阅读说明：本技术 用于检验血液的方法、设备和系统 (Method, device and system for testing blood ) 是由 B·佳碧 K·斯特芬妮 L·乔基姆 F·凯瑟琳 于 2021-04-28 设计创作，主要内容包括：本公开涉及用于检验血液的方法、设备和系统。提供了用于检验对象的血液的计算机实现方法,该方法包括：获得用于血液的多个血液学参数；执行血液学参数的合理性检查；以及根据合理性检查的结果,基于参数输出对象的感染反应的确定的概率的指示。(The present disclosure relates to methods, devices and systems for testing blood. There is provided a computer-implemented method for testing blood of a subject, the method comprising: obtaining a plurality of hematological parameters for the blood; performing a rationality check of the hematological parameter; and outputting an indication of the determined probability of the infection response of the subject based on the parameter according to the result of the plausibility check.)

1. A computer-implemented method for testing blood of a subject, comprising:

-obtaining a plurality of hematological parameters for said blood;

-performing a plausibility check of at least one of said haematological parameters; and

-outputting an indication of the determined probability of the infection response of the subject based on at least one of the parameters in accordance with the result of the plausibility check.

2. The computer-implemented method of claim 1, wherein

The step of performing a plausibility check comprises checking a subset of said haematological parameters.

3. The computer-implemented method of one of claims 1-2, wherein

The step of performing a plausibility check comprises checking a haematological parameter obtained from a differential measurement of white blood cells.

4. The computer-implemented method of one of claims 1-3, wherein

The step of performing a plausibility check comprises checking the white blood cell count as a haematological parameter.

5. The computer-implemented method of one of claims 1-4, wherein

The step of performing a plausibility check comprises checking a haematological parameter whose value is correlated with obtaining a reliable value of another haematological parameter of said plurality of haematological parameters.

6. The computer-implemented method of any of claims 1-5, wherein

The step of performing a plausibility check comprises comparing the value of each of the haematological parameters for said plausibility check with a corresponding threshold value or a corresponding lower and upper threshold value.

7. The computer-implemented method of any of claims 1-6, wherein

The step of outputting the indication in dependence on the result of the plausibility check comprises: the indication is output when the plausibility check passes and not output when the plausibility check does not pass.

8. The computer-implemented method of any of claims 1-7, wherein

Outputting a measurement of at least one hematological parameter that causes a failure of the plausibility check when the plausibility check fails.

9. The computer-implemented method of one of claims 1-8, further comprising:

when the plausibility check fails, an error message or an error code is output.

10. The computer-implemented method of claim 9, wherein

The error message or error code includes information indicative of at least one hematological parameter that caused the failure of the plausibility check.

11. The computer-implemented method of any of claims 1-10, wherein

The indication of the probability of the infection reaction includes a value of an index value corresponding to the probability of the infection reaction.

12. The computer-implemented method of any of claims 1-11, further comprising:

generating the indication based on at least two of the hematological parameters, wherein

The step of performing a plausibility check comprises checking a portion of said at least two of said hematological parameters.

13. The computer-implemented method of any of claims 1-11, further comprising:

generating the indication based on at least two of the hematological parameters, wherein

The step of performing a plausibility check comprises checking a portion of said at least two haematological parameters and a haematological parameter different from said at least two of said haematological parameters.

14. The computer-implemented method of any of claims 1-13, further comprising:

a general plausibility check of the detection device measuring said blood is performed.

15. The computer-implemented method of one of claims 1-14, wherein

The step of performing a plausibility check comprises checking a haematological parameter reflecting the operating state of a detection device measuring said blood, or reflecting the quality of a reagent mixed with said blood.

16. A diagnosis support apparatus comprising:

a controller configured to:

-obtaining a plurality of hematological parameters for the blood;

-performing a plausibility check of at least one of said haematological parameters; and

-outputting an indication of the determined probability of the infection response of the subject based on at least one of the parameters in accordance with the result of the plausibility check.

17. A cytometer system comprising a blood detector apparatus and a diagnostic support apparatus according to claim 16.

18. A computer program product, comprising:

a computer-readable medium storing instructions that enable a general-purpose computer to perform the operations of the computer-implemented method of any of claims 1-15.

Technical Field

The present invention relates to a computer-implemented method, a diagnostic support device, a cytometer system and a computer program product for testing blood.

Background

Systemic Inflammatory Response Syndrome (SIRS) is a condition in which a subject is experiencing a severe inflammatory response throughout the body, due to infectious lesions or non-infectious lesions.

An example of SIRS accompanied by infectious inflammatory responses is sepsis. Sepsis is a disease whose symptoms may progress to severe sepsis, septic shock, and multiple organ dysfunction syndrome (MOD), eventually leading to death if not properly disposed at an early stage. Thus, if a subject has been given a diagnosis of SIRS, early determination of whether the response is an infectious or non-infectious inflammatory response, particularly whether the subject is infected with bacteria or viruses, has a large impact on the treatment to be given to the subject. Moreover, the determination of whether an inflammatory response is an infectious response or a non-infectious response may have a large impact on the treatment that will be administered to a subject without SIRS.

Disclosure of Invention

Technical problem

EP 2302378B 1 describes a cytometer and a diagnostic support method by which diagnostic support information is determined on the basis of several measured hematological parameters, for example in the form of an index (ici: intensive care infection score), for supporting the determination whether the inflammatory response of a subject is an infectious inflammatory response, in particular an infectious inflammatory response caused by bacteria or viruses. Bacterial or viral infections, if not immediately and properly treated, can rapidly progress to life-threatening conditions such as sepsis. A medical professional may attempt to determine whether inflammation is caused by a bacterial or viral infection that leads to sepsis or some other cause that leads to SIRS diagnosis.

The method of EP 2302378B 1 determines an ici index based on the measured hematological parameter and outputs the ici index as diagnostic support information. However, the ICIS index is output without a technical assessment of the reliability of the determined ICIS index, resulting in the possibility of outputting an inappropriate ICIS index value, and the possibility of an unreliable assessment of the possibility of an infectious response (such as sepsis).

Against this background, it is an object of the present invention to overcome these limitations.

Solution scheme

According to a first aspect, a computer-implemented method includes obtaining a plurality of hematological parameters for the blood; performing a plausibility check of at least one of the hematological parameters; and outputting an indication of the determined probability of the infection response of the subject based on at least one of the parameters in accordance with the results of the plausibility check.

According to a second aspect, a diagnosis support apparatus includes a controller configured to: obtaining a plurality of hematological parameters for the blood; performing a plausibility check of at least one of the hematological parameters; and outputting an indication of the determined probability of the infection response of the subject based on at least one of the parameters in accordance with the results of the plausibility check.

According to a third aspect, a cytometer system includes a blood detector apparatus and a diagnostic support apparatus according to embodiments of the present disclosure.

According to a fourth aspect, a computer program product includes a computer-readable medium storing instructions that enable a general purpose computer to perform operations of a computer-implemented method according to embodiments of the present disclosure.

The solutions according to the first, second, third and fourth aspect ensure that only valid, reliable and numerical parameter values are used for determining the probability of an infection response of a subject.

Drawings

Fig. 1 is a front view showing a schematic configuration of a cytometer system according to an embodiment.

Fig. 2 is a block diagram showing the configuration of a detection device of the cytometer system according to the embodiment.

Fig. 3 is a schematic plan view showing a configuration of a detector of the detection apparatus according to the embodiment.

Fig. 4 is a block diagram showing a configuration of a first diagnosis support apparatus of the cytometer system according to the embodiment.

Fig. 5 is a block diagram showing a configuration of a second diagnosis support apparatus of the cytometer system according to the embodiment.

Fig. 6 is a flowchart showing a processing sequence performed by the second diagnosis support apparatus of the cytometer system according to the embodiment.

Figure 7 is a flow chart illustrating a processing sequence performed by the second diagnostic support apparatus of the cytometer system according to another embodiment.

Figure 8 shows a RET scattergram created by the detection apparatus of the cytometer system according to an embodiment.

Figure 9 shows a DIFF scattergram created by the detection apparatus of the cytometer system according to an embodiment.

Figure 10 illustrates a WBC/BASO scatter plot created by a detection device of a cytometer system according to an embodiment.

Fig. 11 shows an example of an output screen for showing the measurement result of blood sampling.

Fig. 12 shows another example of an output screen for showing the measurement result of blood sampling.

Figure 13 is a flow chart illustrating a processing sequence performed by the first diagnostic support apparatus of the cytometer system according to another embodiment.

Figure 14 is a flow chart illustrating a processing sequence performed by the first diagnostic support apparatus of the cytometer system according to another embodiment.

Fig. 15A and 15B show examples of DIFF scattergrams for explaining the rationality check.

Fig. 16A and 16B show examples of RET scattergrams for explaining the rationality check.

Detailed Description

Hereinafter, specific descriptions of a diagnosis support apparatus, a diagnosis support method, and a computer program according to embodiments of the present invention will be given with reference to the accompanying drawings. It will be understood that the following embodiments are not intended to limit the invention defined by the claims, and that not all combinations of features described in the embodiments are necessarily indispensable factors for means of solution.

Figure 1 is a front view showing a schematic configuration of a cytometer system according to an embodiment of the present invention. As shown in fig. 1, a cytometer system 1 according to an embodiment of the present invention includes a blood detection device 2, a first diagnostic support device 3, and a second diagnostic support device 3'. The blood detection apparatus 2 is an apparatus that detects blood cells showing, for example, an inflammatory reaction in blood of a subject. The first diagnosis support apparatus 3 and the second diagnosis support apparatus 3' support determination of whether or not an inflammatory reaction is caused by infection. The first diagnosis support apparatus 3 may be a computer, which may be referred to as an Information Processing Unit (IPU), and receives the detection data from the blood detection apparatus 2 and analyzes the detection data. The second diagnosis support apparatus 3' may be a computer that may be referred to as a Work Area Manager (WAM) and receives a measurement command for calculating an ici index value of a subject from an external device (experimental information system (LIS)) and transmits the measurement command to the first diagnosis support apparatus 3.

The first diagnosis support apparatus 3 transmits a measurement command of the subject to the detection apparatus 2, and receives data containing a result of the detection performed by the detection apparatus 2, and performs analysis processing. The cytometer system 1 is installed, for example, in a medical facility such as a hospital or a hematology laboratory. The detection apparatus 2 and the first diagnosis support apparatus 3 may be connected via a transmission cable 3a so as to be able to perform data communication therebetween. Likewise, the first diagnosis support apparatus 3 and the second diagnosis support apparatus 3 'may be connected via a transmission cable 3 a' so as to be able to perform data communication therebetween. Note that the connection between the detection apparatus 2 and the first diagnosis support apparatus 3, and/or the connection between the first diagnosis support apparatus 3 and the second diagnosis support apparatus 3 'is not limited to the direct wired connection formed by the transmission cables 3a and 3 a', respectively. For example, the connection may be realized via a wireless connection (such as a dedicated line using a telephone line, a LAN, or a communication network such as the internet).

At the lower right corner of the front of the detection apparatus (or blood analyzer) 2, a blood collection tube setting section 2a is provided, on which a blood collection tube containing blood of a subject can be set. When an operator presses a push button switch 2b provided near the blood collection tube setting section 2a, the blood collection tube setting section 2a is moved toward the operator, thereby enabling the operator to set a blood collection tube thereon. When the operator presses the push switch 2b again after setting the blood collection tube, the blood collection tube setting portion 2a moves toward the detection device 2 to be accommodated in the detection device 2. However, the detection device 2 is not limited to manual setting of a blood collection tube. Alternatively, the blood collection tubes may be collected in a sampling rack and moved by a conveyor belt or conveyor to the detection apparatus 2, with the respective blood collection tube being provided to the detection apparatus 2.

Figure 2 is a block diagram illustrating the configuration of the detection apparatus 2 of the cytometer system 1 according to an embodiment of the present invention. Referring to fig. 2, the detection apparatus 2 includes a sample feeder 4, a detector 5, a controller 8, and a communication section 9. The sample feeder 4 is a fluid unit including a sample preparation unit 4a, and the sample preparation unit 4 is composed of a chamber, a plurality of electromagnetic valves, a diaphragm pump, and the like. The sample preparation unit 4a prepares a detection sample by mixing the blood of the subject with a reagent. The sample feeder 4 feeds the detection sample prepared by the sample preparation unit 4a to the detector 5. The controller 8 controls the operation of the components of the detection device 2. The communication section 9 may be, for example, an RS-232C interface, a USB interface, or an Ethernet (registered trademark) interface, and transmits/receives data to/from the diagnosis support apparatus 3.

Fig. 3 is a schematic plan view schematically showing the configuration of the detector 5 of the blood cell technology system 1 according to the embodiment of the present invention. Referring to fig. 3, the detector 5 is an optical flow cytometer, and White Blood Cells (WBCs), Reticulocytes (RETs), mature Red Blood Cells (RBCs), and Platelets (PLTs) in blood are detected by the flow cytometer using a semiconductor laser. The term "red blood cell" is used herein to specifically include "Reticulocytes (RET)" and "mature Red Blood Cells (RBC)". The detector 5 comprises a flow cell 51, the flow cell 51 being used to form a fluid flow for detecting samples. The flow cell 51 is formed of a translucent material (such as quartz, glass, synthetic resin, or the like), and has a tubular shape. Flow cell 51 has a flow path therein through which the test sample and sheath fluid flow. The detector 5 includes a semiconductor laser light source 52, and the semiconductor laser light source 52 is provided to output laser light toward the flow cell 51. Between the semiconductor laser light source 52 and the flow cell 51, an irradiation lens system 53 including a plurality of lenses is provided. The irradiation lens system 53 collects the parallel beam output from the semiconductor laser light source 52 to form a beam spot. An optical axis extends from the semiconductor laser light source 52 through the flow cell 51 in line. The photodiode 54 is disposed on the optical axis such that the photodiode 54 is disposed on the opposite side of the flow cell 51 from the irradiation lens system 53. The beam stopper 54a is provided so as to block light directly from the semiconductor laser light source 52.

When the detection sample flows into the flow cell 51, scattered light and fluorescence occur based on the laser light. Among the scattered light and the fluorescent light, light of the laser light in the irradiation (i.e., forward) direction is photoelectrically converted by the photodiode 54. Among the light traveling along the optical axis extending in line from the semiconductor laser light source 52, the light directly from the semiconductor laser light source 52 is blocked by the beam stopper 54 a. Only scattered light that travels substantially in the optical axis direction (hereinafter referred to as forward scattered light) is incident on the photodiode 54. Forward scattered light emitted from the detection sample flowing in the flow cell 51 is photoelectrically converted into an electric signal by the photodiode 54, and each photoelectrically converted electric signal (hereinafter referred to as forward scattered light signal) is amplified by the amplifier 54b to be output to the controller 8. The intensity of the forward scattered light signal is indicative of the size of the blood cells.

The side condenser lens 55 is provided at the side of the flow cell 51 so as to be positioned in a direction on the optical axis intersecting the optical axis extending from the semiconductor laser light source 52 to the photodiode 54 in line. The side condenser lens 55 condenses side light (i.e., light output in a direction on the optical axis intersecting the optical axis extending in line from the semiconductor laser light source 52 to the photodiode 54) that occurs when laser light is emitted to the detection sample by the flow cell 51. A beam splitter 56 is provided downstream of the side condenser lens 55. The light condensed by the side condenser lens 55 is divided into a scattered light component and a fluorescent light component by the beam splitter 56. In the optical axis direction in which the light reflected by the beam splitter 56 advances (i.e., the direction on the optical axis intersecting the optical axis passing through the side condenser lens 55 and the beam splitter 56), a photodiode 57 for receiving the side scattered light is provided. On the optical axis passing through the side condenser lens 55 and the beam splitter 56, a photodiode 58 and a filter 58a for receiving fluorescence are provided.

The light reflected by the beam splitter 56 is side scattered light, and is photoelectrically converted into an electric signal by the photodiode 57. Each of the photoelectrically converted electrical signals (hereinafter referred to as side scattered light signals) is amplified by an amplifier 57a and then output to the controller 8. Each side scattered light signal indicates internal information of the blood cell (size of cell nucleus, etc.). The light transmitted through the beam splitter 56, which is fluorescence, is photoelectrically converted into an electrical signal by the photodiode 58 after being wavelength-selected by the filter 58 a. Each of the photoelectrically converted electric signals (hereinafter referred to as fluorescent signals) is amplified by the amplifier 58b and then output to the controller 8. Each fluorescent signal indicates the degree of staining of the blood cells.

Fig. 4 is a block diagram showing the configuration of the first diagnosis support apparatus 3 of the cytometer system 1 according to the embodiment of the present invention. As shown in fig. 4, the first diagnosis support apparatus 3 includes at least a data processing section (controller) 31, an image display section 32, and an input section 33, and the data processing section (controller) 31 includes a CPU (central processing unit) and the like. The data processing section 31 includes a CPU31a, a memory 31b, a hard disk 31c, a readout device 31d, an input/output interface 31e, an image output interface 31f, a communication interface 31g, and an internal bus 31 h. In the data processing section 31, the CPU31a is connected to each of the memory 31b, the hard disk 31c, the readout device 31d, the input/output interface 31e, the image output interface 31f, and the communication interface 31g via the internal bus 31 h.

The CPU31a controls the operation of each of the above hardware components, and processes data received from the detection apparatus 2 according to a computer program 34 stored in the hard disk 31 c.

The memory 31b is configured as a volatile memory such as an SRAM or a flash memory. The loading module is loaded into the memory 31b when the computer program 34 is executed. The memory 31b stores temporary data and the like generated when the computer program 34 is executed.

The hard disk 31c is constructed as a fixed storage device or the like, and is incorporated in the first diagnosis support apparatus 3. The computer program 34 is downloaded by the readout device 31d as a portable disk drive from a portable storage medium 35 (such as a DVD, a CD-ROM, a USB flash drive, etc.) that stores information (such as programs, data, etc.). The computer program 34 is then stored in the hard disk 31 c. The computer program 34 is loaded from the hard disk 31c to the memory 31b to be executed. It will be appreciated that the computer program 34 may be a computer program downloaded from an external computer via the communication interface 31 g.

The input/output interface 31e is connected to an input section 33 configured as a keyboard, tablet, or the like. The image output interface 31f is connected to an image display section 32, and the image display section 32 may be a CRT monitor, an LCD, or the like. Alternatively, the input section 33 and the image display section 32 may be included in a single device (such as a touch-based monitor).

The communication interface 31g is connected to the internal bus 31h, and performs data transmission/reception with an external computer such as the second diagnosis support apparatus 3' (described below), the detection apparatus 2, and the like by being connected to an external network such as the internet, a LAN, and a WAN. For example, the above hard disk 31c is not limited to a hard disk incorporated in the first diagnosis support apparatus 3, but may be an external storage medium such as an external storage connected to the first diagnosis support apparatus 3 via the communication interface 31 g.

Fig. 5 is a block diagram showing the configuration of the second diagnosis support apparatus 3' of the cytometer system 1 according to the embodiment of the present invention. The second diagnosis support apparatus 3' may have the same hardware configuration as the first diagnosis support apparatus 3 described above, and a detailed description is therefore omitted. As shown in fig. 5, the communication interface 31 g' performs data transmission/reception with the first diagnosis support apparatus 3. In addition, the hard disk 31 c' may store a message indicating the ICIS index. The message may further comprise an indication that the subject has or may have an infection response, and a message indicating that the subject has or may have a non-infection response, as diagnostic support information, for supporting an output of an indication of a determined probability of an infection response (ICIS index) of the subject. The hard disk 31c 'may also store scoring thresholds (described below) for one or more of a plurality of hematological parameters, which are used to score the plurality of hematological parameters to determine whether the subject's response is an infectious response or a non-infectious response, and thresholds (described below) for a plausibility check.

In the following, a description is given of the operation of the cytometer system 1 according to an embodiment of the present invention. First, the sample feeder 4 of the detection apparatus 2 sucks blood from the blood collection tube provided in the blood collection tube setting portion 2a, divides the sucked blood into a plurality of small portions according to a measurement command, and adds a predetermined dedicated reagent to the small portions, thereby preparing, for example, a RET detection sample, a DIFF detection sample, and a WBC/BASO detection sample. Note that RET detection sampling is prepared by subjecting blood to a dilution process, and further by a staining process using a dedicated reagent for detecting reticulocytes. The DIFF detection sample is prepared by subjecting blood to a dilution process, by subjecting to a lysis process using a dedicated reagent for lysing erythrocytes, and further by subjecting to a staining process using a dedicated reagent for classifying leukocytes into a plurality of subgroups. The WBC/BASO detection sample is prepared by subjecting blood to a dilution process, and further by a lysis process using a dedicated reagent for lysing red blood cells. The sample feeder 4 feeds the prepared detection sample to the flow cell 51 of the detector 5.

Fig. 6 is a flowchart showing a processing sequence executed by the CPU31a ' of the data processing section 31 ' of the second diagnosis support apparatus 3 ' of the cytometer system 1 according to the embodiment of the present invention. As shown in fig. 6, the CPU31a 'first obtains a plurality of hematological parameters, for example, net #, NE-SFL, RET #, HFLC #, IG #, EO #, Delta-He, RBC-He, PLT of the subject' S blood (S31). This can be achieved as follows:

when a detection sample is fed to the flow cell 51, the CPU31a of the first diagnosis support apparatus 3 can receive data of the forward scattered light signal, the side scattered light signal, and the fluorescence signal output from the detector 5 of the detection apparatus 2 via the communication interface 31g and store the data in the memory 31 b. The CPU31a creates a plurality of scattergrams based on the data of the forward scattered light signal, the side scattered light signal, and the fluorescence signal detected by the detector 5 and stored in the memory 31 b. The plurality of scatter charts created by the CPU31a include, for example, at least: a RET scattergram (see fig. 8) having a Y-axis of the intensity of forward scattered light signals and an X-axis of the intensity of fluorescent signals, both of which are output by the detector 5; a DIFF scattergram (see fig. 9) having a Y-axis of the intensity of the fluorescent signal and an X-axis of the intensity of the side scattered light signal, both of which are output by the detector 5; and a WBC/BASO scattergram (see fig. 10) having a Y-axis of the intensity of the forward scattered light signal and an X-axis of the intensity of the side scattered light signal, both of which are output by the detector 5. Alternatively, the X-axis and Y-axis of each scatter plot may be reversed.

Next, the CPU31a of the first diagnosis support apparatus 3 determines a plurality of hematological parameters for the blood of the subject by using the scatter diagram as a detection result. Examples of such hematological parameters are NEUT #, UE-SFL, RET #, HFLC #, IG #, EO #, Delta-He, RBC-He, PLT (described in detail below).

Fig. 8 shows a RET scattergram created by the blood cell counting system 1 according to an embodiment of the present invention. Based on the RET scattergram, the CPU31a of the first diagnosis support apparatus 3 calculates the difference (Delta-He) between the reticulocyte hemoglobin equivalent (RET-He), and the hemoglobin equivalent of mature red blood cells (RBC-He), both of which are hematological parameters obtained as follows. As shown in fig. 8, the CPU31a of the first diagnosis support apparatus 3 identifies three areas by using the RET scattergram: a mature Red Blood Cell (RBC) region 60, a Platelet (PLT) region 61, and a Reticulocyte (RET) region 62. Based on the RET scattergram, the CPU31a determines RBC-He, which is the forward scattered light intensity of the median value of all cells contained in the mature red blood cell region 60(RBC), and RET-He, which is the forward scattered light intensity of the median value of all cells contained in the reticulocyte region 62 (RET). The CPU31a of the first diagnosis support apparatus 3 calculates a reticulocyte count (RET #) from the reticulocyte region 62 as one of the plurality of hematological parameters. The CPU31a of the first diagnosis support apparatus 3 further calculates a platelet count (PLT) from the platelet region 61 as one of the plurality of hematological parameters. An example of such a calculation is shown in EP 2302378B 1.

Next, the CPU31a of the first diagnosis support apparatus 3 calculates a neutrophil count (NEUT #) as one of the plurality of hematological parameters by using the DIFF scattergram and the WBC/BASO scattergram. Note that the term "granulocyte" is used herein to explicitly include both "mature granulocytes" and "immature granulocytes". "mature granulocytes" specifically include Neutrophils (NEUT), Eosinophils (EO), and Basophils (BASO).

Fig. 9 shows a DIFF scattergram created by the blood cell counting system 1 in a white blood cell differential measurement according to an embodiment of the present invention. FIG. 10 shows a WBC/BASO scattergram created by the blood cell counting system 1 according to an embodiment of the present invention. As shown in fig. 9, the DIFF scattergram classifies blood cells into 6 regions, a Monocyte (MONO) region 71, a Lymphocyte (LYMPH) region 72, a Neutrophil (NEUT) + Basophil (BASO) region 73, an Eosinophil (EO) region 74, an Immature Granulocyte (IG) region 75, and a High Fluorescence Lymphocyte (HFLC) region 76. The sum of the number of Neutrophils (NEUT) and the number of Basophils (BASO) can be calculated by counting the number of leukocytes in the Neutrophil (NEUT) + Basophil (BASO) area 73 based on the DIFF scattergram. Here, CPU31a of first diagnosis support apparatus 3 further calculates an eosinophil count (EO #) from eosinophil region 74 as one of the plurality of hematological parameters.

In order to calculate a neutrophil count (NEUT #) from the sum of the number of Neutrophils (NEUT) and the number of Basophils (BASO) as one of the plurality of hematological parameters according to the area 73 in FIG. 9, the CPU31a of the first diagnostic support apparatus 3 determines the number of Basophils (BASO) by using the WBC/BASO scattergram. As shown in fig. 10, the WBC/BASO scattergram classified leukocytes into two regions, Monocyte (MONO) + Lymphocyte (LYMPH) + Neutrophil (NEUT) + Eosinophil (EO) region 81 and Basophil (BASO) region 82. Thus, the number of Basophils (BASO) in the Basophil (BASO) area 82 may be calculated by counting the number of white blood cells in the basophil area 82 based on the WBC/BASO scattergram. A neutrophil count (NEUT #) that is one of the plurality of hematological parameters may then be obtained by subtracting the number of Basophils (BASO) calculated based on the WBC/BASO scatterplot from the sum of the number of Neutrophils (NEUT) and the number of Basophils (BASO) calculated based on the DIFF scatterplot.

Next, the CPU31a of the first diagnosis support apparatus 3 calculates a value (NE-SFL) indicating the fluorescence intensity of neutrophils in the blood, which is one of the plurality of hematological parameters, by using the DIFF scattergram. Specifically, the value (NE-SFL) in fig. 9 can be obtained by calculating the median of the fluorescence intensities of all cells (i.e., Neutrophil (NEUT) and Basophil (BASO)) included in the Neutrophil (NEUT) + Basophil (BASO) region 73 based on the DIFF scattergram. Although the obtained value (NE-SFL) included the influence of fluorescence intensity of Basophils (BASO), the number of Basophils (BASO) was small, and therefore the influence was small.

Next, the CPU31a of the first diagnosis support apparatus 3 calculates an immature granulocyte count (IG #) by using a DIFF scattergram, IG #, which is one of the plurality of hematological parameters with respect to granulocytes in blood. Specifically, the immature granulocyte count (IG #) can be obtained by counting the number of cells in the Immature Granulocyte (IG) region 75 based on the DIFF scattergram.

Next, the CPU31a of the first diagnosis support apparatus 3 calculates a high fluorescence lymphocyte count (HFLC #) regarding the lymphocytes of the antibody synthesis by using a DIFF scattergram, the HFLC # being one of a plurality of hematological parameters of the blood. Specifically, the high fluorescence lymphocyte count (HFLC #) can be obtained by counting the number of cells in the High Fluorescence Lymphocyte (HFLC) area 76 based on the DIFF scattergram.

The skilled person understands that the above examples of obtaining a plurality of hematological parameters are non-limiting and that other scatter plots and other hematological parameters may be determined.

Referring back to fig. 6, in step S31, the second diagnostic support apparatus 3 'obtains the plurality of hematological parameters, e.g., neit #, NE-SFL, RET #, HFLC #, IG #, EO #, Delta-He, RBC-He, PLT, from the first diagnostic support apparatus 3 by receiving the determined hematological parameters (as described above) via the communication interface 31 g'.

Then, according to step S32 of fig. 6, the CPU31a 'of the second diagnosis support apparatus 3' performs a plausibility check on at least one of the hematological parameters obtained at step S31.

In particular, a plausibility check may be performed on hematological parameters obtained from differential measurements of white blood cells, such as NE-SFL and NEUT #. In addition, a plausibility check may be performed on hematological parameters for white blood cell counts (such as NEUT #) and/or red blood cell counts (such as RET #).

The plausibility check may be performed by comparing at least one of the hematological parameters to a threshold value or a lower threshold value and an upper threshold value. That is, the rationality check may be performed by comparing the value of each of the hematological parameters for the rationality check with the corresponding threshold value or the corresponding lower and upper threshold values. The threshold(s) are predetermined and set to ensure that the hematological parameter has a reasonable or reliable value that meets quality requirements with respect to the ici index value. In other words, the threshold(s) are set to a plausible value that excludes determinations that would result in errors in the ICIS index value.

Next, in step S33, the CPU31a 'of the second diagnosis support apparatus 3' determines whether the plausibility check on at least one of the hematological parameters is passed, that is, whether the obtained at least one of the hematological parameters is higher than a predetermined threshold value, or within a certain parameter range defined as a lower threshold value and an upper threshold value.

If the plausibility check is passed (yes in step S33), the CPU31a 'of the second diagnosis support apparatus 3' proceeds to step S34 to determine an ici index value indicating a probability of having an infection response, in particular, indicating a possibility of having a bacterial infection (described in more detail below). On the other hand, if the plausibility check is not passed (no in step S33), the CPU31 a' proceeds to step S36, and outputs a corresponding error code or error message (described in more detail below), while an indication of the determined probability (the ICIS index) is not output. In addition, if the rationality check is not passed (no in step S33), the CPU31 a' may cause the measurement result of the at least one hematological parameter that causes the rationality check to fail to be output (see fig. 12 below).

When passing the rationality check, the CPU31 a' determines an ici index value based on the obtained hematological parameter (step S34), and then outputs the ici index value as output diagnosis support information in step S35, the ici index value being associated with a probability of having an infection response, particularly, a possibility of having a bacterial infection.

Specifically, with respect to the calculation of the ici index value, the CPU31 a' may set the ici points with respect to the hematological parameters (Delta-He, RBC-He, NEUT #, EO #, PLT, NE-SFL, IG #, HFLC #) obtained in step S31, and calculate the ici index value by summing up the set ici points. This may be achieved by implementing parameter-specific ici rules as follows:

a) ICIS rules for HFLC #

Here, the CPU31 a' determines the ici point based on the following rule on the hematological parameter HFLC #:

rule 1	ICIS point
		HFLC#＜M1/μL	0
M1/μL≤HFLC#＜M2/μL	1
		M2/μL≤HFLC#＜M3/μL	2
HFLC#＞＝M3/μL	4

The first ICIS rule applies specific scoring thresholds M1, M2, and M3. The skilled person understands that the actual values of the thresholds M1, M2 and M3 depend on the blood sample, the configuration and accuracy of the testing device 2, etc. As such, the corresponding first ici spot 0, 1, 2 or 4 is set according to the determined high fluorescence lymphocyte count HFLC #.

b) ICIS rules for IG # and EO #

Here, the CPU31 a' determines the ici point based on the following rules regarding the hematological parameters IG # and EO #:

rule 2	ICIS point
		IG#＜M4/μL	0
M4/μL≤IG#＜M5/μL	1
		M5/μ L. ltoreq. IG # < M6/μ L and EO # < M7/μ L	2
IG # > M6/μ L and EO # < M7/μ L	4
		IG # ≧ M5/μ L and EO # ≧ M7/μ L	0

The second ICIS rule applies specific scoring thresholds M4, M5, M6, and M7. The skilled person understands that the actual values of the thresholds M4, M5, M6 and M7 depend on the blood sample, the configuration and accuracy of the detection apparatus 2, etc. As such, according to the determined granulocyte count IGC/: and a determined eosinophil count EO #, setting the corresponding second ICIS point 0, 1, 2, or 4.

c) ICIS rules for ne.sfl

Here, the CPU31 a' determines the ici point based on the following rule on the hematological parameter ne.sfl:

rule 3	ICIS point
		NE-SFL＜M8Ch	0
M8ch≤NE-SFL＜M9ch	1
		M9ch≤NE-SFL＜M10Ch	2
NE-SFL＞＝M10ch	4

The third ICIS rule applies specific scoring thresholds M8, M9, and M10. The skilled person understands that the actual values of the thresholds M8, M9 and M10 depend on the blood sample, the configuration and accuracy of the testing device 2, etc. In this way, the corresponding third ici point 0, 1, 2 or 4 is set according to the fluorescence value ne.sfl of the determined intermediate value.

d) ICIS rules for Delta-He and RBC-He

Here, the CPU31 a' determines the ici point based on the following rules with respect to the hematology parameters Delta-He and RBC-He:

rule 4	ICIS point
		Delta-He is more than or equal to M11pg (microgram)	0
M12pg is not less than Delta-He < M11pg and RBC-He is not less than M13pg	1
		M14pg is not less than Delta-He < M12pg and RBC-He is not less than M13pg	2
Delta-He < M14pg and RBC-He ≧ M13pg	4
		Delta-He < M11pg and RBC-He < M13pg	0

The fourth ICIS rule applies specific scoring thresholds M11, M12, M13, and M14. The skilled person understands that the actual values of the thresholds M11, M12, M13 and M14 depend on the blood sample, the configuration and accuracy of the detection apparatus 2, etc. As such, a corresponding fourth ICIS point of 0, 1, 2, or 4 is set based on the determined Delta-He and RBC-He values.

e) ICIS rules for NEUT #, EO #, and PLT

Here, the CPU31 a' determines the ici point based on the following rules regarding the hematological parameters NEUT #, EO # and PLT. With respect to the ICIS index, the total neutrophil count may be understood as the sum of the IG # value and the NEUT # value ("immature granulocytes and mature granulocytes"), referred to below as NEUT #.

Rule 5	ICIS point
		M15/μL≤NEUT#＜M16/μL	0
M16/μL≤NEUT#＜M17/μL	1
		NEUT # ≧ M17/μ L and EO # < M18/μ L and NEUT # < M19/μ L	2
NEUT#＜M15/μL	2
		NEUT # ≧ M19/μ L and PLT < M20/μ L and EO # < M18/μ L	4
NEUT # ≧ M17/μ L and EO # ≧ M18/μ L and NEUT # < M19/μ L	0
		NEUT # ≧ M19/μ L and PLT ≧ M20/μ L and EO # < M18/μ L	0
NEUT # ≧ M19/μ L and PLT < M20/μ L and EO # ≧ M18/μ L	0
		NEUT # ≧ M19/μ L and PLT ≧ M20/μ L and EO # ≧ M18/μ L	0

The fifth ICIS rule applies specific scoring thresholds M15, M16, M17, M18, M19, and M20. The skilled person understands that the actual values of the thresholds M15, M16, M17, M18, M19 and M20 depend on the blood sample, the configuration and accuracy of the detection device 2, etc. As such, a corresponding fifth ici point 0, 1, 2 or 4 is set based on the determined values of neit #, EO #, and PLT.

As illustrated, in order to calculate the ici index value in step S34, respective scoring thresholds are set with respect to the hematological parameter by applying a plurality of ici rules in which the hematological parameter obtained in step S31 is compared with respective scoring thresholds pre-stored in the hard disk 31 c'. While EP 2302378 provides further details as to how such a scoring threshold may be determined (e.g. based on ROC (receiver operating characteristics) analysis, which uses ROC curves to determine the scoring threshold), the skilled person understands that other methods may be equally applied to the determination of the above scoring threshold.

Therefore, the CPU31 a' may calculate the ici index value by summing the corresponding ici points determined according to the ici rule as described above. The minimum score was 0 points, indicating that the inflammatory response is highly unlikely to be caused by infection. The maximum score was 20 points. The skilled person understands that the closer the score is to 20 points, the more likely the inflammatory response is caused by an infection (such as a bacterial infection), as illustrated in EP 2302378B 1. The determination of the ICIS index is not limited to the above example. In particular, the skilled person understands that the determination of the ICIS index may be performed based on more or less hematological parameters or other ICIS rules.

According to a further embodiment, the CPU31a 'of the second diagnosis support apparatus 3' may determine the type of infection reaction, for example, whether the inflammatory reaction of the subject is an infection reaction or a non-infection reaction, for example, whether it is an infection inflammatory reaction or a non-infection inflammatory reaction, or a bacterial infection reaction or a non-bacterial infection reaction, based on the index (ICIS) calculated in step S34. The outputted diagnostic support information may then include both the ICIS index and the type of infection response.

In a further embodiment, the CPU31a 'may determine whether the subject's response is an infectious response or a non-infectious inflammatory response based on an index (ICIS). Specifically, if the index (ICIS) is not less than the determination threshold value stored in the hard disk 31c, the CPU31 a' may determine that the reaction of the object is, or may be, an infection reaction; if the index (ICIS) is less than a determination threshold, the response of the subject is determined to be, or is likely to be, a non-infectious response.

Returning now to the description of the output processing of the diagnosis support information performed by the CPU31a 'of the second diagnosis support apparatus 3' (see fig. 6). If the rationality check in step S33 is passed, the CPU31a 'outputs the diagnosis support information to the image display section 32' via the image output interface 31f ', and outputs the diagnosis support information to another computer, printer, or the like via the communication interface 31 g' (step S35). Specifically, the diagnosis support information includes the specific ici index value calculated in step S34, and may further include the determined type of infection response.

Further, if the plausibility check is not passed, the CPU31a 'of the second diagnosis support apparatus 3' reads an error code or an error message from the hard disk 31c ', outputs the error code or the error message to the image display section 32' via the image output interface 31f ', and outputs the error code or the error message to another computer, a printer, or the like via the communication interface 31 g' (step S36).

Although the above embodiments have been described with respect to examples of hematological parameters NEUT #, NE-SFL, RET #, HFLC #, IG #, EO #, Delta-He, RBC-He, PLT, the skilled person understands that the ICIS index value may be determined based on more or less hematological parameters, in particular, the above hematological parameters do not all have to be used for the ICIS index value. For example, in the above embodiment, although RET # is used for the rationality check, it may not be required to determine the ici index value.

Fig. 7 is a flowchart showing a processing sequence executed by the CPU31a ' of the data processing section 31 ' of the second diagnosis support apparatus 3 ' of the cytometer system 1 according to another embodiment of the present invention. Here, unlike the processing sequence in fig. 6, in step S32a, the rationality check is performed on only a subset of the hematological parameters obtained in step S31. For example, if a set of 9 haematological parameters, such as NEUT #, NE-SFL, RET #, HFLC #, IG #, EO #, Delta-He, RBC-He, PLT, is obtained in step S31, as explained above with respect to FIG. 6, it is not necessary to perform the rationality check S32a on the entire set of haematological parameters, but rather the rationality check S32a may be performed on only a limited subset, for example, on the three parameters NEUT #, NE-SFL, RET #. The skilled person understands that this improves the efficiency of performing a plausibility check on the ici index of a blood sample, since a plausibility check is not required for all obtained hematological parameters.

More specifically, according to step S32a of fig. 7, the CPU31a ' of the data processing section 31 ' of the second diagnosis support apparatus 3 ' obtains a subset of the hematological parameters, and performs a plausibility check on the subset. As explained above, when a plurality of hematological parameters (such as NEUT #, NE-SFL, RET #, HFLC #, IG #, EO #, Delta-He, RBC-He, PLT) are obtained, a subset of the hematological parameters refers to a limited number of hematological parameters. In the present non-limiting example, the subset may correspond to the three parameters NEUT #, NE-SFL, RET #.

Here, the rationality preferably has to be passed for the entire subset of hematological parameters in order to output diagnostic support information (ICIS index). In other words, if the plausibility check is successful (passed) only for one or two hematological parameters, e.g., for the entire subset of hematological parameters, but fails for at least one other hematological parameter for the entire subset, the plausibility check of step 33a fails (failed).

The underlying reason for the possibility of performing a plausibility check on only a limited subset of the obtained haematological parameters is based on the inventors' observation that there is a relationship between haematological parameters when determining plausibility. In particular, when considering the created scatter plot, it has been found that, for example, reasonable values for NEUT # are also of concern for determining other hematological parameters, and vice versa.

Fig. 15A and 15B show an example of this relationship. Specifically, from the scatter plot in FIG. 15A, the median of NE-SFL (see FIG. 9 above) is easily determined because it has a clearly defined population from which it can be determined. However, fig. 15B shows a case of a very low count, and the center of the neutrophil population may not be the actual center, and the coefficient of variation between the points of the scattergram is large, so that in a case where the NEUT # is very low, NE-SFL is unreliable.

The skilled person understands that the failed plausibility check on NE-SFL has an impact on other hematological parameters derived from the scatter plot. For example, the reliability of IG # classification or HFLC # (compare with fig. 9) can generally be checked via NE-SFL, which is a sensitivity parameter and reflects a fluorescence measure.

The reliability of Delta-He or RBC-He can be checked via RET # because these parameters are obtained from the same RET scattergram, and RET # gives an impact on the calculation of Delta-He and RBC-He. If the number of RET cells is below the threshold, there are insufficient cells present, which in turn may not be able to determine the reliable center of the RET population on the Y-axis, which results in unreliable RET-He, and consequently unreliable Delta-He. Fig. 16A and 16B show an example of this relationship, here for the RET scattergram. Here, fig. 16A shows an example with a clear RET population. However, fig. 16B shows an example where RET # (almost) does not exist, making RET-He unreliable. This unreliability is then also related to the haematological parameter Delta-He (RET-He-RBC-He), that is to say if RET-He is unreliable, Delta-He is also considered unreliable and therefore not considered for ICIS index calculations.

In step S32a of fig. 7, each parameter of the subset may be compared to a corresponding threshold or lower and upper thresholds. In the above non-limiting example, the CPU31 a' is configured (specifically programmed) to check whether the RET # value is above a threshold value, which is an example of a threshold valueSuch as a value that reflects the smallest RET population to obtain a reliable center for that RET population, followed by the RET-He value. The threshold may be 0.00410⁶/μL、0.005 10⁶μ L or 0.00610⁶/. mu.L, etc. The skilled person understands that such thresholds may be different for different sample sizes or different analyzer settings.

If the RET # value is above the threshold, the rationality check for RET # is passed (i.e., RET # has a reasonable value). Otherwise, if the RET # value is equal to or less than the threshold, the CPU31 a' creates a parameter-specific message ("ici _ RET _ UNRELIABLE") as a code or flag indicating that the plausibility check for RET has failed, which code or flag does not allow calculation of the ici score.

Further, the CPU31 a' is configured to (specifically programmed to) check whether the NE-SFL has a fluorescence intensity between the lower threshold and the upper threshold. If the fluorescence intensity is in the range defined between the lower threshold and the upper threshold, the rationality check for NE-SFL is passed (i.e., NE-SFL has a reasonable value). Otherwise, if the NE-SFL value is equal to or less than the lower threshold, or equal to or greater than the upper threshold, the CPU31 a' creates a parameter-specific message ("ici _ NE _ SFL _ UNRELIABLE") as a code or flag indicating that the plausibility check for the NE-SFL has failed, which code or flag does not allow calculation of the ici score.

Further, the CPU31 a' is configured (specifically programmed) to check whether the neit # value is higher than a threshold value. Such a threshold reflects the minimum population of NEUT required to obtain reliable NE-SFL (fluorescence intensity of NEUT regions in the corresponding scatter plot). If NEUT # value is above the threshold, then the rationality check for NEUT # is passed (i.e., NEUT # has a reasonable value). Otherwise, if the NEUT # value is equal to or less than the threshold, the CPU31 a' creates a parameter-specific message ("ici _ NEUT _ UNRELIABLE") as a code or flag indicating that the plausibility check for NEUT # has failed, which code or flag does not allow calculation of the ici score. The threshold may be 0.410³/μL、0.5 10³mu.L or 0.610³/. mu.L, etc. The skilled person understands that such thresholds may be different for different sample sizes or different analyzer settingsIn (1).

Based on this rationality check, the CPU31 a' determines in step S33a whether the rationality check has passed for all the parameters of the subset (i.e., for all the three hematological parameters NEUT #, NE-SFL, RET #, of the above example).

If the rationality check has passed for all parameters of the subset (yes in step S33 a), the CPU31 a' proceeds with the calculation of the ici index in step S34 (as described in fig. 6) and the remaining steps as explained above in fig. 7.

Otherwise, if the rationality check is not passed for one or more parameters of the subset (no in step S33 a), the CPU31 a' proceeds to step S36 and outputs an error code (flag) or an output message, e.g., an ici _ NEUT _ UNRELIABLE, an ICIS _ NE _ SFL _ UNRELIABLE, an ICIS _ RET _ UNRELIABLE, as described in fig. 7, identifying which parameter of the subset failed the rationality check.

Fig. 11 shows an example of an output screen 100 for showing the measurement results of blood sampling according to a measurement command for calculating an ici index value, wherein a determined hematological parameter is indicated. Here, in the output screen 100, for example, with respect to neit #, LYMPH #, MONO # and the like, specific determined values of hematological parameters of blood samples having specific sample IDs, priorities, collection dates and the like are shown. In addition, the ici index value is shown with a highlighted box 101, which has a value of 16 for the current blood sample. The skilled person understands from this value that the inflammatory response of the subject is most likely an infectious response. When the ici index value is output due to the fact that the plausibility check has passed, a technically reliable ici index value may be provided.

Fig. 12 shows another example of an output screen 110, the output screen 110 being used to show the measurement results of a blood sample according to a measurement command for calculating an ici index value, wherein a determined hematological parameter is indicated, for example, the example neit # ═ 0.3110³mu.L. Since the parameter for NEUT # has been obtained as 0.3110³μ L, and the value is below the corresponding threshold (as indicated above), the rationality check fails, there is no ICThe IS index value IS determined (indicated as "not measurable" in highlighted box 111 of fig. 12) and indicates that the parameter NEUT # IS not reliable for the ici index ("ici _ NEUT _ UNRELIABLE"). That is, although specific values have been determined for the hematological parameters, the plausibility check identifies that the measurements are not reliable for the purpose of the ICIS calculation, and therefore does not output an ICIS index value. As such, the error message or error code includes information indicative of at least one hematological parameter (here, with respect to the population of NEUTs) that caused the rationality check to fail. Note that in the output screen 110, even if the ici index value is not displayed, the measurement result of the hematological parameter NEUT # that causes a failure in the plausibility check is displayed. This is because such NEUT # is considered correct and reliable. That is, NEUT # is unreliable only for ICIS calculations because it is below the threshold defined in the plausibility check, but such NEUT # is correct, so NEUT # is displayed. The same applies to RET measurements. In the absence of RET #, RET-He and Delta-He become unreliable for ICIS calculations. However, since such RET # is correct, RET # is also displayed on the output screen.

The error message or error code may indicate a particular hematological parameter that caused the rationality check to fail. This enables the operator to grasp the reason why the ICIS calculation is not performed. For example, when an error message or error code indicates NE-SFL as the parameter that caused the plausibility check to fail, the operator may conclude that the sensitivity of the fluorescence detector may be poor, or that the staining reagent may be degraded, and therefore take appropriate action.

In other embodiments, the rationality check in step S33 of fig. 6 or step S33a of fig. 7 may also be performed for other situations:

for example, the plausibility check may comprise checking a haematological parameter reflecting the operating state of the detection device 2 measuring blood. Specifically, the hematological parameter NE-SFL reflects the sensitivity of detecting fluorescence from cells. As such, the hematological parameter may be indicative of an operational state of a sample preparation device in the fluorescence detector or detection apparatus 2 for staining cells with a fluorescent dye.

Alternatively or additionally, the plausibility check may comprise checking a parameter reflecting the quality of the mixed reagents. For example, the haematological parameter NE-SFL (which is a sensitivity parameter for detecting fluorescence activity in region 73 of figure 9) may also reflect the degree of staining of the cells. The parameter may also indicate whether the staining reagent is degenerated.

Alternatively or additionally, the rationality check may comprise checking for a specific haematological parameter whose value is correlated with obtaining a reliable value of another haematological parameter of the plurality of haematological parameters. For example, the present inventors have recognized that obtaining reliable NE-SFL may require a minimal population of NEUTs on a blood sample. Similarly, the present inventors have also recognized that obtaining reliable RET-He and Delta-He parameters may require a minimum RET population. This is because if the number of RET cells is below the threshold, there are insufficient RET cells present, with the inability to determine the reliable center of the RET population on the Y-axis, which results in unreliable RET-He, and consequently unreliable Delta-He. Such an inherent dependency or relationship between hematological parameters makes it possible to perform a rationality test on a reduced set of hematological parameters (such as the subset explained above), which makes the rationality test more time efficient.

Furthermore, it is possible to perform a rationality test not only on the actually obtained (measured) haematological parameters of the subject's blood, but also on the general case of the cytometer system 1 and the reagents used, and therefore the rationality test may comprise an additional general rationality test for the equipment used. Examples of such a general rationality test may include one or more of the following: message format errors (which indicate that the format of the message from the hematology analyzer (detection device 2) is invalid), air intake errors, error flags, functional errors, suspect sampling, missing tests, and/or unsupported parameter units (which indicate that the results from the hematology analyzer (detection device 2) contain units for blood tests that are not supported for calculation of the ICIS score).

For each of the above general plausibility checks, a corresponding single error code may be provided. For example, "ici _ Error _ Result" indicates a measure of Error by the hematology analyzer (detection device 2), and contains a resultant Error flag that does not allow calculation of an ici score.

Similarly, the error code "ici _ ACTION _ MESSAGE" from the hematology analyzer (detection device 2) may contain an ACTION MESSAGE that does not allow calculation of an ici score, such as "suspect sampling" or "check sampling".

In the above, an embodiment has been described in which the cytometer system 1 has a detection device 2, a first diagnostic support device 3 and a second diagnostic support device 3'. In such a configuration, the first diagnostic support apparatus may evaluate data of the forward scatter signals, the side scatter signals, and the fluorescence signals to create a corresponding scatter plot, and determine a set of hematological parameters for blood sampling of the subject. Subsequently, the second diagnosis support apparatus 3' obtains the plurality of hematological parameters, and performs a plausibility check as described above.

According to an alternative embodiment, the cytometer system 1 may include a detection device 2 and a single diagnostic support device 3. In such a configuration, the diagnosis support apparatus 3 may evaluate the data of the forward scattered light signals, the side scattered light signals, and the fluorescence signals to create a corresponding scattergram, and determine a set of hematological parameters for blood sampling of the subject to obtain the plurality of hematological parameters, and perform the plausibility check as described above.

Such an alternative embodiment may be used to implement a method according to the flowchart of fig. 13. Specifically, according to step S51 of fig. 13, the CPU31a of the diagnosis support apparatus 3 may receive data of the forward scattered light signal, the side scattered light signal, and the fluorescence signal output from the detector 5 of the detection apparatus 2 via the communication interface 31g and store the data in the memory 31 b. Subsequently, in step S52, the CPU31a creates a corresponding scatter chart, for example, at least: a RET scattergram having a Y-axis of the intensity of forward scattered light signals and an X-axis of the intensity of fluorescent signals, both signals being output by the detector 5; a DIFF scattergram having a Y-axis of the intensity of the fluorescent signal and an X-axis of the intensity of the side scatter signal, both signals being output by the detector 5; and a WBC/BASO scattergram having a Y-axis of intensity of forward scattered light signals and an X-axis of intensity of side scattered light signals, both of which are output by the detector 5.

Next, the CPU31a of the diagnosis support apparatus 3 obtains a plurality of hematological parameters for the blood of the subject by using the scatter diagram as a detection result. Examples of such hematological parameters are NEUT #, NE-SFL, RET #, HFLC #, IG #, EO #, Delta-He, RBC-He, PLT (as described above).

Next, the CPU31a of the diagnosis support apparatus 3 further executes the steps of: the rationality checks of steps S54 and S55 are performed, the ici index value is calculated (step S56), and diagnosis support information is output (step S57), or an error code or an error message is output (step S58). Steps S54-S58 correspond to steps S32-S36 of fig. 6. The skilled person realizes that all steps of fig. 13 are performed by a single diagnostic support apparatus 3, unlike the embodiment described in relation to fig. 6, in which the workload of determining the set of haematological parameters and the rationality check is shared between two diagnostic support apparatuses in the embodiment described in relation to fig. 6.

Alternative embodiments may also be used to implement the method according to the flowchart of fig. 14. In contrast to fig. 13, here, the plausibility checks of steps S54a and S55a are performed on a subset of the hematological parameters, rather than on the entire set of hematological parameters obtained from the scatter plot in step S53. As explained above, the entire set of hematological parameters obtained by a single diagnosis support device in step S53 may be as follows: NEUT #, NE-SFL, RET #, HFLC #, IG #, EO #, Delta-He, RBC-He, PLT. In this example, the subset of hematological parameters used for the plausibility check may correspond to the three parameters NEUT #, NE-SFL, RET #. Furthermore, in an alternative embodiment where a single diagnostic support device is applied, using only a subset of the hematological parameters for plausibility checks makes the reliability of the test performance more efficient.

Above, embodiments have been described in which the cytometer system 1 has a detection device 2, the detection device 2 comprising a detector 5, the detector 5 being configured to detect various types of specific information of the blood sample, such as forward scattered light and side scattered light from the cells and fluorescence after the cells have passed through the flow cell. The detection device 2 may additionally or alternatively comprise a different detector configured to measure the Direct Current (DC) impedance of cells of the blood sample individually passing through the cell interrogation zone. The detection device 2 may also or alternatively include a further distinct detector configured to measure Radio Frequency (RF) conductivity of cells of the blood sample that individually pass through the cell interrogation zone.

In the above, an example of preparing a WBC/BASO detection sample for a specified Basophil (BASO) by subjecting blood to a dilution process and further by subjecting to a lysis process using a dedicated reagent for lysing erythrocytes has been described. Alternatively, the detection sample for a given Basophil (BASO) may be prepared by subjecting blood to a dilution treatment, a lysis treatment, and further a staining treatment using a dedicated reagent for staining blood cells. In this case, a scattergram having a Y-axis of the intensity of the forward scattered light signal and an X-axis of the intensity of the fluorescent signal may be prepared to perform cell classification.

In the above, an example of analyzing both the DIFF scattergram and the WBC/BASO scattergram for classifying white blood cells into five groups of Monocytes (MONO), Lymphocytes (LYMPH), Neutrophils (NEUT), Basophils (BASO), and Eosinophils (EO) has been described. Alternatively, only DIFF scatter may be analyzed for classifying white blood cells into these five groups.

In the above, the embodiments have been described in which the ici index value is output on the output picture when the rationality check passes, and the ici index value is not output on the output picture when the rationality check fails. Alternatively, when the rationality check fails, the ici index value and a flag indicating that the ici index value is not reliable may be output in an output picture.

In the above, the embodiments have been described in which the ici index value is output as an indication of the probability of the infection reaction of the subject. Alternatively, a flag or message indicating the probability of an infection reaction of the subject may be output as the indication.

Above, an example has been described in which the hematological parameter NE-SFL is used as a parameter reflecting the quality of the reagent mixed with blood, because NE-SFL reflects the degree of staining of cells by the staining reagent. Alternatively or additionally, a hematological parameter reflecting the quality of the lysis reagent (lysis reagent) may be used. For example, a white blood cell count (such as a monocyte count or neutrophil count) may be used as a parameter that reflects the quality of the lysis reagent, as abnormally many monocytes or neutrophils will appear in the presence of too much debris, indicating incomplete lysis.