Automated analysis device, automated analysis system, and data analysis method

Abstract

Provided is an automated analysis device making it possible to eliminate the concern that a user will overlook an item that may potentially be experiencing fluctuations in a reaction process due to a specific abnormal cause. An automated analysis device (100) is provided with: a generation unit for generation reaction process data about a sample; an input unit (16) for receiving input of a first item and a second item related to the first item, of predetermined items related to the sample; and an output unit (15) that indicates the effect if an analysis value of the first item falls under an abnormal range region (34) and an analysis value of the second item falls under a first region (43), on the basis of information received from the input unit (16) and also information from a storage unit (14) storing a first discrimination line for discerning whether analysis values of the predetermined items are in a normal range region (33) in the reaction process data and a second discrimination line for discerning the first region (43) and a second region (44) that are within the normal range region (33) and are closer to the first discrimination line and far away from the first discrimination line, respectively.

Description

Automatic analysis device, automatic analysis system, and data analysis method

[0001] The present invention relates to an automatic analyzer, an automatic analysis system, and a data analysis method.

[0002] For example, automated analyzers capable of performing biochemical analysis and immunological analysis for clinical testing process data obtained by measuring biological samples (also called specimens) such as blood and urine. For example, the automated analyzer measures the change in absorbance over time for a reaction solution in which a certain amount of sample and reagent are mixed and stirred to cause a reaction. Based on this, the automated analyzer calculates the concentration, activity value, and other measured values ​​of target substances in the sample.

[0003] An example of prior art related to the above-described automatic analyzer is Patent Document 1. Patent Document 1 discloses that in an automatic analyzer, a reaction rate constant is calculated using an approximation formula based on absorbance changes stored in chronological order, and the presence or absence of a reaction abnormality is determined based on the value of the reaction rate constant.

[0004] JP 2009-204448 A

[0005] However, in the device described in Patent Document 1, even if the cause of an abnormality estimated for a certain item of a specific sample is a specific cause, and the cause of the abnormality may be affecting the reaction process of other items of the sample, if the other items of the sample are determined to be normal, the possibility of this is not notified. As a result, the user may focus only on the item determined to be abnormal and may miss fluctuations in the reaction process of other items of the sample that have been affected.

[0006] The present invention has been made in view of the above-mentioned circumstances, and an object of the present invention is to provide an automatic analyzer, an automatic analysis system, and a data analysis method that can eliminate the risk that a user will overlook an item in which a fluctuation in the reaction process may be caused by a specific abnormality.

[0007] The automated analyzer according to the present invention, which has solved the above-mentioned problems, comprises an analysis unit that analyzes predetermined items related to a sample based on a reaction between the sample and a reagent; a generation unit that generates reaction process data for the sample; an input unit that receives input of a first item among the predetermined items related to the sample and a second item among the predetermined items related to the first item; and an output unit that, based on information received from the input unit and information stored in a memory unit that stores a first discrimination line that determines whether the analysis value of the predetermined item in the reaction process data is within a normal range region or an abnormal range region, and a second discrimination line that determines whether a first region within the normal range region is closer to the first discrimination line and a second region further from the first discrimination line, determines whether the analysis value of the first item falls within the abnormal range region and the analysis value of the second item falls within the first region.

[0008] According to the present invention, an automatic analyzer, an automatic analysis system, and a data analysis method can be provided that can eliminate the risk that a user will overlook an item in which a fluctuation in the reaction process may be occurring due to a specific abnormality.

[0009] 1 is a schematic diagram illustrating an example of the configuration of an automatic analysis system including an automatic analyzer according to embodiment 1. FIG. 2 is a graph illustrating an example of reaction process data. FIG. 3 is a distribution diagram illustrating the concepts of a first discrimination line, a normal range region, and an abnormal range region. FIG. 4 is a distribution diagram illustrating the concepts of a second discrimination line, a first region, and a second region. FIG. 5 is an example of a screen displaying information output from an automatic analyzer. FIG. 6 is an example of a screen displaying a second item related to a first item. FIG. 7 is a flowchart illustrating the flow of a data analysis method according to embodiment 1. FIG. 8 is a flowchart illustrating the flow of a preferred aspect of the data analysis method. FIG. 9 is a distribution diagram illustrating the concepts of a third discrimination line, a third region, and a fourth region according to embodiment 2. FIG. 10 is a flowchart illustrating the flow of a data analysis method according to embodiment 2.

[0010] An embodiment of the present invention will be described in detail with reference to the drawings. In the following description of the embodiment, it goes without saying that the components (including element steps) are not necessarily essential unless otherwise specified or considered to be obviously essential in principle.

[0011] First Embodiment [Automatic Analysis Apparatus and Automatic Analysis System in First Embodiment] FIG. 1 is a schematic diagram illustrating an example of the configuration of an automatic analysis system 1000 including an automatic analysis apparatus 100 for analyzing samples in a first embodiment.

[0012] 1, the automated analyzer 100 includes an analysis unit 1, a memory unit 14, an output unit 15, an input unit 16, and a control unit 17. The analysis unit 1 includes a transport line 4, a sample probe 5, a reagent probe 6, a reagent disk 8, a reaction disk 10, a stirring unit 11, a photometer 12, and a washing unit 13, and analyzes predetermined items related to the sample based on the reaction between the sample and the reagent.

[0013] The transport line 4 transports a sample rack 3, which is loaded with a plurality of sample containers 2 containing samples such as blood or urine, to a position accessible by a sample probe 5. The sample rack 3 and transport line 4 may be replaced with a sample disk that rotates in one direction to move the sample containers 2 loaded in a ring shape.

[0014] The reagent disk 8 stores a plurality of reagent containers 7 containing reagents to be reacted with the specimen, and rotates in one direction to move the reagent containers 7 to positions accessible by the reagent probes 6. The reaction disk 10 holds a plurality of reaction containers 9, into which specimens and reagents are dispensed, in an annular shape, and rotates in one direction to move the reaction containers 9 to predetermined positions.

[0015] The sample probe 5 dispenses a sample from a sample container 2 loaded on a sample rack 3 transported by a transport line 4 into a reaction container 9. The reagent probe 6 dispenses a reagent from a reagent container 7 into the reaction container 9 into which the sample has been dispensed. Note that the number of reagents dispensed into the reaction container 9 is not limited to one, and multiple reagents may be dispensed. The stirring unit 11 is arranged around the reaction disk 10 and stirs the sample and the contents of the reaction container 9 into which the reagent has been dispensed.

[0016] The photometers 12 are arranged around the reaction disk 10 and measure the absorbance of the solution in the reaction vessel 9 stirred by the stirring unit 11 each time the reaction vessel 9 passes in front of it. Since the reaction disk 10 rotates intermittently at a regular timing, the absorbance of the solution in the reaction vessel 9 is measured at regular time intervals. The measured absorbance is converted into the concentration of a specific component contained in the sample using a pre-prepared calibration curve. The calibration curve is prepared by performing a calibration that includes measuring the absorbance of a reaction solution obtained by reacting a standard solution with a known concentration of the specific component with a reagent. The cleaning unit 13 is arranged around the reaction disk 10 and cleans the reaction vessel 9 after analysis has been completed.

[0017] The standard solution used for calibration is an aqueous solution containing a specific component, and has at least a concentration near the upper limit and a concentration near the lower limit of the measurement range of the automated analyzer 100. In other words, at least two standard solutions are used. The standard solution in which the concentration of the specific component is nearly zero and does not react with the reagent is called the first standard solution, and is often saline or purified water.

[0018] The storage unit 14 is, for example, a hard disk drive (HHD) or a solid state drive (SSD), and stores the absorbance measured by the photometer 12, the concentration converted from the absorbance, and the like. The storage unit 14 also stores the first discrimination line and the second discrimination line (described later), and the like. The storage unit 14 may use information stored outside the automatic analyzer 100, such as a cloud server.

[0019] The output unit 15 and input unit 16 constitute an operation unit 19 that allows a user to operate the automated analyzer 100. The output unit 15 is, for example, a liquid crystal display or a touch panel, and displays information such as absorbance and concentration. The input unit 16 is, for example, a keyboard or a mouse, and is used to input conditions and parameters required for analysis. If the output unit 15 is a touch panel, a GUI (Graphical User Interface) displayed on the touch panel functions as the input unit 16. In this example, the input unit 16 receives input of a first item among the predetermined items related to the sample, and a second item related to the first item among the predetermined items related to the sample. The first and second items will be described later.

[0020] The control unit 17 is a computing unit such as a CPU (Central Processing Unit) that controls each component and performs various calculations. The control unit 17 may also determine whether or not an abnormality exists in the measurement results. For example, the control unit 17 generates reaction process data (described below) from the absorbance, measurement time, and other data stored in the memory unit 14. That is, the control unit 17 functions as a generator (not shown) that generates the reaction process data. For example, the control unit 17 compares the measured value with a condition related to abnormality determination. Alternatively, the control unit 17 compares the value of a predetermined variable (analysis value) representing the characteristics of the reaction in the reaction process data with a condition related to abnormality determination. The condition may be, for example, a threshold value constituting a normal range region. The control unit 17 determines, for example, whether the value of the variable is within the normal range region or the abnormal range region. In this case, the data of the measurement and analysis results obtained by the automated analyzer 100 includes determination result information, such as whether or not an abnormality exists.

[0021] In this example, the remote terminal 18 is connected to the automatic analyzer 100. The remote terminal 18 may include a memory unit, a control unit, an input unit, and an output unit similar to those of the automatic analyzer 100. The automatic analyzer 100 may send data obtained by analysis to the remote terminal 18, causing the remote terminal 18 to perform calculations, input, output, and the like. The remote terminal 18 may be located near the automatic analyzer 100 as shown in the figure, or may be located remotely from the automatic analyzer 100 via a LAN or the like. The remote terminal 18 may also be connected to multiple automatic analyzers 100. The remote terminal 18 may also be referred to as a computer.

[0022] The above is the configuration of the automatic analyzer 100. The automatic analyzer 100 and the remote terminal 18 (computer) are combined to configure an automatic analysis system 1000.

[0023] [Measured Values] Measured values ​​are output from the automated analyzer 100 for clinical testing, which has functions such as biochemical analysis. These "measured values" are values ​​having predetermined units, such as concentration or activity values ​​calculated based on absorbance data, and can be one of the factors described below.

[0024] [Reaction Process Data] The following describes the reaction process data output from the automated analyzer 100. The reaction process data is data that indicates changes over time in absorbance and the like from the start to the completion of sample measurement. The predetermined variables are variables that represent the characteristics of the reaction process and can be calculated based on the reaction process data.

[0025] FIG. 2 is a graph showing an example of reaction process data. The reaction process data 21 shown in FIG. 2 is data showing the change in the absorbance (represented by the symbol A, a dimensionless quantity) of a sample over time. In the graph of FIG. 2, the horizontal axis represents time (measurement time, represented by the symbol T, in minutes), and the vertical axis represents absorbance A. The automated analyzer 100 measures the absorbance A of the solution in the reaction vessel 9 and calculates the concentration of the component substances in the solution as measured values ​​from the absorbance A. In the graph, plots indicated by black dots represent the values ​​of absorbance A measured at each time point. For example, at time T1, from 0 to 5 minutes, the absorbance A is approximately constant at a1. At time T2, from 5 to 10 minutes, the absorbance A increases from a value a2 and finally reaches a value a3.

[0026] Curve 22 is an approximation curve for the distribution of absorbance A, created based on the plot of absorbance A at time T2. The characteristic quantity of the reaction process obtained from this approximation curve is a value that may vary depending on the solution in reaction vessel 9, the analysis item, the reaction during measurement, etc., and represents the characteristics of the reaction. Difference 23 indicated by the arrow is the difference between absorbance values ​​a2 and a3 (referred to as final reaction absorbance change amount A1 (FIG. 3)), and is one of the characteristic quantities of the reaction process obtained from curve 22.

[0027] [Factors] Next, factors will be described. Factors are composed of measured values ​​or variables in the reaction process data 21 as described above.

[0028] Fig. 3 is a distribution diagram illustrating the concepts of the first discrimination line, normal range region 33, and abnormal range region 34. Fig. 3 shows a distribution formed by combining the above-mentioned measured values ​​and two factors (F1, F2) that represent the characteristics of the reaction in the reaction process data 21 in Fig. 2. In the example of the first embodiment, factor F1 is the measured value, and factor F2 is the final reaction absorbance change amount A1.

[0029] In the distribution diagram of Figure 3, dotted lines 31 and 32 distinguish between a normal range region 33 and two abnormal range regions 34 (#1 and #2). In this example, dotted lines 31 and 32 are collectively referred to as the first discrimination line. In this example, the first discrimination line is applied to the first item. The first discrimination line is a boundary line (a line consisting of successive thresholds) that determines whether the analysis value of a predetermined item in the reaction process data is within the normal range region 33 or the abnormal range region 34.

[0030] In this distribution diagram, the plots indicated by black dots are pairs of plots of values ​​of factor F1 and value of factor F2 based on the measurement values ​​and reaction process data output from the automated analyzer 100, in other words, combinations of factor values. In other words, the plots indicated by black dots are variable values ​​and correspond to analysis values. In FIG. 3 , for example, a distribution of plot group 35 is present in the lower part near the bottom left of the normal range region 33. Plot group 35 corresponds to a case determined to be normal. In this example, there is also one plot 36 that is outside of the plot group 35. Plot 36 is within the abnormal range region 34#1. Plot 36 corresponds to a case determined to be abnormal.

[0031] 3, if the abnormality range area 34#1 or 34#2 is detected, the system displays possible causes associated with the corresponding abnormality range area 34 in table 531 (FIG. 5), which will be described later. The automatic analyzer 100 has pre-set abnormality causes associated with each abnormality range area 34. This reduces the time it takes for the user to search for the cause of the abnormality. Furthermore, if the cause of the abnormality is a specific cause, such as an issue with the device or reagent, the analysis of the sample from which the abnormality occurred can be stopped and the cause of the abnormality addressed before resuming the analysis. This allows for obtaining appropriate analysis results and reducing the waste of samples and reagents.

[0032] The normal range region of factor F1 and the normal range region of factor F2 have a predetermined relationship, and the normal range region 33 is formed by a combination of the factor values. The normal range region 33 is defined by a predetermined formula as a region in which the value of factor F1 and the value of factor F2 have a predetermined relationship, such as a linear relationship. The normal range region 33 can thus take any shape depending on the prerequisites, etc. Therefore, the first discrimination line is not limited to a straight line as shown in this example, but can also take any shape, such as a curve, in order to distinguish between the normal range region 33 and the abnormal range region 34.

[0033] Fig. 4 is a distribution diagram illustrating the concepts of the second discrimination line, the first region 43, and the second region 44. Fig. 4 shows a distribution configured by combining two factors (F1, F2), similar to Fig. 3. In the example of the first embodiment, factor F1 is the measured value, and factor F2 is the final reaction absorbance change amount A1.

[0034] In the distribution diagram of Figure 4, lines 41 and 42 divide the normal range region 33 into a first region 43 and a second region 44. In this example, lines 41 and 42 are collectively referred to as the second discrimination line. In this example, the second discrimination line is applied to the second item. The second discrimination line is a boundary line that distinguishes between a first region 43 that is close to the first discrimination line and a second region 44 that is farther from the first discrimination line within the normal range region 33.

[0035] In this distribution diagram, the plots indicated by black dots have the same meaning as in Figure 3. For example, within the normal range region 33 of the second item, a plot in the second region 44 corresponds to a case where the analytical value of the second item is determined to be normal. Furthermore, one plot 45 in the first region 43 indicates that although the analytical value of the second item is within the normal range region 33, there is a possibility that fluctuations have occurred due to the influence of the abnormal cause that caused the first item to be determined to be abnormal.

[0036] In this example, the second discrimination lines (lines 41 and 42) are located inside the first discrimination lines (dotted lines 31 and 32), i.e., in the normal range region 33, and are parallel to and maintain a certain distance from the first discrimination lines. The distance between the second discrimination lines and the first discrimination lines may be set in advance by the manufacturer of the automated analyzer 100 or may be set arbitrarily by the user. However, the second discrimination lines do not overlap the first discrimination lines. Furthermore, the second discrimination lines do not exist outside the first discrimination lines, i.e., in the abnormal range region 34 ( FIG. 3 ).

[0037] [Output] Figure 5 is an example of a screen displaying information output from the automated analyzer 100. As shown in Figure 5, table 51 in this example shows the name of the device that performed the analysis for each sample, the sample identification number, sample type, and analysis date and time. The information to be displayed is not limited to the information shown in table 51, and may be any information related to the sample, such as user comments, the source (department) requesting the test, and the number of measurement items.

[0038] Table 52 in this example displays information for each analysis item of the sample selected in Table 51. Examples of such information include the item name, measurement values, alarms, the module and system used for the analysis, and the reaction process. The displayed information is not limited to that shown in Table 52, and may include any information related to each analysis item, such as the time at which each analysis item was dispensed into the reaction vessel 9, the number of the reaction vessel 9 used in the analysis, the unit of measurement value, and whether or not dilution was performed. Furthermore, if the first item in Table 52 falls within the abnormal range in FIG. 3 , this may be indicated by coloring the item name 520, alarm 521, or reaction process 522 column. In addition to Table 52, if there is any content that needs to be emphasized to the user in this example, this may be indicated by coloring the content in a similar manner.

[0039] Table 53 in this example displays detailed information about the analysis item selected in Table 52. Examples of detailed information include the suspected abnormality causes in Table 531, an enlarged view of the reaction process displayed in Table 52, information about the second item described below, information about the reagents used in the analysis, and information about the feature quantities of the reaction process data. The information displayed in Table 53 is not limited to that shown in Table 53, and may be any information related to the selected sample or selected analysis item, such as the sample identification number, serum information values, or user comments.

[0040] 3, when an abnormality that corresponds to the abnormality range area 34#1 is detected, the table 531 displays the possible causes associated with the abnormality range area 34#1. If the cause does not correspond to any of the displayed possible causes, the user can input the cause himself.

[0041] 6 shows an example screen 600 for setting (inputting) a second item related to a first item. In this example, a table 61 is a list of first items to be set. That is, the second item related to a first item selected in the table 61 is set on the right side of the table 61. In this example, A is selected as the first item.

[0042] Button 62 is a button that allows the user to select whether or not to automatically determine the second item related to the first item selected in table 61. In this example, when button 62 is selected, a check mark appears on the left side of the button. When button 62 is selected, the item to be registered as the second item is automatically determined based on the cause of the abnormality. As described above, the cause of the abnormality is determined by the corresponding abnormality range area 34 in FIG. 3. For example, if the cause of the abnormality is due to the sample or the device, all items other than the first item registered for that sample are registered as the second item, and if the cause of the abnormality is due to the reagent, nothing is registered as the second item. Alternatively, the user may register a second item corresponding to the cause of the abnormality in advance.

[0043] Button 63 is a button that allows the user to select whether or not to arbitrarily determine the second item related to the first item selected in table 61. In this example, by selecting button 63, a check mark appears on the left side of the button.

[0044] Table 631, table 632, button 633, and button 634 can only be selected when button 63 is selected. Table 631 is a list of items that can be registered as second items. Table 632 is a list of items registered as second items. Button 633 is a button for registering (setting) the selected item. Button 634 is a button for deregistering (cancelling) the selected item.

[0045] If you want to register an item in table 631 as a second item, select the item you want to register in table 631 and press button 633. By doing this, the item selected in table 631 is moved to table 632 and registered as the second item. On the other hand, if you want to remove the item from registration as a second item, select the item you want to remove from registration in table 632 and press button 634, which moves the selected item to table 631 and removes it from registration as a second item.

[0046] After setting the second item on the screen example 600, pressing button 636 completes the setting (completes registration) and saves it in the storage unit 14. On the other hand, pressing button 635 cancels the setting and does not save it in the storage unit 14.

[0047] The first items are all analysis items that can be analyzed by the automated analyzer 100. The second items are a group of items related to the first items. The second items can be selected from all analysis items that can be analyzed by the automated analyzer 100. In addition, the second items can be set for each of the first items.

[0048] For example, as shown in Figure 6, if the first item is A, it is assumed that the second item will be registered as item B, which uses the same analysis method, or items C and D, which use the same wavelength of light when measuring absorbance. Also, for example, if the first item is B, it is assumed that item A, which uses the same analysis method, will be registered as the second item. This example is not limiting, and the user can select any combination of the first and second items based on their own ideas.

[0049] As described above, the automated analyzer 100 and the automated analysis system 1000 include an analyzer 1, a generator, an input unit 16, and an output unit 15. Based on information from the memory unit 14 storing the first and second discrimination lines and information received from the input unit 16, the output unit 15 displays a message indicating that the analytical value of the first item falls within the abnormal range 34, which is outside the normal range 33 from the first discrimination line, and that the analytical value of the second item falls within the first range 43. The process flow for displaying the message indicating that the above conditions are met will be described in the data analysis method described below. In this example, the above-described configuration eliminates the risk of the user overlooking an item (the second item) whose reaction process may be fluctuating due to a specific abnormal cause.

[0050] [Data Analysis Method in Embodiment 1] Next, the data analysis method will be described. Fig. 7 is a flowchart showing the flow of the data analysis method in embodiment 1. Fig. 7 explains the main processing flow by the control unit 17 of the automatic analyzer 100. The flow in Fig. 7 has steps S1 to S3 and S5 to S8.

[0051] In step S1, a screen such as that shown in Figure 6 is operated to input and set a first item among predetermined items related to the sample and a second item related to the first item (input step). The automated analyzer 100 stores the set contents in the memory unit 14. This causes the control unit 17 to start analysis.

[0052] In step S2, the automated analyzer 100 calculates factor values ​​for a plurality of predetermined variables based on the sample reaction process data (see FIG. 2) included in the measurement data (calculation step). These factor values ​​correspond to the analysis values ​​for the first and second items. In this example, values ​​for two factors (F1 and F2) are calculated.

[0053] In step S3, the automated analyzer 100 performs an anomaly determination for each factor value (the analysis values ​​of the first and second items) using each factor value calculated in step S2 and the first discrimination line in Figure 3 (anomaly determination step). As described above, the first discrimination line is applied to the first item. As described above, the first discrimination line is a boundary line that determines whether the analysis value of a predetermined item in the reaction process data is within the normal range region 33 or the abnormal range region 34. If the analysis value of the first item is determined to be normal in step S3, the process proceeds to step S8. If the analysis value is determined to be abnormal in step S3, the process proceeds to step S5.

[0054] In step S5, the automatic analyzer 100 reads the contents saved in step S1 from the memory unit 14 and checks the second item registered for the first item for which the automatic analyzer 100 proceeded to step S5. In other words, the automatic analyzer 100 checks the analysis value of the second item related to the first item (first checking step).

[0055] In step S6, the automated analyzer 100 checks the abnormality determination result in step S3 for the item registered as the second item checked in step S5 (second check step). That is, the automated analyzer 100 checks whether the analysis value of the second item checked in the first check step was determined to be normal or abnormal in the abnormality determination step. If the analysis value of the second item was determined to be normal in the abnormality determination step, the process proceeds to step S7. If the analysis value of the second item was determined to be abnormal in the abnormality determination step, the process proceeds to step S8.

[0056] In step S7, the automated analyzer 100 discriminates the second item using the factor values ​​calculated in step S3 and the second discrimination line in Figure 4. That is, if the analysis value of the second item is determined to be normal in the second confirmation step, the automated analyzer 100 uses the second discrimination line to discriminate whether the analysis value of the second item falls within the first region 43 or the second region 44 (discrimination step). The second discrimination line is a boundary line set within the normal range region 33 to distinguish between the first region 43, which is closer to the first discrimination line, and the second region 44, which is farther from the first discrimination line. In both cases where the analysis value of the second item falls within the first region 43 and the second region 44, the process proceeds to step S8.

[0057] In step S8, the automated analyzer 100 outputs (displays) the results of the analyses performed in steps S2, S3, and S5 to S7 together with the analysis information on the output unit 15, such as a screen (output step). Specifically, the following displays are made as shown in (a) to (c):

[0058] (a) When the automated analyzer 100 determines that the analysis value of the first item is normal in step S3 (the abnormality determination step) (i.e., when the analysis value of the first item is determined to be within the normal range region 33), the automated analyzer 100 displays on the output unit 15 that the analysis value of the first item is normal. For example, if the analysis value of the first item is determined to be normal in step S8, it is displayed on a screen such as that shown in FIG. 5, distinguishing it from values ​​determined to be abnormal. In other words, the automated analyzer 100 displays on the output unit 15 that each factor value (analysis value) of the first item is normal.

[0059] (b) If the automatic analyzer 100 determines that an abnormality has occurred in the above-mentioned step S6 (second confirmation step) (i.e., if the analysis value of the second item has been determined to be within the abnormal range area 34 in the abnormality determination step), the automatic analyzer 100 displays on the output unit 15 that the analysis value of the second item is abnormal.

[0060] (c) If the automated analyzer 100 determines in step S7 (discrimination step) that the analysis value of the second item falls within the first region 43, it displays on the output unit 15 a message that the analysis value of the first item falls within the abnormal range region 34 and that the analysis value of the second item falls within the first region 43. Furthermore, if the automated analyzer 100 determines in step S7 (discrimination step) that the analysis value of the second item falls within the second region 44, it displays on the output unit 15 a message that the analysis value of the second item falls within the second region 44. For example, in the distribution diagram of FIG. 4 , if the analysis value (factor value) of the second item falls within the first region 43, it determines that although the analysis value of the second item is within the normal range region 33, there is a possibility that a fluctuation has occurred due to the influence of the abnormal cause that caused the first item to be determined to be abnormal. Then, in step S8, a message to that effect is displayed on the output unit 15 in the example screen shown in Figure 5, distinguishing it from cases where the second item has been determined to be normal (i.e., cases where the analysis value of the second item has been determined to fall into the first region 43) and cases where the first item has been determined to be abnormal. Furthermore, if the analysis value (factor value) of the second item falls into the second region 44, the second item is also determined to be normal. Then, in step S8, a message to that effect is displayed in the example screen shown in Figure 5, distinguishing it from cases where the first item has been determined to be abnormal.

[0061] The above-mentioned screen is, for example, data to be displayed on the display screen of the output unit 15 in FIG. 1, and may be in the form of a web page, for example. The screen is displayed, for example, as shown in FIG. 5 above. This screen includes information reporting an abnormality related to the measurement of the target sample, information on possible causes, etc. After step S8, the flow ends. The above-mentioned process is repeated in the same way for all first and second items of all target samples.

[0062] As described above, the data analysis method includes the above-described steps S1 to S3 and S5 to S8, and if the determination step of step S7 determines that the analytical value of the second item falls within the first region 43, then in step S8, a message is displayed on the output unit 15 indicating that the analytical value of the first item falls within the abnormal range region 34 and that the analytical value of the second item falls within the first region 43. This eliminates the risk that the user will overlook an item (second item) whose reaction process may be fluctuating due to a specific abnormal cause.

[0063] [More Preferred Aspect of Data Analysis Method] Next, a more preferred aspect of the data analysis method will be described with reference to Fig. 8. Fig. 8 is a flowchart showing the flow of a preferred aspect of the data analysis method. Like Fig. 7, Fig. 8 explains the main processing flow by the control unit 17 of the automated analyzer 100. The flow in Fig. 8 has steps S1 to S8. Of these, steps S1 to S3 and S5 to S8 are the same as those in the first embodiment described above, and the inclusion of step S4 is what differs from the first embodiment.

[0064] As shown in FIG. 8, this data analysis method includes an abnormality cause confirmation step (step S4) between an abnormality determination step (step S3) and a first confirmation step (step S5).

[0065] In step S4, the automated analyzer 100 checks which abnormal range region 34 in the distribution diagram shown in FIG. 3 corresponds to the analysis value (factor value) of the first item determined to be abnormal in step S3. In step S4, whether to proceed to step S5 or step S8 is determined based on the cause of the abnormality associated with the corresponding abnormal range region 34. That is, the automated analyzer 100 checks whether the analysis value (each factor value) of the first item determined to be abnormal in the abnormality determination step is the cause of the abnormality that will cause the analysis value of the second item to be further examined. If the cause of the abnormality is the cause of the abnormality that will cause the analysis value of the second item to be further examined (YES in step S4), the automated analyzer 100 proceeds to the first confirmation step (step S5). On the other hand, if the cause of the abnormality is not the cause of the abnormality that will cause the analysis value of the second item to be further examined (NO in step S4), the automated analyzer 100 proceeds to the output step (step S8), where the output unit 15 displays a message indicating that the cause of the abnormality is not the cause of the abnormality that will cause the analysis value of the second item to be further examined (a message indicating that the cause of the abnormality is not the cause of the abnormality that will cause the analysis value of the second item to be further examined).

[0066] For example, if the analysis value (factor value) of the first item determined to be abnormal in step S3 falls within the abnormal range region 34, which indicates that the cause of the abnormality is due to the device or the sample, the process proceeds to step S5. Furthermore, if the analysis value (factor value) of the first item determined to be abnormal in step S3 falls within the abnormal range region 34, which indicates that the cause of the abnormality is due to the reagent, the process proceeds to step S8. In this case, in step S8, an abnormality is displayed on the screen example shown in FIG. 5, and table 531 indicates that the cause of the abnormality is due to the reagent. The abnormality cause for proceeding to step S5 may be preset by the manufacturer of the automated analyzer 100 or may be set by the user. By including the abnormality cause confirmation step, the user can quickly determine when there is no need to proceed to detailed examination of the analysis value of the second item.

[0067] <Embodiment 2> Fig. 9 is a distribution diagram illustrating the concepts of the third discrimination line, third region 93, and fourth region 94 in embodiment 2. Fig. 9 shows a distribution configured by combining two factors (F1, F2), similar to Figs. 3 and 4. In the example of embodiment 2, factor F1 is the measured value, and factor F2 is the final reaction absorbance change amount A1.

[0068] In the distribution diagram of Figure 9, lines 91 and 92 divide the abnormal range region 34 into a third region 93 and a fourth region 94. In this example, lines 91 and 92 are collectively referred to as the third discrimination line. In this example, the third discrimination line is applied to the first item. The third discrimination line is a boundary line that distinguishes between the third region 93, which is closer to the first discrimination line, and the fourth region 94, which is farther from the first discrimination line.

[0069] The third region 93 and the fourth region 94 are located within the abnormal range region 34 and are used to determine the degree of abnormality. For example, if plots exist in both the third region 93 and the fourth region 94, the plot located on the side of the fourth region 94, which is farther away from the normal range region 33, is determined to be more abnormal.

[0070] In this distribution diagram, the plots indicated by black dots have the same meaning as in Figures 3 and 4. In this example, there is one plot 95 that exists in the abnormal range region 34. The plot 95 is located within the fourth region 94. Because the plot 95 exists in the abnormal range region 34, it is determined to be abnormal, and because it exists in the fourth region 94, it is determined that the degree of abnormality is high. In other words, the user can grasp the degree of abnormality of the first item. It can also predict that abnormalities may have occurred in other items.

[0071] In this example, the third discrimination lines (lines 91 and 92) are located outside the first discrimination lines (dotted lines 31 and 32), i.e., in the abnormal range region 34, and are parallel to and maintain a certain distance from the first discrimination lines. The distance between the third discrimination line and the first discrimination line may be set in advance by the manufacturer of the automatic analyzer 100 or may be set arbitrarily by the user. However, the third discrimination line does not necessarily overlap with the first discrimination line. Furthermore, the third discrimination line is not located inside the first discrimination line, i.e., in the normal range region 33.

[0072] [Data Analysis Method in Embodiment 2] Fig. 10 is a flowchart showing the flow of a data analysis method in embodiment 2. Fig. 10 explains the main processing flow by the control unit 17 of the automated analyzer 100 in embodiment 2. The flow in Fig. 10 has steps S1 to S3, step S104, and steps S5 to S8. Of these, steps S1 to S3 and S5 to S8 are the same as those in embodiment 1 described above, and differ from embodiment 1 in that step S104 is included.

[0073] As shown in FIG. 10, this data analysis method includes an abnormality degree determination step (step S104) between the abnormality determination step (step S3) and the first confirmation step (step S5).

[0074] In step S104, the automated analyzer 100 determines whether the analysis value (factor value) of the first item determined to be abnormal in step S3 falls into the third region 93 or the fourth region 94 of the distribution diagram in FIG. 9 . If the analysis value of the first item falls into the fourth region 94, the degree of abnormality is determined to be high, and the process proceeds to the first confirmation step (step S5). If the analysis value of the first item falls into the third region 93, the degree of abnormality is determined to be low, and the process proceeds to the output step (step S8), where the output unit 15 displays a message indicating that the degree of abnormality of the analysis value of the first item is low. This allows the user to understand the degree of abnormality of the first item. It also allows the user to predict the possibility that abnormalities may have occurred in other items.

[0075] The automated analyzer, automated analysis system, and data analysis method according to the present invention have been described in detail above using embodiments. However, the present invention is not limited to the above-described embodiments and includes various modifications. For example, the above-described embodiments have been described in detail to clearly explain the present invention, and the present invention is not necessarily limited to those including all of the described configurations. Furthermore, it is possible to replace part of the configuration of one embodiment with the configuration of another embodiment, or to add the configuration of another embodiment to the configuration of one embodiment. Furthermore, it is possible to add, delete, or replace part of the configuration of each embodiment with other configurations.

[0076] 100 Automatic analysis device 1000 Automatic analysis system 1 Analysis unit 14 Memory unit 15 Output unit 16 Input unit 18 Remote terminal (computer) 31, 32 Dotted line (first discrimination line) 33 Normal range area 34 Abnormal range area 41, 42 Line (second discrimination line) 43 First area 44 Second area 91, 92 Line (third discrimination line) 93 Third area 94 Fourth area

Claims

1. An automatic analyzer comprising: an analysis unit that analyzes predetermined items related to a sample based on a reaction between the sample and a reagent; a generation unit that generates reaction process data for the sample; an input unit that receives input of a first item among the predetermined items related to the sample and a second item among the predetermined items related to the first item; and an output unit that, based on information received from the input unit and based on information stored in a memory unit that stores a first discrimination line that determines whether the analysis value of the predetermined item in the reaction process data is within a normal range region or an abnormal range region, and a second discrimination line that determines whether a first region within the normal range region is closer to the first discrimination line and a second region further from the first discrimination line, the analysis value of the first item falls within the abnormal range region and the analysis value of the second item falls within the first region.

2. The automatic analyzer according to claim 1, wherein a cause of an abnormality is associated with each of the abnormal range regions.

3. An automatic analyzer according to claim 1, further comprising a third discrimination line for discriminating between a third region closer to the first discrimination line and a fourth region farther from the first discrimination line within the abnormal range region, and determining that the degree of abnormality is small when the analysis value of the first item falls within the third region, and determining that the degree of abnormality is large when the analysis value of the first item falls within the fourth region.

4. An automatic analyzer according to claim 1, wherein when the analysis value of the first item falls within the abnormal range, a message to that effect is displayed on the output unit.

5. An automatic analyzer according to claim 1, wherein the analysis value of the first item falls within the abnormal range area, and the cause of the abnormality associated with the corresponding abnormal range area is displayed on the output unit.

6. An automatic analysis system comprising an automatic analyzer that analyzes samples and a computer connected to the automatic analyzer, wherein the automatic analyzer comprises: an analysis unit that analyzes predetermined items related to the sample based on a reaction between the sample and a reagent; and a generation unit that generates reaction process data for the sample; and the computer comprises: an input unit that receives input of a first item among the predetermined items related to the sample and a second item among the predetermined items related to the first item among the predetermined items related to the sample; and an output unit that, based on information received from the input unit and based on information stored in a memory unit that stores a first discrimination line that determines whether the analysis value of the predetermined item in the reaction process data is within a normal range region or an abnormal range region, and a second discrimination line that determines whether a first region within the normal range region is closer to the first discrimination line and a second region further from the first discrimination line, and which indicates that the analysis value of the first item falls within the abnormal range region and the analysis value of the second item falls within the first region.

7. An input step for receiving an input of a first item among predetermined items related to a specimen and a second item among predetermined items related to the first item, and storing the first item and the second item related thereto; a calculation step for generating reaction process data for the specimen and calculating an analytical value for the first item based on the reaction process data for the specimen; an abnormality determination step for determining whether the analytical value of the first item is abnormal using a first determination line that determines whether the analytical value of the predetermined item in the reaction process data is within a normal range or an abnormal range; a first confirmation step for confirming the analytical value of the second item related to the first item if the analytical value of the first item is determined to fall within the abnormal range in the abnormality determination step; and a second confirmation step for confirming whether the analytical value of the second item confirmed in the first confirmation step was determined to be normal or abnormal in the abnormality determination step. a determination step of, if the analysis value of the second item is determined to be normal in the second confirmation step, determining whether the analysis value of the second item falls within the first region or the second region using a second discrimination line that is within the normal range and that is closer to the first discrimination line and that is different from a second region that is farther from the first discrimination line; and an output step of displaying a message that the analysis value of the first item is normal if the analysis value is determined to be normal in the abnormality determination step, displaying a message that the analysis value of the second item is abnormal if the analysis value is determined to be abnormal in the second confirmation step, displaying a message that the analysis value of the first item falls within the abnormal range region and that the analysis value of the second item falls within the first region if the analysis value is determined to fall within the first region in the determination step, and displaying a message that the analysis value of the second item falls within the second region if the analysis value of the second item is determined to fall within the second region in the determination step.

8. A data analysis method according to claim 7, further comprising an anomaly cause confirmation step between the anomaly determination step and the first confirmation step, wherein the anomaly cause confirmation step confirms whether or not the anomaly cause is one that will cause the analysis value of the second item related to the analysis value of the first item determined to be anomaly in the anomaly determination step to proceed to a more detailed examination of the analysis value of the second item, and if it is one that will cause the analysis value of the second item to proceed to a more detailed examination, proceeds to the first confirmation step, and if it is not one that will cause the analysis value of the second item to proceed to a more detailed examination, proceeds to the output step and displays a message indicating that it is not an anomaly cause and that the analysis value of the second item will proceed to a more detailed examination of the analysis value of the second item.

9. A data analysis method according to claim 7, further comprising an abnormality degree determination step between the abnormality determination step and the first confirmation step, wherein the abnormality degree determination step has a third discrimination line that distinguishes between a third region closer to the first discrimination line and a fourth region farther from the first discrimination line within the abnormal range region, and when the analysis value of the first item falls within the third region, it is determined that the degree of abnormality is small and the process proceeds to the output step, displaying a message that the degree of abnormality of the analysis value of the first item is small, and when the analysis value of the first item falls within the fourth region, it is determined that the degree of abnormality is large and the process proceeds to the first confirmation step.

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