recording processing P3.
- operation log recording process P4 reagent data recording process P5, sample data recording process P6, calibration data recording process P7, quality control data recording process P8, maintenance history recording process P9, A log file generation process P10 is included.
- operation log recording process P4 reagent data recording process P5
- sample data recording process P6, calibration data recording process P7, quality control data recording process P8, maintenance history recording process P9 is included.
- the processing device 22 converts the measured values (raw data) input from the sensor 11 into effective values.
- the measured values input from the sensor 11 are each raw data of emission intensity, current value, voltage value, and resistance value.
- FIG. 4 is an example of time-series data of emission intensity measured during sample measurement
- FIG. 5 is an example of time-series data of voltage values
- FIG. 6 is an example of time-series data of current values
- the horizontal axis of each figure corresponds to discrete time, and measured values for each discrete time are plotted.
- the controller 20 applies a voltage to the electrodes at a specific timing from the start of measurement and acquires data. In the examples shown in FIGS. 4 to 7, a predetermined voltage is applied to the reference electrode 13 and the working electrode 15 at the 41st point for all data.
- the processing device 22 converts the measured value (raw data) input from the sensor 11 for each measurement into the following two formulas stored in advance in the memory 21 (for example, ROM). to convert it to a valid value.
- Measurement data recording process In the measurement data recording process P3, the processing device 22 assigns a measurement ID to each measurement, and records the raw data and effective values of the measurement values in the memory 21 in association with the measurement ID.
- the processing device 22 records the operation log input from the mechanism section 91 and the sensor 11 in the memory 21.
- the operation log input from the mechanism unit 91 or the like includes, for example, the operation timing and operation amount of each motor, the current value of the motor, the signal of the sensor for controlling the operation of the motor, the opening/closing timing and current value of the fluid valve, and the like. included.
- the processing device 22 compares the reagent data input from the reagent data reading device 93 with the condition data recorded in advance in the memory 21, and uses the condition data when the condition data is met. It is recorded in the memory 21 as a possible reagent.
- the condition data for matching reagent data includes reagent ID, lot number, expiration date, on-board expiration date, required remaining amount, etc., and is input by the UI 94 or another computer and sent to the control device via the communication interface 23. 20 and recorded in memory 21 . Further, in the reagent data recording process P5, the processing device 22 records the history of the used reagent in the memory 21 for each measurement ID. As a result, the raw data and valid values of the measurement values and the data of the reagent used for the measurement are linked via the measurement ID.
- sample data recording process P6 the processing device 22 compares the sample data input from the sample data reader 92 with the condition data recorded in advance in the memory 21, and measures when the condition data is matched.
- the sample is recorded in the memory 21 as a possible sample, and the measurement is performed in a timely manner.
- Condition data for comparing sample data includes sample ID, measurement type (QC measurement, patient sample measurement, etc.), measurement items, etc., and is input by UI 94 or another computer and sent to control device 20 via communication interface 23. , and recorded in the memory 21.
- the processing device 22 records in the memory 21 the execution history of the calibration for calculating the concentration of the measurement item of the patient sample each time the calibration is performed. Specifically, the calculated calibration curve, the date and time of calibration, the measurement items, the success or failure of calibration, and the like are recorded in the memory 21 as the calibration execution history.
- the processor 22 stores the QC measurement implementation history in the memory 21.
- the processing device 22 records the maintenance history in the memory 21.
- the processing device 22 collects data necessary for failure diagnosis of the sensor 11 among the data stored in the memory 21 for each measurement to generate a log file. Also, the processing device 22 transmits the log file to the server 30 via the communication interface 23 and the network NW. Since the log file is created for each measurement and sequentially uploaded to the server 30, the server 30 stores not only data measured by the sensor 11 currently used in the automatic analyzer 1, but also Data of measurements by the sensors 11 that were used are also accumulated.
- FIG. 8 is a block diagram showing a sensor diagnosis processing flow by the processing device of the server.
- the diagnostic function of the sensor 11 of the automatic analyzer 1 is executed by the server 30, and the server 30 constitutes a diagnostic system.
- the processing device 32 Upon receiving the log file from the automatic analyzer 1, the processing device 32 records the log file in the memory 31 in the log file storage process P21. In the subsequent determination data extraction process P22, the processing device 32 extracts data necessary for diagnosing the sensor 11 from the log file stored in the memory 31, such as effective values of measured values, QC results, calibration results, replacement history of the sensor 11, and the like.
- the processor 32 stores the extracted data required for diagnosis in the memory 31.
- the processing device 32 reads the necessary data again from the memory 31 in the determination calculation process P24. Then, based on the calculation result of the determination calculation process P24, the processing device 32 diagnoses the sensor 11 in the replacement necessity determination process P25 and determines the necessity of replacement.
- the determination result is transmitted to the automatic analyzer 1 via the communication interface 33 and the network NW, and notified to the user or the like via the display of the UI 94 . It is also possible to display the diagnostic result on the UI (user interface) 34 of the server 30 .
- the UI 34 of the server 30 is similar to the UI 94 of the control device 20 of the automatic analyzer 1.
- FIG. 9 is a flow chart showing detailed procedures of determination calculation processing and replacement necessity determination processing in the flow of FIG. 8, and FIG. 10 is a flow chart showing detailed procedures of basic data group extraction in the flow of FIG.
- the judgment calculation processing corresponds to the procedure of steps 100 to 130 in the flow of FIG. 9, and corresponds to the first process and second process described in the embodiments of the invention.
- Step 100 the processing device 32 first reads the determination data obtained by the sensor 11 in the past from the memory 31 and uses it as the basis for the diagnostic index of the sensor 11 currently used in the automatic analyzer 1.
- a data group is extracted from the decision data (step 100).
- a reference period is set in which the automatic analyzer 1 presumes that the past sensor 11 was operating normally under a predetermined algorithm, and the data acquired by the past sensor 11 during the reference period are extracted as the basic data group.
- step 100 The details of step 100 will be explained using FIG.
- the "predetermined period” described in the embodiment of the invention is -30 to 30 days from the 0th day
- the "reference period” is -37 to -31. An example of setting the day for one week will be described.
- Step 101 the processing unit 32 first reads out from the memory 31 determination data for 37 days immediately before the replacement of the past sensor 11, such as QC results, calibration results, and effective values of measured values (step 101). .
- this past 37 days of data for the sensor 11 will be referred to as a "data set”.
- the data of other automatic analyzers other than the automatic analyzer 1 are also collected in the server 30, the past determination data of the sensor 11 acquired by the other automatic analyzers are also read.
- the sensor 11 may be replaced multiple times. In that case, a plurality of data sets (that is, data sets for the number of past sensors 11 that have been replaced) are read out by one automatic analyzer (see FIG. 11).
- Step 102 refer to the QC measurement data for 30 days (-30th to 0th day) immediately before the exchange included in the data set, and confirm that the QC failure data is not included in those QC measurements.
- the success or failure of QC measurement is determined from data recorded in the process of collecting log files in the automatic analyzer 1 .
- a Z-score (described later) can be calculated from measurement data of QC measurement, and QC with a Z-score outside the range of ⁇ 3 can be determined as a failure.
- the processor 32 excludes data sets that have failed QC even once in the 30 days before replacement from sources for extracting basic data. As a result, the basic data group will not be extracted from the past data set of the sensor 11 that failed QC during the 30 days before replacement.
- Step 103 the calibration data for 30 days (-30th to 0th day) immediately before the replacement included in the data set is referred to confirm that data for which calibration failed is not included (step 103).
- the success or failure of calibration is also determined from data recorded in the process of collecting log files in the automatic analyzer 1 .
- the processing device 32 excludes data sets for which calibration has failed even once during the 30 days before replacement, from sources for extracting basic data. The basic data group is no longer extracted from the data set of the past sensor 11 whose calibration failed in the 30 days before replacement.
- the condition of the basic data group can be that the effective value or variation of the measured value of the dummy measurement is within a specified range.
- ⁇ Step 104 filtering is performed on the success or failure of QC and calibration for 30 days before replacement, extracting only the data set with no history of failure in QC or calibration, and extracting data from -37 to -31 days of the extracted data set
- a basic data group is extracted from (step 104).
- the data extracted as the basic data group is a set number of data (for example, 50) randomly sampled from the data in the period of -37 to -31 days.
- the measurement type of data to be sampled is at least one type selected by setting from QC measurement, calibration, patient specimen measurement, and dummy measurement. When there are multiple measurement items, data on multiple measurement items can be mixed and selected.
- the processor 32 extracts effective values of the basic data group extracted in step 100 (step 110).
- the effective value of the resistance value between the counter electrode 14 and the working electrode 15 measured at each measurement of the basic data group is extracted.
- the processor 32 uses all the data (Xall) extracted in step 110 to calculate the average value (XallAve) and standard deviation (XallSD) (step 120).
- the processor 32 computes the Z score (ZscoreSTD) for each data (Xstd) of all the data (Xall) extracted in step 110 (step 130).
- the Z score (ZscoreSTD) is calculated by subtracting the average value (XallAve) calculated in step 120 from the individual data (Xstd) and dividing by the standard deviation (XallSD), as shown in the following formula.
- step 130 the processing of the basic data group is completed.
- the procedure up to step 130 corresponds to the processing contents of the determination calculation processing P24.
- a replacement necessity determination process P25 is performed based on the data of the diagnosis target sensor, that is, the sensor 11 currently in use in the automatic analyzer 1.
- FIG. This replacement necessity determination process P25 corresponds to the third process described in the embodiment of the invention.
- the processing device 32 extracts a target data group to be compared with the basic data group for diagnosis of the diagnostic target sensor from the data of the diagnostic target sensor (step 140).
- the data extracted as the target data group is data of a set number (eg, 50 or more of the basic data group) randomly sampled from the data of the latest set period (eg, one week up to the present).
- the type of data to be sampled is the data corresponding to the basic data group, and in this embodiment, the effective value of the resistance value.
- the measurement type of data to be sampled is at least one selected from QC measurement, calibration, patient specimen measurement, and dummy measurement in accordance with the extraction conditions of the basic data group. When there are a plurality of measurement items, it is possible to mix and select data on the plurality of measurement items corresponding to the extraction of the basic data group.
- a Z score (ZscoreJDG) is calculated according to the following equation (step 150).
- the following formula calculates the Z score (ZscoreJDG) by subtracting the average value (XallAve) of the basic data group and dividing by the standard deviation (XallSD) of the basic data group for each data (XJDG) of the target data group. For example.
- ⁇ Step 160 After calculating the Z-score (ZscoreJDG) for each data (XJDG) of the target data group, processor 32 performs a first significance test (step 160).
- a one-tailed t-test is exemplified as the first significance test.
- the first significance test for example, the average value of the Z scores (ZscoreJDG) calculated in step 150 is calculated, and if the average value is smaller than the first judgment value, the hypothesis that the sensor 11 needs to be replaced is assumed. Based on this, the test is performed at a significance level of 5%. In other words, when the average value of the Z scores (ZscoreJDG) is smaller than the first judgment value and there is a significant difference, it means that the state of the sensor 11 deserves replacement.
- processor 32 After completing the first significance test, processor 32 performs a second significance test (step 170).
- a one-tailed t-test is exemplified as the second significance test.
- the second significance test for example, if the average value of Z scores (ZscoreJDG) is greater than the second judgment value, the test is performed at a significance level of 5% based on the hypothesis that the sensor 11 does not need to be replaced. In other words, when the average value of the Z scores (ZscoreJDG) is larger than the second judgment value and there is a significant difference, it means that the state of the sensor 11 deserves replacement.
- the second determination value has a smaller absolute value than the first determination value and is close to zero. Therefore, when the average value of the Z scores (ZscoreJDG) is larger than the second judgment value, it means that the difference between the data of the target data group and the data of the basic data group is small.
- the processor 32 determines whether or not the sensor 11 needs to be replaced based on the results of the first significance test and the second significance test (step 180). Specifically, when the result of the first significance test is significant and the result of the second significance test is no significant difference, the processing device 32 determines that the sensor 11 needs to be replaced. If the result of the first test of significance is no significant difference and the result of the second test of significance is that there is a significant difference, the processor 32 determines that replacement of the sensor 11 is unnecessary. If the results of both the first significant difference test and the second significant difference test show no significant difference, the processor 32 determines that some component of the automatic analyzer 1 including the sensor 11 is abnormal. If the results of both the first significance test and the second significance test show a significant difference and contradict each other, the processing device 32 does not determine whether the sensor 11 needs to be replaced.
- the processing device 32 outputs the determination result through the communication interface 33, notifies the user or the like through the UI 94 of the automatic analyzer 1, for example, and terminates the diagnosis processing in FIG. 9 (step 190).
- the notification destination of the determination result is not limited to the UI 94 of the automatic analyzer 1, but may be other UIs, such as the UI 34 of the server 30 or the UI of another service center computer.
- the UI 94 displays text to the effect that the sensor 11 needs to be replaced, and prompts the user, etc., to replace the sensor. call to action.
- FIG. 12 is a diagram showing an example of a screen for setting determination conditions for a sensor to be diagnosed.
- the setting screen shown in the figure is displayed on the UI 34 of the server 30 (FIG. 8). It is also possible to adopt a configuration in which it is displayed on a UI of a computer that can access the server 30, such as the UI 94 of the control device 20 of the automatic analyzer 1.
- FIG. The example in FIG. 12 is an example, and it is also possible to set condition items other than the items illustrated in the figure.
- the setting screen of FIG. 12 can be shared by all the automatic analyzers connected to the server 30, or can be prepared for each ID of the automatic analyzer.
- measurement items can be set in the upper area of the screen labeled "Target Measurement Items".
- the basic data group and the target data group described above are extracted from the data acquired by the measurement of the measurement items set here.
- a screen configured to select preset 1, preset 2, dummy measurement, and manual is exemplified.
- the processing unit 32 automatically performs standard prescribed measurement items, when preset 2 is selected, predetermined measurement items for which judgment is strictly performed, and when dummy measurement is selected, the measurement of a dummy sample is automatically performed. set.
- the prescribed measurement items of presets 1 and 2 can be set by the user or by using default items.
- the user or the like can arbitrarily set at least one measurement item from prepared options such as TSH and CEA.
- parameters related to extraction of basic data groups can be set in the area at the bottom of the screen labeled "Target data period”.
- Target data period In the column displayed as "QC/Calibration failure confirmation period", it is possible to specify the period of creation date and time of data for confirming the success or failure of QC and calibration by entering numerical values.
- Zscore calculation period it is possible to specify the period of creation date and time of the data used to calculate the Z score (ZscoreSTD, ZscoreJDG) by inputting numerical values.
- the numerical values of the period displayed in FIG. 12 are, for example, currently set values (or default values), and can be arbitrarily changed and saved.
- the reference period data during which normal operation is estimated is extracted as a basic data group.
- the sensor 11 currently used in the automatic analyzer 1 is diagnosed by comparison with. Comparison with data statistically presumed to be in a normal state in this manner enables highly accurate diagnosis of the diagnosis target sensor of the automatic analyzer 1 . As a result, downtime of the automatic analyzer 1 due to deterioration or failure of the sensor 11 can be preventively suppressed.
- the basic data group is sequentially accumulated and updated as the automatic analyzer 1 operates, and changes in the operation of the automatic analyzer 1 and consumable lot changes are also reflected in the basic data group. Therefore, the sensor 11 can be diagnosed flexibly according to the conditions of the automatic analyzer 1 in operation.
- the basic data group and the target data group By extracting the basic data group and the target data group from multiple measurement items, it is possible to secure the number of data even in an automatic analyzer with a small number of measurements and make an appropriate diagnosis.
- the data obtained by the sensor 11 during the measurement may vary in value depending on the measurement item, or the degree of variation may vary.
- by extracting a basic data group and a target data group from a plurality of measurement items and performing statistics it is possible to standardize the influence of variations in data due to measurement items.
- QC samples, standard samples (calibration samples), and dummy samples are all measured under the same or similar conditions as patient samples. Therefore, the state of the sensor 11 can be accurately diagnosed by diagnosing using data of at least one type of measurement among the patient sample, QC sample, standard sample, and dummy sample.
- QC samples and standard samples have the advantages of stable components, few contaminants, and excellent stationarity.
- QC samples in particular have the advantage of being able to measure frequently and secure a large amount of data. Measurement of patient specimens is also advantageous in that the number of data can be secured.
- the same kind of sample or reagent is used every time, so it is suitable for monitoring the state of the sensor 11 over time under the same conditions.
- the advantages of monitoring the state of the sensor 11 over time are common to measurements of QC samples and standard samples, which are excellent in constancy.
- the state of the sensor 11 can be properly diagnosed.
- the one-sided t-test is used as the significance test, but other tests such as the two-sided t-test and the chi-square test can also be used.
- Notifying that the normal state of the sensor 11 is estimated in addition to the case where the sensor 11 is estimated to be in an abnormal state helps the user to grasp the state of the automatic analyzer 1 and plan maintenance. In addition, by notifying that the sensor 11 cannot be determined to be abnormal or normal, it is possible to prompt the user or the like to take flexible measures.
- the measured value may exhibit special behavior regardless of the state of the sensor 11, which may affect the diagnostic accuracy of the sensor 11. Therefore, by allowing the user to arbitrarily set the measurement items on the UI, it is possible to adjust the diagnostic accuracy by considering the influence of the measurement items. In addition, by presetting predetermined measurement items, it is possible to reduce labor for setting measurement conditions.
- the data extraction period can be adjusted with reference to past diagnosis results, etc. Diagnosis accuracy can be optimized for each analyzer. By preparing default values, it is also possible to reduce labor for setting measurement conditions.
- FIG. 13 is a block diagram showing a data processing flow in a diagnostic system according to a second embodiment of the present invention.
- elements that are the same as or correspond to those of the first embodiment are denoted by the same reference numerals as in the previous drawings, and description thereof will be omitted.
- the difference of this embodiment from the first embodiment is that the automatic analyzer 1 is equipped with a diagnostic system for the sensor 11 .
- the processing displayed in the diagnostic function section F in the drawing is a series of processing related to the diagnostic function that the server 30 (FIG. 8) was in charge of in the first embodiment.
- Data and processing allocated to the memory 31 and the processing device 32 in the first embodiment regarding the diagnostic function are allocated to the memory 21 and the processing device 22 of the control device 20 in the present embodiment.
- the diagnostic algorithm for the state of the sensor 11 is the same as in the first embodiment, and the user or the like is notified of the diagnostic result through the UI 94 .
- the automatic analyzer 1 of this embodiment can also be configured to communicate with the server 30 and other automatic analyzers via the network NW.
- the past sensor data of other automatic analyzers can be added to the diagnosis of the sensor 11 as in the first embodiment.
- the automatic analyzer 1 is of a stand-alone type, the sensor 11 can be diagnosed based on its own data without considering data obtained by other automatic analyzers.
- this embodiment is the same as the first embodiment, and can obtain the same effects as the first embodiment.
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Abstract
Description
「診断対象センサ」
本願明細書では、自動分析装置で現在使用中のセンサであって異常及び正常等の状態の診断対象となるセンサを診断対象センサと記載する。診断対象センサには、自動分析装置に搭載されたセンサであって電気的な挙動を伴う全てのセンサが含まれる。つまり、測定項目(例えば対象成分の濃度)を測定するセンサの他、自動分析装置の動作制御に関わるセンサや、自動分析装置の各部の状態監視に用いられるセンサ等も診断対象センサの例である。従って、例えば流体の流路中に設置して流路状態を電圧でチェックするのに用いる圧力センサも、診断対象センサに該当する。その他、送液ポンプの送液圧を電気信号に変換してチェックするのに用いる圧力センサや、撹拌機構の撹拌強度をインピーダンスでチェックするのに用いるインピーダンスメータ等も、診断対象センサの一例である。
自動分析装置で使用された後、交換に伴って既に自動分析装置から取り外された使用済みのセンサを過去のセンサと記載する。過去のセンサには、診断対象センサが使用されている自動分析装置で過去に使用されたセンサが該当し得ることは言うまでもないが、診断対象センサが使用されている自動分析装置とは別個体の自動分析装置で過去に使用されたセンサを含めることができる。つまり、他の自動分析装置のセンサのデータも故障診断の指標の基礎となり得る。
過去のセンサが正常な状態であったことが推定される設定期間を参照期間と記載する。
標準試料(較正用試料)、QC試料(精度管理試料)、ダミー試料等、患者検体の測定に先立って自動分析装置で測定される試料を参照用試料と記載する。参照用試料と記載した場合には、標準試料、QC試料、ダミー試料の少なくとも1種(1種又は複数種)が含まれるものとする。標準試料は、較正の際に検量線を作成するために測定する調製試料である。QC試料は、QC(精度管理)の際に測定される調製試料である。ダミー試料は、患者検体の測定前の準備動作として測定される所定の試料である。標準試料、QC試料、ダミー試料等は、いずれもロット生産され、同一ロットに属する複数のものが自動分析装置の同一のセンサで順次測定され得る。
本実施形態においては、自動分析装置で過去に使用されたセンサから出力されたアナログの電気信号のデータを統計して指標とし、現在使用中のセンサを診断する。診断するセンサは、生化学分析装置、免疫分析装置、血液凝固時間測定装置、ISE測定装置等の生体試料の分析項目の測定に用いられる各種のセンサであり、例えばイオン選択性電極を備えたフローセル型センサ等である。センサが出力するアナログの電気信号には、試料の分析項目の測定値(分析対象成分の濃度)に限らず、例えばセンサの電極に制御電圧を印加した際に電極に生じる抵抗値や電流値等の応答値も含まれる。制御対象が電圧でなく電流や抵抗である場合には、電流や抵抗に応答して計測される電圧値もセンサの出力する電気信号に該当する。
・参照期間の設定について
第1プロセスにおいては、参照期間をどのように特定するかが重要である。参照期間に過去のセンサで取得されたデータを基礎として診断対象センサの診断の指標を生成するため、過去のセンサの状態が良好であった期間を参照期間に設定することが本質である。例えばサービスマンによる点検履歴のデータから実際にセンサが問題なく動作していた時期が明らかであれば、その時期を参照期間に設定することができるが、過去のセンサの交換前の所定期間を外して設定した期間を参照期間とすることが1つの好適例である。参照期間から除外する除外期間としての所定期間や参照期間の長さ(又は始期と終期)は設定による。
特定された参照期間から抽出されるデータが、診断対象センサの診断の指標の基礎データ群となる。参照期間中の全てのデータを基礎データ群とすることもできるし、ランダムに又は設定の規則に従って(例えば一定時間間隔で)抽出した設定数のデータを基礎データ群とすることもできる。過去のセンサ及び診断対象センサが複数の測定項目(TSHやCEA等)の測定に使用されるものであれば、選択された2つ以上の測定項目についてそれぞれ設定数だけ抽出したデータを基礎データ群とすることもできる。
第2プロセスでは、診断対象センサが複数の測定項目の測定に使用される場合、過去のセンサの参照期間のデータから、選択された2つ以上の測定項目についてそれぞれ設定数だけデータを抽出し、抽出したデータを全て合わせて統計することが望ましい。選択する複数の項目は、全測定項目とすることもできるし、一部の測定項目を複数選択することもできる。こうして選択された測定項目の測定で得られたデータを基に演算された統計値が、診断対象センサの診断の指標となる。ここで演算される統計値としては、平均値、移動平均値、中央値、標準偏差の少なくともいずれかの値、又はこの少なくともいずれかの値に基づく値(Zスコア等)を例示することができる。
・データの抽出及び統計について
第3プロセスでは、設定された評価期間の診断対象センサのデータ(以下、対象データと略記する)が、第2プロセスと同様に統計される。対象データの統計値は診断対象センサの現在の状態が反映された値であり、これを第2プロセスで演算した基礎データの統計値と比較することで診断対象センサの現在の状態が診断される。評価期間は、例えば直近の1週間や1月といった現在を終期とする設定期間である。
上記の通り、第2プロセスで演算される統計値は、過去のセンサについて正常が推定される参照期間のデータ(すなわち基礎データ)を統計した指標である。従って、本プロセスで演算した対象データの統計値と基礎データの統計値との差異(差分の絶対値の大きさ)が大きければ診断対象センサの異常が疑われる。コンピュータ処理を考慮すると、例えば基礎データの統計値と対象データの統計値の差異について異常判定用の第1判定値を設定する。これにより、処理装置において、基礎データの統計値と対象データの統計値との差異を演算し、その差異が第1判定値より大きい場合に診断対象センサに異常があると判定することができる。
本発明は自動分析装置に適用され得る。自動分析装置に搭載される分析ユニットとしては、例えば生化学分析装置や免疫分析装置等が挙げられる。但し、これは一例であり、本発明は以下に説明する実施例に限定されるものではなく、試薬との反応結果に基づいて試料を分析する分析ユニットを搭載した自動分析装置に広く適用され得る。例えば、臨床検査に用いる質量分析装置や血液の凝固時間を測定する凝固分析装置等を搭載した自動分析装置も適用対象に含まれ得る。また、これら各種の分析ユニットを複数種搭載した複合型の自動分析装置や、少なくとも1つの自動分析装置を含む自動分析システムにも本発明は適用可能である。以下に図面を用いて、自動分析装置の測定項目の測定用のセンサの診断に本発明を適用した具体的実施例を説明する。
図1は本発明の第1実施例に係る診断システムの適用対象となる自動分析装置の一構成例を模式的に表す平面図である。図1に示した自動分析装置1は、ラック搬送ライン2、インキュベータディスク3、第1搬送機構4、保持部材5、試料分注ノズル6、試薬ディスク7、試薬分注ノズル8、第2搬送機構9、分析ユニット10及び制御装置20を備えている。
図2は図1に示した自動分析装置で試料の測定に使用されるセンサの模式図である。自動分析装置1の分析ユニット10には、フローセル型のセンサ11が備わっている。センサ11は、フローセル12と、フローセル12の内部に備わった3つの電極(参照極13、対極14、作用極15)とを含んで構成されている。3つの電極はポテンショスタット16によって目的の電圧になるように制御される。試料と試薬との反応生成物RPが参照極13に捕集された状態で、ポテンショスタット16によって参照極13と作用極15との間に特定の電圧を印加すると反応生成物RPが発光する。反応生成物RPの発光強度を、フローセル12を挟んで参照極13と反対側(図2中では上側)に配置した光電子増倍管17で検出する。光電子増倍管17で検出された発光強度はA/D変換器18で数値化され、生データ記録処理P1を経て測定項目の測定値の生データとしてメモリ21(又はセンサ11のメモリ)に測定日時と共に記録される。その際、参照極13及び作用極15の間に電圧を印加することで対極14及び作用極15の間に生じる電流値、電圧値及び抵抗値が、ポテンショスタット16で計測される。本実施例では、光電子増倍管17の出力だけでなく、参照極13及び作用極15への印加電圧値、対極14及び作用極15の間に生じる電流値、電圧値及び抵抗値が、生データ記録処理P1において記録される。
未知の試料である患者検体に含まれる分析対象成分を高精度に定性分析及び定量分析するために、較正やQC測定が適時に行われる。例えば定量分析の場合、自動分析装置1は以下の(1)-(5)のような作業手順で日々運用される。
まず、電源を投入して自動分析装置1を立ち上げる。また、試薬容器C3を設置して試薬を初期充填したり、試薬ディスク7の内部の温度調節をしたり、電極に一定電圧をかけて内部標準液を連続測定しセンサ11の電極の電位が安定しているかを点検したり、必要に応じてメンテナンスしたりする。
測定項目(分析対象成分)の濃度が既知である高濃度の標準試料と低濃度の標準試料を測定する。これら測定により、測定項目の濃度とセンサ11(光電子増倍管17)の出力との関係式(検量線)を作成する。但し、較正の頻度は測定項目により異なり、例えば各測定項目についての較正が周期的に(例えば1月周期で)順番に行われる。
測定項目の濃度が取り得る範囲が既知で濃度レベルの異なる複数のQC試料を測定し、較正で作成した検量線を用いてQC試料中の測定項目の濃度を演算する。演算された濃度がQC試料の既知の濃度範囲内であるかをチェックすることで、検量線が適切であるかを確認する。QC測定は、患者検体の測定結果を保証するための状態確認の位置付けであることから、頻繁に実施される。例えば、複数の測定項目を並行して1-3回/日の頻度でQC測定が行われる。
測定項目の濃度が未知である患者検体を測定し、検量線を用いて測定項目の濃度を演算する。この患者検体測定の前に、いわゆるバックグラウンド測定又はダミー測定を行い、自動分析装置1の状態を確認する場合もある。
必要に応じて自動分析装置1の各部の清掃や点検等を行い、電源を落として自動分析装置1を立ち下げる。
図3は図1に示した自動分析装置における処理フローを表すブロック図である。自動分析装置1の制御装置20には、センサ11、機構部91、試料データ読取装置92、試薬データ読取装置93、UI(ユーザインターフェース)94からのデータが入力される。
測定データ変換処理P2において、処理装置22は、センサ11から入力される測定値(生データ)を有効値に変換する。センサ11から入力される測定値は、発光強度、電流値、電圧値、抵抗値の各生データである。
測定データ記録処理P3において、処理装置22は、測定毎に測定IDを割り振り、測定IDと紐づけて測定値の生データ及び有効値をメモリ21に記録する。
動作ログ記録処理P4において、処理装置22は、機構部91及びセンサ11から入力される動作ログをメモリ21に記録する。機構部91等から入力される動作ログには、例えば各モータの動作タイミングや動作量、モータの電流値、モータの動作を制御するためのセンサの信号、流体バルブの開閉タイミングや電流値等が含まれる。
試薬データ記録処理P5において、処理装置22は、試薬データ読取装置93から入力された試薬データを、予めメモリ21に記録された条件データに突き合わせ、条件データに合致する場合に使用可能な試薬としてメモリ21に記録する。試薬データを突き合わせる条件データは、試薬のIDやロット番号、有効期限、オンボード有効期限、必要な残量等であり、UI94により又は他のコンピュータで入力されて通信インターフェース23を介して制御装置20に入力され、メモリ21に記録される。また、試薬データ記録処理P5において、処理装置22は、使用された試薬の履歴を測定ID毎にメモリ21に記録する。これにより、測定値の生データ及び有効値と測定に使用された試薬のデータとが測定IDを介して紐づく。
試料データ記録処理P6において、処理装置22は、試料データ読取装置92から入力された試料データを、予めメモリ21に記録された条件データに突き合わせ、条件データに合致する場合に測定可能な試料としてメモリ21に記録し、適時に測定を実行する。試料データを突き合わせる条件データは、試料ID、測定種別(QC測定、患者検体の測定等)、測定項目等であり、UI94により又は他のコンピュータで入力されて通信インターフェース23を介して制御装置20に入力され、メモリ21に記録される。
較正データ記録処理P7において、処理装置22は、較正の実施の都度、患者試料の測定項目の濃度演算のための較正の実施履歴をメモリ21に記録する。具体的には、演算した検量線、較正の日時や測定項目、較正の成否等が、較正の実施履歴としてメモリ21に記録される。
精度管理データ記録処理P8において、処理装置22は、QC測定の実施履歴をメモリ21に記憶する。具体的には、測定項目やQCの判定結果(成否)等が、QC測定の実施履歴としてメモリ21に記録される。
メンテナンス履歴記録処理P9において、処理装置22は、メンテナンス履歴をメモリ21に記録する。具体的には、ユーザによる点検やサービスマンによるセンサ交換等の履歴が、メンテナンス履歴としてメモリ21に記録される。
ログファイル生成処理P10において、処理装置22は、メモリ21に記憶されたデータのうち、センサ11の故障診断に必要なデータを測定毎に集約してログファイルを生成する。また、処理装置22は、通信インターフェース23及びネットワークNWを介してログファイルをサーバ30に送信する。ログファイルは測定毎に作成されてサーバ30に逐次アップロードされるため、サーバ30には、自動分析装置1で現在使用されているセンサ11による測定のデータのみならず、自動分析装置1で過去に使用されていたセンサ11による測定のデータも蓄積される。
図8はサーバの処理装置によるセンサ診断処理フローを表すブロック図である。本実施例では、自動分析装置1のセンサ11の診断機能はサーバ30で実行され、サーバ30が診断システムを構成する。自動分析装置1からログファイルを受信すると、処理装置32は、ログファイル格納処理P21においてログファイルをメモリ31に記録する。続く判定データ抽出処理P22において、処理装置32は、メモリ31に格納されたログファイルからセンサ11の診断に必要なデータ、例えば測定値の有効値、QC結果、較正結果、センサ11の交換履歴等を抽出する。続く判定データ格納処理P23において、処理装置32は、抽出した診断に必要なデータをメモリ31に格納する。その後、診断実行の際に、処理装置32は、判定演算処理P24において必要なデータを再度メモリ31から読み出す。そして、判定演算処理P24の演算結果に基づき、処理装置32は、交換要否判定処理P25においてセンサ11を診断して交換要否を判定する。判定結果は、通信インターフェース33及びネットワークNWを介して自動分析装置1に送信され、UI94の表示を介してユーザ等に通知される。サーバ30のUI(ユーザインターフェース)34に診断結果を表示することも可能である。サーバ30のUI34は、自動分析装置1の制御装置20のUI94と同様のものである。
図9は図8のフロー中の判定演算処理と交換要否判定処理の詳細手順を表すフローチャート、図10は図9のフロー中の基礎データ群抽出の詳細手順を表すフローチャートである。ここでは、フローセル型のセンサ11の電極の状態を処理装置32により診断する例を説明する。判定演算処理は、図9のフロー中のステップ100-130の手順に該当し、また発明の実施の形態で説明した第1プロセス及び第2プロセスに当たる。
図9のフローを開始すると、処理装置32は、まず過去のセンサ11で得られた判定データをメモリ31から読み出し、現在自動分析装置1で使用中のセンサ11の診断の指標の基礎とする基礎データ群を判定データから抽出する(ステップ100)。基礎データ群は、既定のアルゴリズムの下、自動分析装置1で過去のセンサ11が正常に動作していたことが推定される参照期間を設定し、参照期間に過去のセンサ11で取得されたデータを基礎データ群として抽出する。
ステップ100の手順において、処理装置32はまず、過去のセンサ11が交換される直前の37日分の判定データ、例えばQC結果、較正結果、測定値の有効値をメモリ31から読み出す(ステップ101)。説明の便宜上、この過去のセンサ11についての37日分のデータを「データセット」と呼ぶ。このとき、サーバ30に自動分析装置1以外の他の自動分析装置のデータも収集されている場合、他の自動分析装置で取得された過去のセンサ11の判定データも読み出す。また、自動分析装置によっては、センサ11を複数回交換している場合もある。その場合は、1台の自動分析装置で複数のデータセット(つまり交換された過去のセンサ11の数分のデータセット)が読み出される(図11参照)。
次に、データセットに含まれる交換直前の30日間(-30~0日目)のQC測定のデータを参照し、それらQC測定の中にQCに失敗したデータが含まれていないことを確認する(ステップ102)。QC測定の成否については、自動分析装置1においてログファイルを収集する過程で記録されるデータから判定される。サーバ30においてQCの成否を判定する場合、例えばQC測定の測定データからZスコア(後述)を演算し、Zスコアが±3の範囲を外れるQCを失敗と判定することができる。ステップ102において、処理装置32は、交換前30日間にQCに1度でも失敗しているデータセットを基礎データの抽出元から除外する。これにより、交換前30日間にQCに失敗した過去のセンサ11のデータセットから基礎データ群が抽出されることがなくなる。
更に、データセットに含まれる交換直前の30日間(-30~0日目)の較正のデータを参照し、較正に失敗したデータが含まれていないことを確認する(ステップ103)。較正の成否についても、自動分析装置1においてログファイルを収集する過程で記録されるデータから判定される。サーバ30において構成の成否を判定する場合、例えばQCの成否判定と同様のアルゴリズムで行うことができる。ステップ103において、処理装置32は、交換前30日間に較正に1度でも失敗しているデータセットを基礎データの抽出元から除外する。交換前30日間に較正に失敗した過去のセンサ11のデータセットから基礎データ群が抽出されることもなくなる。
以上のように交換前30日間のQC及び較正の成否でフィルタリング処理し、QCにも較正にも失敗履歴のないデータセットのみを抽出し、抽出したデータセットの-37~-31日目のデータから基礎データ群を抽出する(ステップ104)。基礎データ群として抽出するデータは、-37~-31日目の期間のデータからランダムにサンプリングされた設定数(例えば50)のデータである。サンプリングするデータの測定種別は、QC測定、較正、患者検体測定及びダミー測定の中から設定により選択された少なくとも1種である。測定項目が複数ある場合、複数の測定項目についてのデータを混在して選択することができる。
図9に戻り、処理装置32は、ステップ100で抽出した基礎データ群の有効値を抽出する(ステップ110)。ここでは、例えば、基礎データ群の各測定時に計測された対極14及び作用極15の間の抵抗値の有効値が抽出されることとする。
次に、処理装置32は、ステップ110で抽出した全データ(Xall)を用い、平均値(XallAve)と標準偏差(XallSD)を演算する(ステップ120)。
平均値と標準偏差を演算したら、処理装置32は、ステップ110で抽出した全データ(Xall)の個々のデータ(Xstd)について、Zスコア(ZscoreSTD)をそれぞれ演算する(ステップ130)。Zスコア(ZscoreSTD)は、次式の通り、個々のデータ(Xstd)からステップ120で演算した平均値(XallAve)を引き、標準偏差(XallSD)で割ることにより演算される。
ステップ140以降は、診断対象センサ、つまり自動分析装置1で現在使用中のセンサ11のデータに基づく交換要否判定処理P25である。この交換要否判定処理P25は、発明の実施の形態で説明した第3プロセスに当たる。
処理装置32は、診断対象センサの診断のために基礎データ群と比較する対象データ群を、診断対象センサのデータから抽出する(ステップ140)。対象データ群として抽出するデータは、直近の設定期間(例えば現在に至る1週間)のデータからランダムにサンプリングされた設定数(例えば基礎データ群の50以上)のデータである。サンプリングするデータ種別は、基礎データ群に対応するデータであり、本実施例では抵抗値の有効値である。また、サンプリングするデータの測定種別は、QC測定、較正、患者検体測定及びダミー測定の中から、基礎データ群の抽出条件に対応して選択された少なくとも1種である。測定項目が複数ある場合、基礎データ群の抽出に対応して、複数の測定項目についてのデータを混在して選択することができる。
次に、抽出した対象データ群の個々のデータ(XJDG)について、それぞれ次式によりZスコア(ZscoreJDG)を演算する(ステップ150)。次式は、対象データ群の個々のデータ(XJDG)について、基礎データ群の平均値(XallAve)を引いて基礎データ群の標準偏差(XallSD)で割ることで、Zスコア(ZscoreJDG)を演算する例である。
対象データ群の個々のデータ(XJDG)についてZスコア(ZscoreJDG)を演算したら、処理装置32は、第1の有意差検定を実行する(ステップ160)。本実施例では、第1の有意差検定として片側t検定を例示する。第1の有意差検定では、例えば、ステップ150で演算したZスコア(ZscoreJDG)の平均値を演算し、その平均値が第1判定値よりも小さければセンサ11を交換する必要があるという仮説を基に、有意水準5%で検定する。つまりZスコア(ZscoreJDG)の平均値が第1判定値よりも小さく有意差ありとなる場合、センサ11の状態が要交換に値することを意味する。
第1の有意差検定を終えたら、処理装置32は、第2の有意差検定を実行する(ステップ170)。本実施例では、第2の有意差検定として片側t検定を例示する。第2の有意差検定では、例えば、Zスコア(ZscoreJDG)の平均値が第2判定値よりも大きければセンサ11は交換不要であるという仮説を基に、有意水準5%で検定する。つまりZスコア(ZscoreJDG)の平均値が第2判定値よりも大きく有意差ありとなる場合、センサ11の状態が交換不要に値することを意味する。
第2の有意差検定を終えたら、処理装置32は、第1の有意差検定及び第2の有意差検定の結果に基づき、センサ11の交換の要否を決定する(ステップ180)。具体的には、第1の有意差検定の結果が有意差あり、第2の有意差検定の結果が有意差なしの場合、処理装置32は、センサ11の交換が必要であると判定する。第1の有意差検定の結果が有意差なし、第2の有意差検定の結果が有意差ありの場合、処理装置32は、センサ11の交換は不要であると判定する。第1の有意差検定及び第2の有意差検定の双方の結果が有意差なしの場合、処理装置32は、センサ11を含めて自動分析装置1の何らかの部品に異常があると判定する。第1の有意差検定及び第2の有意差検定の双方の結果が有意差ありで矛盾する場合、処理装置32は、センサ11の交換の要否を判定しない。
最後に、処理装置32は、通信インターフェース33を介して判定結果を出力し、例えば自動分析装置1のUI94を通じてユーザ等に通知して図9の診断処理を終了する(ステップ190)。判定結果の通知先は自動分析装置1のUI94に限らず、その他のUI、例えばサーバ30のUI34やその他のサービスセンタのコンピュータのUIであっても良い。例えば、第1の有意差検定が有意差あり、第2の有意差検定が有意差なしの場合、センサ11の交換が必要である旨をUI94にテキスト表示し、ユーザ等にセンサ交換のための行動を促す。第1の有意差検定が有意差なし、第2の有意差検定が有意差ありの場合、例えばセンサ11の交換が不要な状態であることをUI94にテキスト表示してユーザ等に通知する。第1の有意差検定及び第2の有意差検定の双方が有意差なしの場合、例えばセンサ11を含む何らかの部品の異常が疑われる旨をUI94にテキスト表示し、ユーザ等の対応を促す。第1の有意差検定及び第2の有意差検定の双方が有意差ありの場合、例えば診断結果としてUI94にエラー表示する。
図12は診断対象センサの判定条件の設定画面の一例を表す図である。同図の設定画面は、サーバ30のUI34(図8)に表示される。自動分析装置1の制御装置20のUI94等、サーバ30にアクセス可能なコンピュータのUIに表示させる構成とすることも可能である。図12の例は一例であり、同図に例示された項目以外の条件項目を設定できるようにすることもできる。また、図12の設定画面は、サーバ30に接続された全ての自動分析装置で共用することもできるし、自動分析装置のID毎に用意することもできる。
(1)上記の通り、本実施例では、自動分析装置1で過去に使用されたセンサ11の使用期間のうち正常動作が推定される参照期間のデータを基礎データ群として抽出し、基礎データ群との比較により自動分析装置1で現在使用されているセンサ11を診断する。このように統計的に正常な状態が推定されるデータとの比較により、自動分析装置1の診断対象センサを高精度に診断することができる。これにより、センサ11の劣化や故障等に伴う自動分析装置1のダウンタイムを予防的に抑制することができる。
図13は本発明の第2実施例に係る診断システムにおけるデータ処理フローを表すブロック図である。同図において第1実施形態と同一の又は対応する要素には既出図面と同符号を付して説明を省略する。
Claims (18)
- 自動分析装置に備えられアナログの電気信号を出力するセンサを診断する診断システムであって、
前記センサが出力する電気信号のデータ及び前記センサの交換履歴を記憶するメモリと、
前記メモリに記録されたデータを処理する処理装置とを備え、
前記処理装置は、
前記自動分析装置で使用された過去のセンサが出力した電気信号のデータのうち設定された参照期間のデータを前記メモリから読み出し、
前記参照期間のデータの統計値を演算し、
前記自動分析装置で使用中の診断対象センサが出力した電気信号のデータのうち、設定された評価期間に記録されたデータを前記メモリから読み出し、
前記評価期間のデータの統計値を演算し、
前記参照期間の統計値と前記評価期間の統計値とから求めた差異に基づき、前記診断対象センサの異常を判定する
診断システム。 - 請求項1の診断システムにおいて、
前記参照期間は、前記過去のセンサの交換前の所定期間を外して設定した期間である診断システム。 - 請求項1の診断システムにおいて、
前記処理装置は、前記過去のセンサの交換履歴を基に、前記過去のセンサの交換時から所定期間だけ遡った時点を演算し、その時点を終期とする設定期間を前記参照期間として特定する診断システム。 - 請求項2の診断システムにおいて、
前記処理装置は、
患者検体の測定に先立って同じロットで複数回にわたって測定可能な参照用試料について、前記過去のセンサを用いて測定する度に、前記参照用試料の測定の成否のデータを前記メモリに記録し、
前記所定期間に実行された前記参照用試料の測定の履歴を前記メモリから読み出し、
前記所定期間に実行された前記参照用試料の測定が全て成功である場合に前記過去のセンサについて前記参照期間を設定する
診断システム。 - 請求項4の診断システムにおいて、
前記参照用試料は、精度管理試料、キャリブレーションに用いる標準試料、ダミー試料の少なくとも1種である診断システム。 - 請求項1の診断システムにおいて、
前記自動分析装置のセンサは、複数の測定項目の測定に使用され、
前記処理装置は、前記参照期間のデータの統計値及び前記評価期間のデータの統計値を、選択された2つ以上の測定項目についてそれぞれ設定数だけ抽出したデータを全て合わせて統計して演算する
診断システム。 - 請求項1の診断システムにおいて、
前記参照期間のデータの統計値及び前記評価期間のデータの統計値は、平均値、移動平均値、中央値、標準偏差の少なくともいずれか、又は前記少なくともいずれかの値に基づく値である診断システム。 - 請求項1の診断システムにおいて、
前記処理装置は、前記参照期間のデータに対する前記評価期間のデータの差異が第1判定値より大きい場合に、前記診断対象センサに異常があると判定する診断システム。 - 請求項8の診断システムにおいて、
前記処理装置は、前記参照期間のデータに対する前記評価期間のデータの差異が前記第1判定値より大きいかを、前記参照期間の統計値と前記評価期間の統計値とに基づく有意差検定により判定する診断システム。 - 請求項9の診断システムにおいて、
前記処理装置は、
前記参照期間のデータの統計値として、前記参照期間のデータの平均値と標準偏差を演算し、
前記評価期間の各データについて、前記平均値との偏差を前記標準偏差で割ったZスコアを演算し、演算したZスコアの平均値と前記第1判定値との比較に基づき前記診断対象センサに異常があるかを判定する
診断システム。 - 請求項8の診断システムにおいて、
前記処理装置は、前記参照期間の統計値と前記評価期間の統計値とを基に、前記参照期間のデータに対する前記評価期間のデータの差異が前記第1判定値よりも小さく設定した第2判定値より小さい場合に前記診断対象センサは正常であると判定する
診断システム。 - 請求項8の診断システムにおいて、
前記処理装置は、前記参照期間の統計値と前記評価期間の統計値とを基に、前記参照期間のデータに対する前記評価期間のデータの差異が前記第1判定値以下で、かつ前記第1判定値よりも小さく設定した第2判定値以上である場合に、前記診断対象センサを除く構成部品の要因を加味し、前記診断対象センサを判定する
診断システム。 - 請求項1の診断システムにおいて、
前記診断対象センサの判定条件を設定可能に構成されたユーザインターフェースを備える診断システム。 - 請求項6の診断システムにおいて、
前記診断対象センサの判定条件を設定可能に構成されたユーザインターフェースを備え、
前記ユーザインターフェースは、前記参照期間及び前記測定項目の少なくとも1つを設定可能に構成されている
診断システム。 - 請求項1の診断システムにおいて、
前記センサがフローセル型である診断システム。 - 請求項1の診断システムにおいて、
前記電気信号が、電流、電圧及び抵抗の少なくともいずれかである診断システム。 - アナログの電気信号を出力するセンサと、前記センサが出力する電気信号のデータ及び前記センサの交換履歴を記憶するメモリと、前記メモリに記録されたデータを処理する処理装置とを備え、前記センサを診断する自動分析装置であって、
前記処理装置は、
過去に使用されたセンサが出力した電気信号のデータのうち、設定された参照期間のデータを前記メモリから読み出し、
前記参照期間のデータの統計値を演算し、
使用中の診断対象センサが出力した電気信号のデータのうち、設定された評価期間に記録されたデータを前記メモリから読み出し、
前記評価期間のデータの統計値を演算し、
前記参照期間の統計値と前記評価期間の統計値とから求めた差異に基づき、前記診断対象センサの異常を判定する
自動分析装置。 - 自動分析装置に備えられたセンサを診断する診断方法であって、
前記自動分析装置で使用された過去のセンサが出力した電気信号のデータのうち、設定された参照期間のデータの統計値を演算し、
前記自動分析装置で使用中の診断対象センサが出力した電気信号のデータのうち、設定された評価期間に記録されたデータの統計値を演算し、
前記参照期間の統計値と前記評価期間の統計値とから求めた差異に基づき、前記診断対象センサの異常を判定する
診断方法。
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JP2008052577A (ja) * | 2006-08-25 | 2008-03-06 | Hitachi High-Technologies Corp | 稼働状況を提示することができる装置 |
JP2008089615A (ja) * | 2007-12-28 | 2008-04-17 | Sysmex Corp | 臨床検体処理装置および臨床検体処理システム |
JP2012519280A (ja) * | 2009-02-27 | 2012-08-23 | オーソ−クリニカル・ダイアグノスティックス・インコーポレイテッド | ネットワーク化された診断臨床分析装置の差し迫った分析故障を検出するための方法 |
JP2019536049A (ja) | 2016-12-02 | 2019-12-12 | エフ.ホフマン−ラ ロシュ アーゲーF. Hoffmann−La Roche Aktiengesellschaft | 生物学的試料を分析する自動分析装置のための故障状態予測 |
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EP4361640A1 (en) | 2024-05-01 |
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