JP2006023214A - Abnormality existence determining method of measurement reaction process, automatic analyzer executing this method, and storage medium stored with program of this method - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To prevent an oversight of an item for indicating abnormal reaction, even when several ten-thousand tests from several thousands are measured in a day, by providing a means for determining whether or not an inspection is properly performed by using a measured value in a reaction process. <P>SOLUTION: This automatic analyzer verifies an analytical measurement result from reaction process data by measuring a value of a photometer in the reaction process; and has a database 33 on normal reaction process data; and has a reaction process evaluating part 35 for successively determining whether or not to be normal data with every predetermined division or every division coincident with a progress process of the analyzer by collating measured time series reaction process data 34 with preset database inside information; and has data measured by the automatic analyzer (an inspection item) for constructing the database on the reaction process and an arithmetic operation part for acquiring a (coincident) measuring result in an allowable range different in an analytical condition and being constant in a result. <P>COPYRIGHT: (C)2006,JPO&NCIPI

Description

  The present invention relates to an automatic analyzer that performs qualitative and quantitative analysis of biological samples such as blood and urine, and more particularly to an automatic analyzer that has a function of measuring a change in physical properties of a sample in a time series. The present invention relates to an automatic analyzer provided.

  For example, in a biochemical analyzer, an automatic analyzer for a clinical test uses a reagent that changes color by reacting with a specific component in a sample, and quantitatively measures the change in color based on a change in absorbance, thereby qualitatively determining the specific component.・ Those that perform quantitative analysis are common. Some of such automatic analyzers have a function of storing a change in absorbance during a reaction process or plotting it on a screen. When an abnormality is found in the measurement result, the operator of the instrument can check whether the sample is really abnormal, or whether the abnormal result is caused by the abnormality of the instrument by examining the plot of absorbance change. there is a possibility. In the process of clinical autoanalysis, abnormalities in the nozzle due to prozone check abnormalities, sampling abnormalities, reagent dispensing abnormalities, stirring mechanism abnormalities, dripping or scattering of high-viscosity reagents, and reagent combinations Analytical inspection may not be performed normally due to contamination or crystal precipitation, but these abnormalities may be detected by analysis of reaction process data.

  However, since changes in reaction process data (not limited to absorbance change data) vary depending on analysis items and sample identification, it is difficult to realize a function that automatically determines whether or not there was an abnormality in the reaction process. There was nothing that realized it. For this reason, in order to investigate reaction process abnormalities from reaction process data, it is necessary to store a large amount of data, and it is necessary to manually examine individual reaction process data one by one, which takes time and cost. It was.

Japanese Patent No. 328087 Journal of Quality Engineering Vol.3 No.1: "Comprehensive evaluation and SN ratio by multidimensional information" Technology development in MT system: Japanese Standards Association, Quality Engineering Application Course

  Since the measurement results in the clinical automatic analysis test only evaluate the final result, if the measurement value is within the set range, the analyzer will not issue an alarm and detection will not occur even if there is an abnormal reaction. It was possible. Even if the measured value is abnormal, there is no means for quickly determining whether the sample is really abnormal.

  The reason why current automatic analyzers cannot quickly determine the presence or absence of abnormalities in the reaction process is largely due to the variety of abnormalities in the reaction process, as described above.

  On the other hand, in recent years, as a statistical method for multivariate data analysis, a method (MT method; Mahalanobis-Taguchi method) that comprehensively determines whether certain data that forms a predetermined space at Mahalanobis distance is abnormal is used. Has been. For example, in Non-Patent Document 1, if a Mahalanobis space (reference space) serving as a reference is created based on healthy person data and the Mahalanobis distance calculated for a subject whose health is unknown is smaller than a certain threshold value (for example, 4). This is a method of belonging to a group of healthy people and discriminating as “healthy”, otherwise judging as “unhealthy” or “abnormal”. Such a method has a wide range of applications and has been published in various fields. For example, there are various examples in Patent Document 1 and Non-Patent Document 2.

  If this method can be applied to the determination of the presence / absence of an abnormality in the reaction process, it is considered possible to reduce the possibility that data that is not abnormal is determined to be abnormal or that an abnormal sample is determined to be normal. However, time-series data such as reaction process data is a data group in which the correlation between each data is strong (constant value within the measurement error and the same slope), so the data at each time point is used as a parameter. The inverse matrix of the correlation matrix cannot be calculated even when creating a space. For this reason, further data processing such as deleting one of the strongly correlated data (or between measurement items) is required, and it takes time to reconstruct the Mahalanobis space, which is the above-mentioned standard, and the processing efficiency decreases. Such problems are latent.

  The present invention makes it possible to apply the MT method to data analysis by preliminarily processing reaction process data, thereby enabling determination of the presence or absence of abnormalities in the reaction process and improving the reliability of measurement data. An object of the present invention is to provide an automatic analyzer.

  In order to achieve the above object, the present invention measures the value of the photometer in the reaction process and verifies the analysis measurement result from the reaction process data in order to realize the verification of the analysis test result.

The aforementioned reaction process is a process corresponding to the process sequence of the apparatus up to the final analysis result of the analyzer. The data group is continuous time-series data. Here, if the number of data points from the start of measurement to the end of measurement is k, the number of processes (device operation etc.) performed in the process is ni, and the number of data is xi,
k = x1n1 + x2n2 + ... + xini
And the number of sections is n. This n corresponds to the reaction time and processing time of the process. The data structure is shown below.

  (1) A standard Mahalanobis space is created from k pieces of data that have been measured successfully.

(2) The k data of item (1) is divided into predetermined time intervals or every ni, and new Mahalanobis spaces are created in conjunction with the process progress.

  (3) The Mahalanobis distance is calculated at the end of measurement in each process. For example, if n1 ends (t1), the data is addressed to the k3 reference spaces when the data collection is completed, and the Mahalanobis distance MD1 is calculated.

  (4) By repeating the item (3) until the end of the measurement, the temporal change in the Mahalanobis distance for each category can be seen. That is, as shown in the table below, Mahalanobis distances MD1, MD2, MD3, and MD4 (for example, in the case of four divisions) are calculated, and the temporal change is known.

  The Mahalanobis distance that ended in the normal process is usually 0-4 (when the threshold is set to 4), so if any Mahalanobis distance in each process exceeds 4, some abnormality has occurred in the process. I understand.

Further, each reference space in each process has a Mahalanobis distance in the range of 0 to 4 and n processes as shown in the above table. For this reason, it is possible to make a more comprehensive judgment by storing the Mahalanobis distance calculated in each process until the completion of measurement and using each value as one comprehensive judgment data. (The reference space at this time is an aggregate of Mahalanobis distances taking into account the thresholds at the time of creating each n-process reference space. In the above table, 4 normal MD2 and 0-4 Mahalanobis distances. is there.)
In addition, a calculation unit that obtains measurement results that are different from the data measured by the automatic analyzer (inspection item) and have analysis conditions within a certain tolerance (matched) to build a database on reaction processes Is provided.

  In such a calculation unit, a basic data group for calculating the Mahalanobis distance is stored. The data group is an eigenvector (k × k) matrix calculated from an average value (k), a standard deviation (k), and a correlation coefficient matrix in each measurement time or a specific section and its maximum required number of axes. is there.

  As described above, such data has a strong correlation between the data. For this reason, the inverse matrix of the correlation coefficient matrix used in the process of calculating the Mahalanobis distance cannot be calculated. For this reason, in this method, an eigenvalue and an eigenvector of the correlation coefficient matrix are obtained, and the number of axes (mode) is determined based on the degree of contribution, and arithmetic processing for calculating the Mahalanobis distance is performed. In such calculation processing, it is not necessary to obtain an inverse determinant from the correlation coefficient matrix at all, and the Mahalanobis distance can be calculated even if the correlation coefficient is “1.0”.

  Furthermore, an arithmetic processing unit for optimizing the determination logic is provided using the measurement results obtained by different results in the measurement under different conditions.

  The present invention has the following effects.

  Inspection reliability is improved. In particular, even if an abnormality occurs in the reaction process, it is possible to detect an abnormal measurement result in which the measurement data falls within the normal value range. Even if the inspection result is out of the normal value range, if the reaction process data is not abnormal, it is measured correctly, so that unnecessary re-inspection is unnecessary.

  Since the reliability related to the measurement abnormality with respect to the test item for the specimen is improved, useless retests can be reduced, and the running cost (that is, consumables such as test reagents and cleaning liquids) for the test of the automatic analyzer can be reduced. In addition, the inspection time can be shortened.

  In addition, since the reaction process data can be stored only for the measurement abnormality data, the cost for data storage can be suppressed.

  The present invention provides a means for determining whether or not a test has been properly performed using measured values in a reaction process, and shows an abnormal reaction even when thousands to tens of thousands of tests are measured per day. The purpose is to prevent items from being overlooked.

  As such a method, a Mahalanobis space serving as a reference is formed from time-series measurement data as in the above-described conventional example, the data is addressed to the space, and a determination is made based on the distance. However, as described above, in such an analyzer, due to the relationship between the prozone check error, sampling error, reagent dispensing error, stirring mechanism error, high viscosity reagent dropout and scattering, and reagent combination caused by measurement data Nozzle contamination and crystal precipitation are composed of time series data. For this reason, an abnormality detection method that can cope with the time-series operation of each device is important. In the conventional method, the space used as the reference is generally configured using time-series data from the start of measurement to the completion of measurement for comprehensive judgment, so it is a comprehensive judgment at the time of completion of measurement. . Therefore, it is difficult to determine which part of the analysis process was abnormal. Furthermore, since this time series data is a data group in which the correlation between each data is strong (a constant value and the same slope within the measurement error), it is necessary to devise the Mahalanobis distance calculation.

  Hereinafter, how the MT method was applied to the analysis of the reaction process will be described using examples. FIG. 1 shows a configuration example of the automatic analyzer. The main mechanism of the automatic analyzer is composed of a specimen disk 2, a reaction disk 1, and a reagent disk 3.

  Before starting the analysis process, several samples are installed in advance on the sample disk. When analysis is started, a predetermined amount of specimen is aspirated by the sample dispensing mechanism (specimen dispensing mechanism) 4 and discharged to a predetermined position of the reaction disk. For example, the specimen on the reaction disk is analyzed by a predetermined analysis sequence incorporated in the apparatus in advance as shown in FIG.

  FIG. 1 shows the operating position of each mechanism section with the reaction disk 1 as the center. A light source lamp 20 (see FIG. 2) for measuring the absorbance of the specimen is provided on the inner periphery of the reaction disk, and a photometer unit 7 is installed on the outer periphery thereof. The absorbance is measured each time the reaction vessel 12 on the reaction disk passes between the light source and the photometer. The absorbance is measured after the reaction disk starts rotating and is accelerated to a constant speed. The reaction disk rotates and stops at a constant angle every cycle, and is measured many times during a predetermined reaction time.

  The control of these mechanical systems is mainly executed by a computer unit called a control unit 11, and an operation computer 15 for receiving sample information, reagent management information, examination request reception, and the like is connected. And is working.

  The structure of the photometer unit 7 used in this embodiment is shown in FIG. The photometer unit used in this embodiment is called a post-spectral multiwavelength photometer. That is, the light emitted from the light source lamp 20 passes through the reaction vessel 12 containing the specimen and then enters the concave diffraction grating as a linear light beam through the entrance slit 21. Here, the light intensity of the light that has been split into multiple wavelengths and transmitted through the specimen is measured by a 12 wavelength photometer.

1.2. Analysis Sequence FIG. 3 shows a flow chart of sample analysis performed in this example. Blood (white blood cells, etc.), cerebrospinal fluid, urine, or the like is used as the specimen, and is previously installed in one specimen container 13 (FIG. 1) on the specimen disc 2. This sample is dispensed into the reaction vessel 12 on the reaction disk 1 for analysis. As preparation before dispensing the specimen, the reaction container on the reaction disk is washed (A01), and a water blank is measured (A02). The water blank is to measure the absorbance of water in order to adjust the specimen absorbance to zero. That is, the absorbance value of the sample dispensed in the reaction container is obtained by the difference from the absorbance value of the water blank. When the measurement of the water blank is completed, the water in the reaction vessel is sucked and discarded (A03). A predetermined specimen is dispensed (sampled) into the reaction container (A04). Thereafter, R1 reagent (A05), R2 (A07) reagent, R3 reagent (A09), and R4 reagent (A11) are added to the reaction vessel in a predetermined amount at a predetermined time, and stirring (A06, A08, A10) is performed. , A12). Here, depending on the analysis item, there is an inspection item in which R4, R3, or R2 is not dispensed. There are three-minute reaction, four-minute reaction, five-minute reaction, and ten-minute reaction in the reaction process, and the absorbance at the time when the reaction disk is rotated by the number of times corresponding to the reaction time is taken as the measured value. Usually, the reaction is often carried out for 10 minutes. When a predetermined reaction time has elapsed and all photometry is completed (A13), the reaction vessel is washed for the next analysis (A01).

1.3. Description of Concentration Calculated Absorbance FIG. 4 shows a typical method for analyzing the absorbance thus obtained. For the concentration calculation from the absorbance, a 1-point analysis method, a 2-point rate analysis method, a 2-point analysis method, a 3-point 2-item analysis method, or the like is used. In the one-point analysis method, the concentration of the component to be examined is calculated from the absorbance after a certain time has elapsed since the addition of the reagent. In the two-point rate analysis method, the difference in absorbance at two times t1 and t2 (t2> t1) determined from reagent addition is expressed as (t2−
The concentration is calculated from the time ratio of absorbance change divided by t1). In the two-point analysis method, the absorbance at two times t1 and t2 (t2> t1) determined from the reagent addition is measured, and the absorbance at t1 is subtracted from the absorbance at t2 multiplied by a constant factor. The concentration is calculated.

  In any case, the absorbance is measured every time the specimen on the reaction disk crosses the photometer, and the concentration of the test target component is determined by arithmetic processing using a part of the measured value. That is, most (or part) of the absorbance measured in the reaction process has been discarded in the conventional analysis method.

1.4. Reaction Process Data Abnormality Detection Algorithm FIGS. 5 and 6 are diagrams showing the entire processing flow of reaction process abnormality detection according to the present invention and the details thereof.

  First, in order to detect an abnormality in each process, data collection is performed in accordance with a predetermined inspection / measurement sequence for each process. For example, in the reaction process, in order to detect an abnormality in the process, a set of reaction process data (reaction process data group) for the test to be determined first is taken into the computer (S1). The timing of capturing the reaction process group data matches the predetermined measurement sequence for each process. Alternatively, absorbance data that is sequentially measured in the reaction process may be taken in and a series of time-series data groups may be obtained later.

  Next, the Mahalanobis distance is calculated | required about the reaction process data obtained from the above (S2). Here, the Mahalanobis distance is a method of multivariate analysis, and it is a scale for measuring whether a certain test object belongs to a reference group (hereinafter referred to as a reference space). In the present invention, a reference space is constructed from the reaction process data group when the analysis is normally performed, and the information is stored in the database DB in advance (FIG. 6). How to obtain the Mahalanobis distance and the reference space will be described later.

  Next, it is determined from the calculated Mahalanobis distance whether the collected data group (subject sample) is abnormal or normal. The Mahalanobis distance when dispensing is normally performed shows a value in the vicinity of 1, whereas if abnormal, the distance takes a value much larger than 1. Using this, the normality / abnormality of dispensing is determined by threshold determination (S3).

  Regarding the reaction process data in this embodiment, the data structure and the reference space will be described with reference to FIGS.

  FIG. 7 shows a reaction process data acquisition method for one wavelength. Each time the specimen sample passes through the photometric point, the absorbance of the specimen is measured and sequentially accumulated in the computer (see FIG. 6), and finally k pieces (time series) determined in advance as shown in FIG. ) Is taken in. Among the k data, in the present example, the first four points are the absorbance of the cell blank value, that is, the zero point of the absorbance, and the fifth and later are the time series of the absorbance after the introduction of the reagent from the suction of water. This is a data group (reaction process) in which changes are traced.

  The number of measurement points for the cell blank value is determined by the mechanism system and the control method, and in the actual analysis, for example, the absorbance for multiple wavelengths such as 12 wavelengths is measured. The absorbance of the second wavelength is changed from (k + 1) th to 2kth, and similarly, the absorbance of the twelfth wavelength is changed from (11k + 1) th to 12kth, and 12k absorbance data are acquired. By using the absorbance for all or a part of the wavelengths, it is possible to detect the reaction process abnormality with higher accuracy. However, in this embodiment, only one wavelength is described for simplicity. The photometric points for each wavelength are measured at regular intervals, but the absorbance data may be increased by installing multiple photometers, and the absorbance at many photometric points where reaction process abnormalities are likely to occur. On the other hand, the absorbance data may be thinned and fetched at a location where there is almost no abnormality, and it is not always necessary that the data is equally spaced.

  In the present embodiment, as described above, the blank process and the reaction process are roughly divided into two, and the process in the middle is divided into two and finally divided into four sections. May be divided in more detail in accordance with

  The reaction process data acquired as described above (in this embodiment, four processes are included) are summarized as shown in FIG. Here, the absorbance at each photometric point is used as an item for obtaining the Mahalanobis distance.

  If the above operation is performed on a reaction process data group for which the measurement result is determined to have been completed normally, a reference reaction process data group can be obtained. FIG. 8 summarizes each reaction process data group obtained when normal dispensing is performed n times, and this is a reference space of n event k items.

  The n normal reaction process data groups referred to here are reaction processes when a reproducible result is obtained for a sample whose measurement result is within a range in which accuracy is guaranteed in the automatic analyzer. is there. For example, when an abnormality such as a prozone phenomenon or sample / reagent dispensing occurs, these are due to an accidental phenomenon and are not reproducible. That is, even if reproducible measurement is performed by changing the device, time, reagent and sample dispensing amount, etc., the same result cannot be obtained, so that it is not a normal reaction process.

  In addition, since n data groups are used and a reference space is created from these data groups, it is not just the collection of statistical data, but the collection of statistical data when normal as described above. There must be no abnormal data. However, if there are variations within the range in which the instrument guarantees measurement accuracy, such as variations in specimen characteristics (measurement values) and photometers, data can be actively distributed to obtain data. It is desirable to improve the accuracy of abnormality detection.

  As for the number n of events in the reference space, a larger number is preferable because the accuracy of abnormality detection is improved. However, if the number is larger than necessary, the economic cost is higher than the obtained information. Therefore, it is preferable to make a decision in consideration of detection accuracy and economic cost. However, when the number of events n is smaller than the number of items k, a correlation matrix to be described later cannot be obtained, so that n must be larger than k.

  The reaction process data group and the reference space obtained as described above are limited to the analysis items, and a reaction process data group and a reference space must be prepared for each analysis item. This is because the analysis time and the type and amount of the reagent used vary depending on the reaction item, and the time change pattern of absorbance varies greatly.

Details of the method of calculating the Mahalanobis space (reference space) for the reaction process data p ₁ , p ₂ ,..., P _k as shown in FIG.

  Further, FIG. 9 illustrates a processing flow at the time of data collection input of the detected object.

および標準偏差σ₁，σ₂，…，σ_k を求め、式（１）の演算を行い正規化する。 The average value for each item (photometry point) for the reaction process data constituting the reference space of n events k items (photometry points) as shown in FIG.

Then, standard deviations σ ₁ , σ ₂ ,..., Σ _k are obtained, and normalized by performing the calculation of equation (1).

On the other hand, when the reference space is a matrix of n rows and k columns and a correlation matrix of this matrix is obtained, a k × k matrix A is obtained. If the inverse matrix of this matrix A is A ⁻¹ , the Mahalanobis distance D ² can be expressed as in equation (2).

  Of these calculations, the average and standard deviation for each item (photometric point) in the reference space for each inspection item and the inverse matrix of the correlation matrix in the reference space are calculated in advance, and the results are used as parameters. (See FIG. 7). When the Mahalanobis distance is repeatedly calculated, it is often advantageous in calculation processing because it saves the trouble of performing these calculations many times.

Here, if the reaction process data p ₁ , p ₂ ,..., P _k are normal, the Mahalanobis distance D ² is a value near 1.0, and if it is abnormal, it is generally compared with 1.0. Take a large value. In order to discriminate between normal and abnormal based on the Mahalanobis distance D ² , a certain threshold value x is set in advance,
Normal if D ² <x,
If D ² ≧ x, it is determined as abnormal.

  In the time series data group shown in FIG. 7 described above, there are many cases where the correlation between the data is strong due to the nature thereof, and there is a case where the inverse matrix cannot be obtained in the process of calculating the above equation (2). To do.

  As a countermeasure in such a case, in general, any one of items with strong correlation may be deleted sequentially. However, in this method and its processing method, further data processing and its processing task must be additionally installed, so that its processing capability is reduced and its reliability is also reduced due to a reduction in the number of data points. For this reason, in the present invention, a processing task is devised and applied without any further processing of the collected data group even when the correlation between the data is strong. Hereinafter, the contents will be described in detail.

  As described above, as a method of calculating the Mahalanobis distance, the method according to the above equation (2), the Schmitt orthogonal expansion method (document: technical development in MT system), and the principal component analysis are used to orthogonalize the data group and to create a correlation matrix. Various calculation methods that apply a cofactor matrix that does not obtain an inverse matrix in the first place and a processing method that singularly decomposes s. Each method has its characteristics and is still developing, and it takes time to apply it to a system that emphasizes real-time performance as in the present invention. For this reason, the present inventor found a new Mahalanobis distance processing method based on the principal component analysis type from the results of various experiments and verifications. Hereafter, the process of the processing content is shown.

(1) The eigenvalue and eigenvector are obtained from the correlation matrix (k × k).
(2) Calculate the contribution rate of eigenvalues and the required number (p). (Maximum k)
(3) From the result of (2), if P <K, only p and P + 1 are applied to calculate Mahalanobis distance calculation from eigenvectors.
If P = K, only k and p are applied to calculate Mahalanobis distance calculation from eigenvectors.

  In such a process, there is no additional processing of the collected data group. Even if there is a strong correlation between items, the eigenvalues of the correlation matrix are calculated first, so k eigenvalues (orthogonal axes) and their contributions are calculated. Since it is possible to determine which number of axes should be applied based on the degree of contribution, unnecessary axes can be easily deleted, and errors in calculating the Mahalanobis distance can be minimized. Furthermore, even if there is a strong correlation with the correlation coefficient of “1.0” (exactly the same data) between items, it is confirmed that Mahalanobis distance can be calculated without any data processing to delete one item. did.

  FIG. 9 shows the calculation process of this process, and FIG. 10 summarizes the correlation between the result calculated by the above equation (2), the orthogonal calculation result, and the calculation result of this method. . FIG. 11- (a) is a data group of Mahalanobis distance at the time of creation of the reference space, and FIG. 11- (b) is a calculated Mahalanobis distance when target data is addressed to the reference space. As is clear from FIGS. 10 and 11, it is clear that there is no great difference between the processing methods, and the validity and reliability of this processing were confirmed.

FIG. 12 is a functional block diagram incorporating the determination logic of the reaction process of FIG. The analysis control unit 31 is a function implemented on the control unit (control computer unit 11), and other functions and data are implemented on the operation computer 15.

  The analysis request receiving unit 30 is for setting what kind of analysis test the operator performs on the sample, and is performed using a screen such as a CRT and an input device such as a keyboard and a mouse. A control command is sent to the analysis control unit 31 from the input information. The analysis control unit 31 executes the analysis by controlling the mechanism shown in FIG. 1, and dispenses the sample on the sample disk 2 onto the reaction disk 1 to perform the reaction. When the analysis is completed for one sample, the reaction process data 34 and the analysis result data 32 at that time are stored in the database. Whether or not the reaction process data 34 is abnormal is determined by the reaction process evaluation unit 35. At this time, evaluation is performed with reference to the reference space database 33. This reference space is prepared for each analysis item, and the reaction process evaluation unit refers only to the reference space corresponding to the analysis item.

  When the reaction process evaluation unit 35 determines that the reaction process is abnormal, the analysis control unit 31 is instructed to reexamine the same test item for the same sample. Information indicating that the reaction process is abnormal is added to the stored analysis result data 32.

  In the apparatus maintenance data storage unit 36, information for each process of the apparatus (process) when the reaction process is determined to be normal or abnormal by the reaction process evaluation unit 35 is stored as data in time series. With such information, for example, the Mahalanobis distance calculated from data collected in a certain process and the elapsed time of movement of the device are monitored, and the temporal transition and deterioration status due to internal factors such as component deterioration inside the device, Alternatively, it can be monitored whether it is caused by a sudden external factor. These trend data are useful as maintenance information for the device itself.

FIG. 13 shows an example of a data group (110 samples, 28 data points) of reaction processes that have been completed normally.

  FIG. 14 shows an example of a data group (number of samples: 31 samples) collected from the detection target separately from the normal reaction process data group.

  FIG. 15 shows an example of typical results (No. 1, 7, 13, when a reference space is created from the data group of FIG. 13 and each data of FIG. 14 is addressed to the reference space. 16, 24). In this example, as described above, the section is set to four sections, and the threshold value in each section is set to 4.5 in each section. No. 16 and 24 of the data of the detected object are determined to be “normal end” because the Mahalanobis distance is within the threshold regardless of the passage of time in any section. On the other hand, Nos. 1, 7, and 13 exceed the threshold at the stage of the initial division and tend to decrease with the passage of time, but the distance increases in the course of the progress of the reagent reaction. This is because some kind of abnormality has occurred in the process (process) of section 1 and has a tendency to return once, but the initial influence continues to affect the reaction process, and it can be determined that the abnormal end has occurred. Alternatively, an “abnormal” alarm is generated at the end of the measurement in the section 1, and a procedure for not measuring or preparing a new sample separately, or a sequence change can be automatically performed.

  As described above, abnormalities such as sampling, reagent dispensing, and stirring may occur as the cause of the abnormality in the reaction process. For example, the reagent discharged during the addition of the R2 reagent may adhere to the side wall of the reaction cell and mix with the sample in the middle of the reaction process, so the result may be higher than the true value. In addition, when a reagent having a higher viscosity is used, the discharged reagent remains as a water droplet at the tip of the dispensing nozzle due to surface tension, the nozzle is contaminated by a combination of reagents, and crystal precipitation occurs.

  Usually, the measurement result (concentration value) in one analytical test is only one real number, and when the measured value in a certain range is obtained, the analyzer is equipped with functions such as automatically performing a retest. The reproducibility is confirmed. If there is an abnormality in the measured value, it may be possible to determine whether it is abnormal or not by examining the absorbance in the reaction process. However, in the system configuration with 50 photometry points, all the reaction process data of 12 wavelengths are stored. It was necessary to store 600 pieces of data in one measurement. For this reason, even if there is an abnormality in the reaction process, if the measured value falls within the normal range, the abnormality may be overlooked.

  The reaction process in which an abnormality is detected from the reaction process data often includes information for determining what is the cause of the abnormality by analyzing the reaction process data. Therefore, if the reaction process data is saved only when an abnormality is detected in the reaction process data, the memory capacity for storage such as a hard disk can be reduced, and once an abnormality is detected, the reaction process data can be used. The cause can be investigated. Since the cause of the abnormality in the reaction process data may be an abnormality in the analyzer itself, the abnormal phenomenon is quickly analyzed by collecting control system information.

Schematic mechanism diagram of an automatic analyzer. Post-spectral multiwavelength photometer. Analysis flow. Example of reaction time course. Overall processing flow. Details of the processing flow. Reaction process data. Detailed reaction process data structure. Reaction process data processing flow (calculation flow) of this processing method. Comparison between this method and each treatment method. Data group examples used for comparison between this method and each treatment method. Data flow diagram. Example of reaction process data (at normal end). Example of reaction process data (data to be measured). An example of an abnormal reaction appearing in the measured data.

Explanation of symbols

DESCRIPTION OF SYMBOLS 1 ... Reaction disk, 2 ... Specimen disk, 3 ... Reagent disk, 4 ... Sample dispensing mechanism, 5 ... Reagent dispensing mechanism, 6a ... Specimen dispensing nozzle washing part, 6b ... Reagent dispensing nozzle washing part, 7 ... Luminous intensity Total unit, 11 ... control unit (control computer unit), 12 ... reaction container, 13 ... sample container, 14 ... reagent container, 15 ... operation computer, 20 ... light source lamp, 21 ... slit, 22 ... concave diffraction grating, DESCRIPTION OF SYMBOLS 23 ... Multi-wavelength photometer, 30 ... Analysis request reception part, 31 ... Analysis control part, 32 ... Analysis result data, 33 ... Standard space database, 34 ... Reaction process data, 35 ... Reaction process evaluation part, 36 ... Apparatus maintenance data .

Claims (8)

Creating a reference Mahalanobis space from time series data of the reaction process in which the measurement has been completed successfully;
Dividing the reaction process into a plurality of predetermined processes, and creating a new Mahalanobis space by dividing each of the plurality of processes;
Calculating a Mahalanobis distance in the Mahalanobis space at the end of the predetermined plurality of processes;
A method for determining the presence or absence of abnormality in a measurement reaction process, comprising: In the method for determining the presence or absence of abnormality in the measurement reaction process according to claim 1,
A method for determining the presence or absence of an abnormality in a measurement reaction process, comprising the step of determining the presence or absence of an abnormality based on the Mahalanobis distance for each of the plurality of predetermined processes. In the method for determining the presence or absence of abnormality in the measurement reaction process according to claim 2,
A method for determining the presence / absence of an abnormality in a measurement reaction process, comprising the step of sequentially determining the presence / absence of an abnormality at each time point when the plurality of predetermined processes are completed. In the method for determining the presence or absence of abnormality in the measurement reaction process according to claim 1,
A method for determining the presence / absence of abnormality in a measurement reaction process, wherein a reference space is created using only the data group of the reaction process determined to have a normal measurement result as a normal data group. In the method for determining the presence or absence of abnormality in the measurement reaction process according to claim 4,
In the measurement, when a measurement result of a reference sample having a known concentration matches each concentration, the reaction process data at that time is created as a normal data group, and a method for determining whether there is an abnormality in the measurement reaction process. In the method for determining the presence or absence of abnormality in the measurement reaction process according to any one of claims 1 to 5,
A method for determining the presence or absence of an abnormality in a measurement reaction process, wherein identification information is added to measurement data in which an abnormality is detected when an abnormality in the reaction process is detected.   An automatic analyzer comprising storage means for storing a program for executing the method for determining the presence or absence of abnormality in a measurement reaction process according to any one of claims 1 to 6. A storage medium storing a program for executing the abnormality determination method for a measurement reaction process according to any one of claims 1 to 6.

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