Method, device and system for testing blood

Abstract

The present disclosure relates to methods, apparatuses and systems for testing blood. A computer-implemented method for testing blood of a subject is provided, the method comprising: obtaining a plurality of hematological parameters for the blood; performing a plausibility check of the hematological parameters; and based on a result of the plausibility check, outputting an indication of a determined probability of an infection response of the subject based on the parameters.

Description

TECHNICAL FIELD

[0001] The present invention relates to a computer-implemented method for examining blood, a diagnostic support device, a blood cell counter system and a computer program product. BACKGROUND

[0002] Systemic inflammatory response syndrome (SIRS) is a state in which a subject is experiencing a severe inflammatory response throughout the body due to an infectious insult or a non-infectious insult.

[0003] An example of SIRS with an infectious inflammatory response is sepsis. Sepsis is a disease whose symptoms can progress to severe sepsis, septic shock and multiple organ dysfunction syndrome (MOD) if not properly treated at an early stage, eventually leading to death. Therefore, if a subject has been given a diagnosis of SIRS, an early determination of whether the inflammatory response is an infectious inflammatory response or a non-infectious inflammatory response, in particular an early diagnosis of whether the subject has a bacterial infection or a viral infection, has a great influence on the treatment to be given to the subject. Also, a determination of whether the inflammatory response is an infectious response or a non-infectious response can have a great influence on the treatment to be given to a subject who does not have SIRS. SUMMARY

[0004] TECHNICAL PROBLEM

[0005] EP 2 302 378 B1 describes a blood cell counter and a diagnostic support method by which diagnostic support information is determined based on several measured hematological parameters in the form of, for example, an index (ICIS: intensive care infection score) for supporting a determination of whether an 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 can quickly progress to life-threatening conditions such as sepsis if not immediately and properly treated. A medical professional can attempt to determine whether the inflammation is caused by a bacterial infection or a viral infection leading to sepsis or by some other cause leading to a SIRS diagnosis.

[0006] The method of EP 2 302 378 B1 determines an ICIS index based on the measured hematological parameters and outputs the ICIS 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 a possibility of outputting an inappropriate ICIS index value and a possibility of an unreliable assessment of the likelihood of an infectious response such as sepsis.

[0007] Against this background, it is an object of the present invention to overcome these limitations.

[0008] SOLUTION

[0009] According to a first aspect, a computer-implemented method comprises obtaining a plurality of hematological parameters for the blood; performing a plausibility check of at least one of the hematological parameters; and based on the result of the plausibility check, outputting an indication of a determined probability of an infection response of the subject based on at least one of the parameters.

[0010] According to a second aspect, a diagnostic support device comprises a controller configured to: obtain a plurality of hematological parameters for the blood; perform a plausibility check of at least one of the hematological parameters; and based on the result of the plausibility check, output an indication of a determined probability of an infection response of the subject based on at least one of the parameters.

[0011] According to a third aspect, a blood cell counter system comprises a blood detector device and a diagnostic support device according to embodiments of the present disclosure.

[0012] According to a fourth aspect, a computer program product comprises a computer readable medium storing instructions enabling a general purpose computer to perform the operations of a computer-implemented method according to embodiments of the present disclosure.

[0013] The solutions according to the first, second, third and fourth aspects guarantee that only valid, reliable and numerical parameter values are used for determining the probability of an infection response of a subject. BRIEF DESCRIPTION OF DRAWINGS

[0014] Figure 1 is a front view illustrating a schematic configuration of a blood cell counter system according to an embodiment.

[0015] Figure 2 is a block diagram illustrating a configuration of a detector device of a blood cell counter system according to an embodiment.

[0016] Figure 3 is a schematic plan view illustrating a configuration of a detector of a detector device according to an embodiment.

[0017] Figure 4 is a block diagram illustrating a configuration of a first diagnostic support device of a blood cell counter system according to an embodiment.

[0018] Figure 5 is a block diagram illustrating a configuration of a second diagnostic support device of a blood cell counter system according to an embodiment.

[0019] Figure 6 is a flowchart illustrating a processing sequence performed by a second diagnostic support device of a blood cell counter system according to an embodiment.

[0020] Figure 7is a flowchart showing a processing sequence executed by a second diagnosis support device of a blood cell counter system according to another embodiment.

[0021] Figure 8 is a RET scatter diagram created by a detection device of a blood cell counter system according to an embodiment.

[0022] Figure 9 is a DIFF scatter diagram created by a detection device of a blood cell counter system according to an embodiment.

[0023] Figure 10 is a WBC / BASO scatter diagram created by a detection device of a blood cell counter system according to an embodiment.

[0024] Figure 11 is an example of an output screen for showing measurement results of a blood sample.

[0025] Figure 12 is another example of an output screen for showing measurement results of a blood sample.

[0026] Figure 13 is a flowchart showing a processing sequence executed by a first diagnosis support device of a blood cell counter system according to another embodiment.

[0027] Figure 14 is a flowchart showing a processing sequence executed by a first diagnosis support device of a blood cell counter system according to another embodiment.

[0028] Figure 15A and Figure 15B is an example of a DIFF scatter diagram for explaining a reasonableness check.

[0029] Figure 16A and Figure 16B is an example of a RET scatter diagram for explaining a reasonableness check. DETAILED DESCRIPTION

[0030] Hereinafter, specific description will be given of a diagnosis support device, a diagnosis support method, and a computer program according to an embodiment of the present application with reference to the accompanying drawings. It will be understood that the following embodiment is not intended to limit the application defined in the claims, and not all combinations of features described in the embodiment are necessarily essential factors of means for solving the problem.

[0031] Figure 1 is a front view showing a schematic configuration of a blood cell counter system according to an embodiment of the present application. As Figure 1As shown, a blood cell counter system 1 according to an embodiment of the present application includes a blood testing device 2, a first diagnostic support device 3, and a second diagnostic support device 3'. The blood testing device 2 is a device that tests blood cells showing, for example, an inflammatory response in blood of a subject. The first diagnostic support device 3 and the second diagnostic support device 3' support determination of whether an inflammatory response is caused by infection. The first diagnostic support device 3 can be a computer that can be referred to as an information processing unit (IPU), and receives testing data from the blood testing device 2, and analyzes the testing data. The second diagnostic support device 3' can be a computer that can be referred to as a work area manager (WAM), and receives a measurement command for calculating an ICIS index value of a subject from an external apparatus (a laboratory information system (LIS)), and transmits the measurement command to the first diagnostic support device 3.

[0032] The first diagnostic support device 3 transmits a measurement command of a subject to the testing device 2, and receives data containing results of testing performed by the testing device 2, and performs analysis processing. The blood cell counter system 1 is installed in, for example, a medical institution such as a hospital or a hematology laboratory. The testing device 2 and the first diagnostic support device 3 can be connected via a transmission cable 3a so as to be able to perform data communication between them. Likewise, the first diagnostic support device 3 and the second diagnostic support device 3' can be connected via a transmission cable 3a' so as to be able to perform data communication between them. Note that the connection between the testing device 2 and the first diagnostic support device 3, and / or the connection between the first diagnostic support device 3 and the second diagnostic support device 3' is not limited to a direct wired connection formed by the transmission cables 3a and 3a', respectively. For example, the connection can be implemented via a wireless connection such as a dedicated line using a telephone line, a LAN, or a communication network such as the Internet.

[0033] In the lower right corner of the front of the testing device (or blood analyzer) 2, a blood collection tube setting portion 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 portion 2a, the blood collection tube setting portion 2a moves toward the operator, thereby enabling the operator to set a blood collection tube thereon. When the operator presses the push button switch 2b again after setting the blood collection tube, the blood collection tube setting portion 2a moves toward the testing device 2 to be housed in the testing device 2. However, the testing device 2 is not limited to manual setting of the blood collection tube. Alternatively, the blood collection tubes can be collected in a sampling rack, and moved to the testing device 2 by a conveyer belt or a conveyer conveyor, in which the respective blood collection tubes are set to the testing device 2.

[0034] Figure 2is a block diagram showing a configuration of a detection apparatus 2 of a blood cell counter system 1 according to an embodiment of the present application. Referring to Figure 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, which 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 blood of a 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 operations of components of the detection apparatus 2. The communication section 9 can 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.

[0035] Figure 3 is a schematic plan view schematically showing a configuration of the detector 5 of the blood cell counter system 1 according to the embodiment of the present application. Referring to Figure 3 , the detector 5 is an optical flow cytometer, and detects white blood cells (WBC), reticular cells (RET), mature red blood cells (RBC), and platelets (PLT) in blood by a flow cytometer using a semiconductor laser. The term "red blood cells" is used herein as explicitly including "reticular cells (RET)" and "mature red blood cells (RBC)". The detector 5 includes a flow cell 51 for forming a fluid flow of a detection sample. The flow cell 51 is formed of a translucent material such as quartz, glass, synthetic resin, or the like, and has a tubular shape. The flow cell 51 has a flow path through which a detection sample and a sheath fluid flow. The detector 5 includes a semiconductor laser light source 52 disposed to output laser light toward the flow cell 51. Between the semiconductor laser light source 52 and the flow cell 51, an illumination lens system 53 including a plurality of lenses is provided. The illumination lens system 53 collects a parallel beam output from the semiconductor laser light source 52 to form a beam spot. An optical axis extends in line from the semiconductor laser light source 52 through the flow cell 51. A photodiode 54 is disposed on the optical axis so that the photodiode 54 is positioned on an opposite side of the flow cell 51 from the illumination lens system 53. A beam stopper 54a is provided so as to block light directly from the semiconductor laser light source 52.

[0036] When the detection sample stream is detected into the flow cell 51, scattered light and fluorescence are generated based on the laser light. In the scattered light and the fluorescence, light of the laser light in the irradiation (i.e., forward) direction is photoelectrically converted by the photodiode 54. In the light traveling along the optical axis extending in line from the semiconductor laser light source 52, light directly from the semiconductor laser light source 52 is blocked by the beam stopper 54a. Only scattered light (hereinafter referred to as forward scattered light) traveling substantially in the direction of the optical axis is incident on the photodiode 54. The 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 a 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 indicates the size of the blood cell.

[0037] A side condenser lens 55 is provided at the side of the flow cell 51 so as to be disposed in a direction intersecting the optical axis on which the optical axis extending in line from the semiconductor laser light source 52 to the photodiode 54 intersects. The side condenser lens 55 condenses side light (i.e., light output in a direction on which the optical axis intersecting the optical axis extending in line from the semiconductor laser light source 52 to the photodiode 54 intersects) that occurs when the laser light is emitted to the detection sample passing through the flow cell 51. A dichroic mirror 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 fluorescence component by the dichroic mirror 56. In the direction of the optical axis on which the light advancing by the side condenser lens 55 and the dichroic mirror 56 intersects the optical axis (i.e., the direction on which the optical axis intersecting the optical axis passing through the side condenser lens 55 and the dichroic mirror 56 intersects), a photodiode 57 for receiving side scattered light is provided. In the optical axis passing through the side condenser lens 55 and the dichroic mirror 56, a photodiode 58 for receiving fluorescence and a filter 58a are provided.

[0038] The light reflected by the dichroic mirror 56 is side scattered light, and is photoelectrically converted into an electric signal by the photodiode 57. Each photoelectrically converted electric signal (hereinafter referred to as a side scattered light signal) is amplified by the amplifier 57a, and then output to the controller 8. Each side scattered light signal indicates internal information of the blood cell (the size of the cell nucleus, etc.). The light (which is fluorescence) transmitted through the dichroic mirror 56 is wavelength-selected by the filter 58a, and then photoelectrically converted into an electric signal by the photodiode 58. Each photoelectrically converted electric signal (hereinafter referred to as a fluorescence signal) is amplified by the amplifier 58b, and then output to the controller 8. Each fluorescence signal indicates the degree of staining of the blood cell.

[0039] Figure 4 is a block diagram showing the configuration of the first diagnosis support device 3 of the blood cell counter system 1 according to the embodiment of the present application. As Figure 4As shown, the first diagnosis support device 3 includes at least a data processing section (controller) 31 including a CPU (Central Processing Unit) or the like, an image display section 32, and an input section 33. The data processing section 31 includes a CPU 31a, a memory 31b, a hard disk 31c, a reading device 31d, an input / output interface 31e, an image output interface 31f, a communication interface 31g, and an internal bus 31h. In the data processing section 31, the CPU 31a is connected to each of the memory 31b, the hard disk 31c, the reading device 31d, the input / output interface 31e, the image output interface 31f, and the communication interface 31g via the internal bus 31h.

[0040] The CPU 31a controls the operation of each of the above hardware components and processes data received from the detection device 2 according to a computer program 34 stored in the hard disk 31c.

[0041] The memory 31b is configured as a volatile memory such as an SRAM or a flash memory. A load module is loaded to the memory 31b at the time of execution of the computer program 34. The memory 31b stores temporary data or the like generated at the time of execution of the computer program 34.

[0042] The hard disk 31c is configured as a fixed storage device or the like and is incorporated in the first diagnosis support device 3. The computer program 34 is downloaded from a portable storage medium 35 (such as a DVD, a CD-ROM, a USB flash drive, or the like) storing information such as a program, data, or the like by the reading device 31d as a portable disk drive. The computer program 34 is then stored in the hard disk 31c. The computer program 34 is loaded from the hard disk 31c to the memory 31b so as to be executed. It will be appreciated that the computer program 34 can be a computer program downloaded from an external computer via the communication interface 31g.

[0043] The input / output interface 31e is connected to the input section 33 configured as a keyboard, a tablet, or the like. The image output interface 31f is connected to the image display section 32, which can be a CRT monitor, an LCD, or the like. Alternatively, the input section 33 and the image display section 32 can be included in a single device such as a touch-based monitor.

[0044] The communication interface 31g is connected to the internal bus 31h and performs data transmission / reception with an external computer such as a second diagnosis support device 3' (described below), the detection device 2, or 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 device 3 but can be an external storage medium such as an external storage connected to the first diagnosis support device 3 via the communication interface 31g or the like.

[0045] Figure 5 is a block diagram showing a configuration of a second diagnostic support device 3' of the blood cell counter system 1 according to the embodiment of the present application. The second diagnostic support device 3' can have the same hardware configuration as the first diagnostic support device 3 described above, and detailed explanation is thus omitted. As shown in Figure 5 the communication interface 31g' performs data transmission / reception with the first diagnostic support device 3. In addition, the hard disk 31c' can store a message indicating the ICIS index. The message can further include an indication that the subject has or can have an infectious reaction, and a message indicating that the subject has or can have a non-infectious reaction, as diagnostic support information for supporting output of an indication of a probability of determination of an infectious reaction (ICIS index) of the subject. The hard disk 31c' can also store a scoring threshold (described below) for one or more of a plurality of hematological parameters for scoring the plurality of hematological parameters to determine whether a reaction of the subject is an infectious reaction or a non-infectious reaction, and a threshold for a plausibility check (described below).

[0046] Hereinafter, a description of the operation of the blood cell counter system 1 according to the embodiment of the present application is given. First, the sampling feeder 4 of the detection device 2 aspirates blood from a blood collection tube provided in the blood collection tube setting section 2a, divides the aspirated 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 the RET detection sample is prepared by subjecting blood to a dilution process, and further to a staining process using a dedicated reagent for detecting reticular cells. The DIFF detection sample is prepared by subjecting blood to a dilution process, to a lysis process using a dedicated reagent for lysing red blood cells, and further to a staining process using a dedicated reagent for classifying white blood cells into a plurality of subgroups. The WBC / BASO detection sample is prepared by subjecting blood to a dilution process, and further to a lysis process using a dedicated reagent for lysing red blood cells. The sampling feeder 4 feeds the prepared detection samples to the flow cell 51 of the detector 5.

[0047] Figure 6 is a flowchart showing a processing sequence executed by the CPU 31a' of the data processing section 31' of the second diagnostic support device 3' of the blood cell counter system 1 according to the embodiment of the present application. As shown in Figure 6 the CPU 31a' first obtains a plurality of hematological parameters, for example, NEUT#, NE-SFL, RET#, HFLC#, IG#, EO#, Delta-He, RBC-He, and PLT of blood of the subject (S31). This can be achieved as follows:

[0048] When the detection sample is sent to the flow cell 51, the CPU 31a of the first diagnostic support apparatus 3 can receive data of the forward scattered light signal, the side scattered light signal, and the fluorescence signal outputted by the detector 5 of the detection apparatus 2 via the communication interface 31g, and store the data in the memory 31b. The CPU 31a creates a plurality of scatter plots 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 31b. The plurality of scatter plots created by the CPU 31a includes, for example, at least: a RET scatter plot (see Figure 8 ) having a Y axis of intensity of the forward scattered light signal and an X axis of intensity of the fluorescence signal, both of which are outputted by the detector 5; a DIFF scatter plot (see Figure 9 ) having a Y axis of intensity of the fluorescence signal and an X axis of intensity of the side scattered light signal, both of which are outputted by the detector 5; and a WBC / BASO scatter plot (see Figure 10 ) having a Y axis of intensity of the forward scattered light signal and an X axis of intensity of the side scattered light signal, both of which are outputted by the detector 5. Alternatively, the X axis and the Y axis of each scatter plot can be reversed.

[0049] Next, the CPU 31a of the first diagnostic support apparatus 3 determines a plurality of hematological parameters of the blood of the subject by using the scatter plots as detection results. Examples of such hematological parameters are NEUT#, UE-SFL, RET#, HFLC#, IG#, EO#, Delta-He, RBC-He, and PLT (to be described in detail below).

[0050] Figure 8 A RET scatter plot created by the blood cell counting system 1 according to the embodiment of the present application is shown. Based on the RET scatter plot, the CPU 31a of the first diagnostic support apparatus 3 calculates a reticulocyte hemoglobin equivalent (RET-He), a difference between the reticulocyte hemoglobin equivalent (RET-He) and a hemoglobin equivalent of mature red blood cells (RBC-He), both of which are hematological parameters obtained as follows. As Figure 8As shown, the CPU 31a of the first diagnostic support device 3 identifies three regions: a mature red blood cell (RBC) region 60, a platelet (PLT) region 61, and a reticulocyte (RET) region 62 by using the RET scatter plot. Based on the RET scatter plot, the CPU 31a determines RBC-He, which is the forward scatter 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 scatter light intensity of the median value of all cells contained in the reticulocyte region 62 (RET). The CPU 31a of the first diagnostic support device 3 calculates a reticulocyte count (RET#) from the reticulocyte region 62 as one of the plurality of hematology parameters. The CPU 31a of the first diagnostic support device 3 further calculates a platelet count (PLT) from the platelet region 61 as one of the plurality of hematology parameters. An example of such a calculation is shown in EP 2 302 378 B1.

[0051] Next, the CPU 31a of the first diagnostic support device 3 calculates a neutrophil count (NEUT#) from the DIFF scatter plot and the WBC / BASO scatter plot as one of the plurality of hematology parameters. Note that the term "granulocyte" is used herein as explicitly including both "mature granulocytes" and "immature granulocytes". "Mature granulocytes" explicitly include neutrophils (NEUT), eosinophils (EO), and basophils (BASO).

[0052] Figure 9 A DIFF scatter plot created in a leukocyte differential measurement by the blood cell counting system 1 according to an embodiment of the present application is shown. Figure 10 A WBC / BASO scatter plot created by the blood cell counting system 1 according to an embodiment of the present application is shown. As Figure 9 As shown, the DIFF scatter plot classifies blood cells into six 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 fluorescent lymphocyte (HFLC) region 76. The number of neutrophils (NEUT) and the sum of the number of basophils (BASO) can be calculated by counting the number of leukocytes in the neutrophil (NEUT) + basophil (BASO) region 73 based on the DIFF scatter plot. Here, the CPU 31a of the first diagnostic support device 3 further calculates an eosinophil count (EO#) from the eosinophil region 74 as one of the plurality of hematology parameters.

[0053] In order to be based on Figure 9 Region 73 calculates the neutrophil count (NEUT#) as one of the multiple hematological parameters from the sum of the number of neutrophils (NEUT) and the number of basophils (BASO). The CPU 31a of the first diagnostic support device 3 determines the number of basophils (BASO) by using a WBC / BASO scatter plot. Figure 10 As shown, the WBC / BASO scatter plot classifies white blood cells into two regions: region 81, which is composed of monocytes (MONO) + lymphocytes (LYMPH) + neutrophils (NEUT) + eosinophils (EO), and region 82, which is composed of basophils (BASO). Therefore, the number of basophils (BASO) in region 82 can be calculated by counting the number of white blood cells in region 82 based on the WBC / BASO scatter plot. The neutrophil count (NEUT#), one of the multiple hematological parameters, can then be obtained by subtracting the number of basophils (BASO) calculated based on the WBC / BASO scatter plot from the sum of the number of neutrophils (NEUT) and the number of basophils (BASO) calculated based on the DIFF scatter plot.

[0054] Next, the CPU 31a of the first diagnostic support device 3 calculates a value (NE-SFL) indicating the fluorescence intensity of neutrophils in the blood using a DIFF scatter plot. This value is one of the plurality of hematological parameters. Specifically, it can be obtained by calculating the median fluorescence intensity of all cells (i.e., neutrophils (NEUT) and basophils (BASO)) contained in the neutrophil (NEUT) + basophil (BASO) region 73 based on the DIFF scatter plot. Figure 9 The value obtained (NE-SFL) includes the effect of fluorescence intensity of basophils (BASO), but the number of basophils (BASO) is small, so the effect is minimal.

[0055] Next, the CPU 31a of the first diagnostic support device 3 calculates the immature granulocyte count (IG#) using a DIFF scatter plot. IG# is one of the plurality of hematological parameters relating to granulocytes in the 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 a DIFF scatter plot.

[0056] Next, the CPU 31a of the first diagnostic support device 3 calculates a high fluorescent lymphocyte count (HFLC#) on lymphocytes that have synthesized antibodies by using the DIFF scatter plot, the HFLC# being one of the plurality of hematology parameters of the blood. Specifically, the high fluorescent lymphocyte count (HFLC#) can be obtained by counting the number of cells in the high fluorescent lymphocyte (HFLC) region 76 based on the DIFF scatter plot.

[0057] The skilled person understands that the above example of obtaining a plurality of hematology parameters is non-limiting and that other scatter plots and other hematology parameters can be determined.

[0058] Referring back to Figure 6 In step S31, the second diagnostic support device 3’ obtains the plurality of hematology parameters, e.g. NEUT#, NE-SFL, RET#, HFLC#, IG#, EO#, Delta-He, RBC-He, PLT, from the first diagnostic support device 3 by receiving the determined hematology parameters (as described above) via the communication interface 31g’.

[0059] Then, according to Figure 6 Step S32 of the second diagnostic support device 3’, the CPU 31a’ of the second diagnostic support device 3’ performs a plausibility check on at least one of the hematology parameters obtained in step 31.

[0060] Specifically, the plausibility check can be performed on hematology parameters obtained from the leukocyte differential measurements, such as NE-SFL and NEUT#. In addition, the plausibility check can be performed on hematology parameters for the leukocyte count, such as NEUT#, and / or the red blood cell count, such as RET#.

[0061] The plausibility check can be performed by comparing at least one of the hematology parameters to a threshold value or a lower threshold value and an upper threshold value. That is, the plausibility check can be performed by comparing the value of each of the hematology parameters for the plausibility check to the corresponding threshold value or the corresponding lower threshold value and upper threshold value. The threshold value(s) are predetermined and set to guarantee that the hematology parameters have a reasonable or reliable value that meets the quality requirements with respect to the ICIS index value. In other words, the threshold value(s) are set to exclude seemingly implausible values that would lead to a false determination of the ICIS index value.

[0062] Next, in step S33, the CPU 31a’ of the second diagnostic support device 3’ determines whether the plausibility check on at least one of the hematology parameters is passed, i.e. whether the obtained at least one of the hematology parameters is above a predetermined threshold value or within a certain parameter range defined as a lower threshold value and an upper threshold value.

[0063] If the plausibility check is passed (YES in step S33), the CPU 31a' of the second diagnosis support device 3' proceeds to step S34 to determine an ICIS index value, which indicates a probability of having an infectious reaction, in particular a likelihood of having a bacterial infection (to be described in more detail below). On the other hand, if the plausibility check is not passed (NO in step S33), the CPU 31a' proceeds to step S36 and outputs a corresponding error code or error message (to be described in more detail below), while the determined indication of probability (ICIS index) is not output. In addition, if the plausibility check is not passed (NO in step S33), the CPU 31a' can cause the measurement results of at least one of the hematological parameters that caused the plausibility check to fail to be output (see below Figure 12 ).

[0064] When the plausibility check is passed, the CPU 31a' determines an ICIS index value based on the obtained hematological parameters (step S34), which is then output as output diagnosis support information in step S35, said ICIS index value being related to a probability of having an infectious reaction, in particular a likelihood of having a bacterial infection.

[0065] In particular, with regard to the calculation of the ICIS index value, the CPU 31a' can set ICIS points with regard to the hematological parameters (Delta-He, RBC-He, NEUT#, EO#, PLT, NE-SFL, IG#, HFLC#) obtained in step S31 and calculate the ICIS index value by totaling the set ICIS points. This can be achieved by implementing parameter-specific ICIS rules as follows:

[0066] a) ICIS rule with regard to HFLC#

[0067] Here, the CPU 31a' determines an ICIS point based on the following rule with regard to the hematological parameter HFLC#:

[0068] Rule 1 ICIS Point HFLC # < M1 / μL 0 M1 / μL < HFLC # < M2 / μL 1 M2 / μL < HFLC # < M3 / μL 2 HFLC # >= M3 / μL 4

[0069] The first ICIS rule applies specific score thresholds M1, M2 and M3. The skilled person understands that the actual numerical values of the thresholds M1, M2 and M3 depend on the configuration and accuracy of the blood sampling, the detection device 2, etc. As such, depending on the determined high fluorescent lymphocyte count HFLC#, a corresponding first ICIS point 0, 1, 2 or 4 is set.

[0070] b) ICIS rule with regard to IG# and EO#

[0071] Here, the CPU 31a' determines the ICIS points based on the following rules for the hematological parameters IG# and EO#:

[0072] Rule 2 ICIS Point IG # < M4 / μL 0 M4 / μL < IG # < M5 / μL 1 M5 / μL < IG # < M6 / μL and EO # < M7 / μL 2 IG # >= M6 / μL and EO # < M7 / μL 4 IG # >= M5 / μL and EO # >= M7 / μL 0

[0073] The second ICIS rule applies specific score thresholds M4, M5, M6 and M7. The skilled person understands that the actual numerical values of the thresholds M4, M5, M6 and M7 depend on the blood sampling, the configuration and accuracy of the detection device 2, etc. As such, depending on the determined granulocyte count IG# and the determined eosinophil count EO#, a corresponding second ICIS point 0, 1, 2 or 4 is set.

[0074] c) ICIS rules for NE.SFL

[0075] Here, the CPU 31a' determines the ICIS points based on the following rules for the hematological parameter NE.SFL:

[0076] Rule 3 ICIS Point NE-SFL < M8ch 0 M8ch < NE-SFL < M9ch 1 M9ch < NE-SFL < M10ch 2 NE-SFL >= M10ch 4

[0077] The third ICIS rule applies specific score thresholds M8, M9 and M10. The skilled person understands that the actual numerical values of the thresholds M8, M9 and M10 depend on the blood sampling, the configuration and accuracy of the detection device 2, etc. As such, depending on the determined intermediate value of the fluorescence value NE.SFL, a corresponding third ICIS point 0, 1, 2 or 4 is set.

[0078] d) ICIS rules for Delta-He and RBC-He

[0079] Here, the CPU 31a' determines the ICIS points based on the following rules for the hematological parameters Delta-He and RBC-He:

[0080] Rule 4 ICIS Point Delta-He >= M11 pg 0 M12 pg < Delta-He < M11 pg and RBC-He >= M13 pg 1 M14 pg < Delta-He < M12 pg and RBC-He >= M13 pg 2 Delta-He < M14 pg and RBC-He >= M13 pg 4 Delta-He < M11 pg and RBC-He < M13 pg 0

[0081] The fourth ICIS rule applies specific score thresholds M11, M12, M13 and M14. The skilled person understands that the actual numerical values of the thresholds M11, M12, M13 and M14 depend on the blood sampling, the configuration and accuracy of the detection device 2, etc. As such, depending on the determined Delta-He and RBC-He values, a corresponding fourth ICIS point 0, 1, 2 or 4 is set.

[0082] e) ICIS rules for NEUT#, EO# and PLT

[0083] Here, the CPU 31a’ determines the ICIS points based on the following rules for the hematological parameters NEUT#, EO# and PLT. With respect to the ICIS index, the total neutrophil count can be understood as the sum of the IG# value and the NEUT# value (“immature granulocytes and mature granulocytes”), referred to as NEUT# in the following.

[0084] Rule 5 ICIS Point M15 / μL < NEUT # < M16 / μL 0 M16 / μL < NEUT # < M17 / μL 1 NEUT # >= M17 / μL and EO # < M18 / μL and NEUT # < M19 / μL 2 NEUT # < M15 / μL 2 NEUT # >= M19 / μL and PLT < M20 / μL and EO # < M18 / μL 4 NEUT # >= M17 / μL and EO # >= M18 / μL and NEUT # < M19 / μL 0 NEUT # >= M19 / μL and PLT >= M20 / μL and EO # < M18 / μL 0 NEUT # >= M19 / μL and PLT < M20 / μL and EO # >= M18 / μL 0 NEUT # >= M19 / μL and PLT >= M20 / μL and EO # >= M18 / μL 0

[0085] The fifth ICIS rule applies specific scoring thresholds M15, M16, M17, M18, M19 and M20. The skilled person understands that the actual numerical values of the thresholds M15, M16, M17, M18, M19 and M20 depend on the configuration and accuracy of the blood sampling, the detection device 2, etc. As such, depending on the determined NEUT#, EO# and PLT values, the corresponding fifth ICIS points 0, 1, 2 or 4 are set.

[0086] As explained, in order to calculate the ICIS index value in step S34, respective scoring thresholds for the hematological parameters are set by applying a plurality of ICIS rules in which the hematological parameters obtained in step S31 are compared to respective scoring thresholds pre-stored in the hard disk 31c’. While EP 2 302 378 further provides details on how such scoring thresholds can be determined (e.g. based on ROC (Receiver Operating Characteristic) analysis which uses ROC curves to determine scoring thresholds), the skilled person understands that other methods can equally be applied to the determination of the above scoring thresholds.

[0087] Thus, the CPU 31a’ can calculate the ICIS index value by summing the respective ICIS points determined according to the ICIS rules as explained above. The minimum score is 0 points, which indicates that an inflammatory response is very unlikely to be caused by an infection. The maximum score is 20 points. The skilled person understands that the closer the score is to 20 points, the more likely it is that the inflammatory response is caused by an infection, such as a bacterial infection. 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 can be performed based on more or less hematological parameters or other ICIS rules.

[0088] According to a further embodiment, the CPU 31a’ of the second diagnostic support device 3’ can determine the type of the infectious response, e.g. whether the inflammatory response of the subject is an infectious response or a non-infectious response, e.g. whether it is an infectious inflammatory response or a non-infectious inflammatory response, or whether it is a bacterial infectious response or a non-bacterial infectious response, based on the index (ICIS) calculated in step S34. The outputted diagnostic support information can then comprise both the ICIS index and the type of the infectious response.

[0089] In a further embodiment, the CPU 31a' can determine, based on the index (ICIS), whether the reaction of the subject is an infectious reaction or a non-infectious inflammatory reaction. Specifically, if the index (ICIS) is not less than a determination threshold value stored in the hard disk 31c, the CPU 31a' can determine that the reaction of the subject is, or can be, an infectious reaction; if the index (ICIS) is less than the determination threshold value, it is determined that the reaction of the subject is, or can be, a non-infectious reaction.

[0090] Now returning to the description of the output processing of the diagnostic support information performed by the CPU 31a' of the second diagnostic support device 3' (see Figure 6 ). If the plausibility check in step S33 is passed, the CPU 31a' outputs the diagnostic support information to the image display section 32' via the image output interface 31f' and to another computer, printer, etc. via the communication interface 31g' (step S35). Specifically, the diagnostic support information includes the specific ICIS index value calculated in step S34 and can further include the determined type of infectious reaction.

[0091] Further, if the plausibility check is not passed, the CPU 31a' of the second diagnostic support device 3' reads an error code or error message from the hard disk 31c', outputs the error code or error message to the image display section 32' via the image output interface 31f' and to another computer, printer, etc. via the communication interface 31g' (step S36).

[0092] Although the above embodiments have been described with respect to the examples of the hematological parameters NEUT#, NE-SFL, RET#, HFLC#, IG#, EO#, Delta-He, RBC-He, PLT, the skilled person understands that the ICIS index value can be determined based on more or less hematological parameters, in particular, not all of the above hematological parameters must be used for the ICIS index value. For example, in the above embodiments, although RET# is used for the plausibility check, it can not be required for determining the ICIS index value.

[0093] Figure 7 is a flowchart showing a processing sequence performed by the CPU 31a' of the data processing section 31' of the second diagnostic support device 3' of the blood cell counter system 1 according to another embodiment of the present application. Here, unlike the processing sequence in Figure 6 , in step S32a, the plausibility check is performed only on a subset of the hematological parameters obtained in step S31. For example, if, as above with respect to Figure 6As explained above, in step S31 a set of 9 hematology parameters such as NEUT#, NE-SFL, RET#, HFLC#, IG#, EO#, Delta-He, RBC-He, PLT is obtained, then it is not necessary to perform the plausibility check S32a on the whole set of hematology parameters, but it is possible to perform the plausibility check S32a only on a limited subset, for example on the three parameters NEUT#, NE-SFL, RET#. The skilled person understands that this improves the efficiency of performing the plausibility check on the ICIS index of the blood sample, because the plausibility check is not necessary for all the obtained hematology parameters.

[0094] More specifically, according to Figure 7 the step S32a, the CPU 31a' of the data processing part 31' of the second diagnosis support device 3' obtains a subset of hematology parameters and performs the plausibility check on this subset. As explained above, when a plurality of hematology parameters such as NEUT#, NE-SFL, RET#, HFLC#, IG#, EO#, Delta-He, RBC-He, PLT are obtained, the subset of hematology parameters refers to a limited number of hematology parameters. In the present non-limiting example, this subset can correspond to the three parameters NEUT#, NE-SFL, RET#.

[0095] Here, the plausibility is preferably necessary to pass for the whole subset of hematology parameters in order to output the diagnosis support information (ICIS index). In other words, if the plausibility check is successful (passed) only for one or two hematology parameters of the whole subset of hematology parameters, for example, but is failed for at least another hematology parameter of the whole subset, then the plausibility check of step 33a is failed (not passed).

[0096] The underlying reason for the possibility of performing the plausibility check only on a limited subset of the obtained hematology parameters is based on the following observation of the inventor, that when determining the plausibility, there is a relationship between the hematology parameters. Specifically, when considering the scatter plot created, it has been found that, for example, a plausible value for NEUT# has a relationship with determining that other hematology parameters are plausible as well, and vice versa.

[0097] Figure 15A and Figure 15B An example of this relationship is shown. Specifically, according to the scatter plot in Figure 15A , it is easy to determine the intermediate value of NE-SFL (see above Figure 9 ), because it has a clearly defined population that can determine this intermediate value. However, Figure 15BThis indicates a very low count, and the center of the neutrophil population may not be the actual center, and the coefficient of variation between points in the scatter plot is large, making NE-SFL unreliable in the case of very low NEUT#.

[0098] Technicians understand that the justification for NE-SFL failure affects other hematological parameters derived from the scatter plot. For example, the reliability of IG# classification or HFLC# (and...). Figure 9 In contrast, fluorescence can generally be detected via NE-SFL, which is a sensitivity parameter and reflects fluorescence measurement.

[0099] The reliability of Delta-He or RBC-He can be checked via RET#, as these parameters are obtained from the same RET scatter plot, and RET# influences the calculation of Delta-He and RBC-He. If the number of RET cells is below a threshold, there are not enough cells present, and a reliable center of the RET population on the Y-axis may not be possible, leading to unreliable RET-He, and consequently, unreliable Delta-He. Figure 16A and Figure 16B An example of this relationship is shown here, in a scatter plot for RET. Figure 16A Examples with clearly defined RET groups are shown. However, Figure 16B This illustrates an example where RET# is (almost) nonexistent, rendering RET-He unreliable. This unreliability is also related to the hematological parameter Delta-He (RET-He-RBC-He), meaning that if RET-He is unreliable, Delta-He is also considered unreliable and therefore not considered for ICIS index calculation.

[0100] exist Figure 7 In step S32a, each parameter of the subset can be compared with a corresponding threshold or a lower and upper threshold. In the above non-limiting example, CPU 31a' is configured (specifically programmed) to check whether the RET# value is higher than a threshold, such as a value reflecting the minimum RET population to obtain a reliable center of the RET population, thereby obtaining the RET-He value. The threshold may be 0.004 10 6 / μL, 0.005 10 6 / μL or 0.006 10 6 / μL, etc. Technicians understand that such thresholds can vary for different sample sizes or different analyzer settings.

[0101] If the RET# value is higher than the threshold, then the plausibility check for RET# passes (i.e. RET# has a reasonable value). Otherwise, if the RET# value is equal to or lower than the threshold, then the CPU 31a’ creates a parameter-specific message (“ICIS_RET_UNRELIABLE”) as a code or flag indicating that the plausibility check for RET has failed, which code or flag does not allow to compute the ICIS score.

[0102] Further, the CPU 31a’ is configured (specifically programmed) to check whether the NE-SFL has a fluorescence intensity between a lower threshold and an upper threshold. If the fluorescence intensity is in the range defined between the lower threshold and the upper threshold, then the plausibility check for NE-SFL passes (i.e. NE-SFL has a reasonable value). Otherwise, if the NE-SFL value is equal to or lower than the lower threshold, or equal to or greater than the upper threshold, then the CPU 31a’ creates a parameter-specific message (“ICIS_NE_SFL_UNRELIABLE”) as a code or flag indicating that the plausibility check for NE-SFL has failed, which code or flag does not allow to compute the ICIS score.

[0103] Further, the CPU 31a’ is configured (specifically programmed) to check whether the NEUT# value is higher than a threshold. Such a threshold reflects the minimum NEUT population required to obtain a reliable NE-SFL (fluorescence intensity in the NEUT area of the corresponding scatter plot). If the NEUT# value is higher than the threshold, then the plausibility check for NEUT# passes (i.e. NEUT# has a reasonable value). Otherwise, if the NEUT# value is equal to or lower than the threshold, then the CPU 31a’ creates a parameter-specific message (“ICIS_NEUT_UNRELIABLE”) as a code or flag indicating that the plausibility check for NEUT# has failed, which code or flag does not allow to compute the ICIS score. The threshold can be 0.4 10 3 / μL, 0.5 10 3 / μL or 0.6 10 3 / μL, etc. The skilled person understands that such a threshold can be different for different sampling sizes or different analyzer settings.

[0104] Based on the plausibility checks, the CPU 31a’ determines in step S33a whether the plausibility checks have passed for all parameters of the subset (i.e. for all three hematology parameters NEUT#, NE-SFL, RET# of the above example).

[0105] If the plausibility checks have passed for all parameters of the subset (YES in step S33a), then the CPU 31a’ proceeds with the ICIS index computation in step S34 (as described above). Figure 6and the remaining steps as explained above in Figure 7

[0106] Otherwise, if the plausibility check does not pass for one or more parameters of the subset (NO in step S33a), the CPU 31a' proceeds with step S36 and outputs an error code (flag) or an output message, e.g. ICIS_NEUT_UNRELIABLE, ICIS_NE_SFL_UNRELIABLE, ICIS_RET_UNRELIABLE identifying which parameter of the subset failed the plausibility check as described above in Figure 7

[0107] Figure 11 An example of an output screen 100 is shown, the output screen 100 for showing the measurement results of blood samples according to the measurement commands for calculating the ICIS index value, wherein the determined hematology parameters are indicated. Here, in the output screen 100, for example, with respect to NEUT#, LYMPH#, MONO# etc. the specific determined values of the hematology parameters of the blood samples with specific sampling IDs, priority, collection date etc. are shown. Further, the ICIS index value is shown with a highlighted box 101, which has a value of 16 for the current blood sample. From this value the skilled person understands that the inflammatory response of the subject is very likely an infectious response. When the ICIS index value is outputted due to the fact that the plausibility check has passed, a technically reliable ICIS index value can be provided.

[0108] Figure 12 Another example of an output screen 110 is shown, the output screen 110 for showing the measurement results of blood samples according to the measurement commands for calculating the ICIS index value, wherein the determined hematology parameters are indicated, e.g. the example NEUT# = 0.3110 3 / μL. Since the parameter for NEUT# has been obtained as 0.3110 3 / μL and this value is below the corresponding threshold value (as indicated above), the plausibility check fails and no ICIS index value is determined (in Figure 12 ​​The highlighted box 111 indicates "not measurable," and indicates that the parameter NEUT# is unreliable for the ICIS index ("ICIS_NEUT_UNRELIABLE"). That is, although specific values ​​have been determined for the hematological parameters, the rationale check still identifies the measurement as unreliable for the purpose of ICIS calculation, and therefore does not output the ICIS index value. Accordingly, the error message or error code includes information indicating at least one hematological parameter (here, regarding the NEUT population) that caused the rationale check to fail. Note that in output screen 110, even though the ICIS index value is not displayed, the measurement result of the hematological parameter NEUT# that caused the rationale check to fail is also displayed. This is because such a NEUT# is considered correct and reliable. That is, NEUT# is only unreliable for ICIS calculation because it is below the threshold defined in the rationale check, but such a 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 calculation. However, this RET# is correct, so RET# is also displayed in the output screen.

[0109] Error messages or error codes can indicate a specific hematological parameter that caused the rationale check to fail. This allows the operator to determine why the ICIS calculation did not perform. For example, when an error message or error code indicates NE-SFL as the parameter causing the rationale check to fail, the operator can infer that the fluorescence detector sensitivity may be poor or the staining reagent may be degraded, and thus take appropriate action.

[0110] In other embodiments, other situations may also be performed. Figure 6 Step S33 or Figure 7 Reasonableness check in step S33a:

[0111] For example, a rationality check may include examining hematological parameters that reflect the operational status of the detection device 2 for measuring blood. Specifically, the hematological parameter NE-SFL reflects the sensitivity to detecting fluorescence from cells. Accordingly, this hematological parameter can indicate the operational status of the fluorescence detector or the sampling preparation apparatus in the detection device 2 used to stain cells with fluorescent dyes.

[0112] Alternatively or additionally, the rationale check may include examining parameters that reflect the quality of the mixed reagents. For example, the hematological parameter NE-SFL (which is used to detect...) Figure 9 The sensitivity parameter of fluorescence activity in region 73 can also reflect the degree of cell staining. This parameter can also indicate whether the staining reagent has degraded.

[0113] Alternatively or additionally, the plausibility check can include checking a particular hematology parameter whose value is related to obtaining a reliable value of another one of the plurality of hematology parameters. For example, the inventors have recognized that obtaining a reliable NE-SFL can require a minimum NEUT population on the blood sample. Similarly, the inventors have also recognized that obtaining reliable RET-He and Delta-He parameters can require a minimum RET population. This is because if the number of RET cells is below a threshold, there are not enough RET cells to determine a reliable center of the RET population on the Y-axis, which leads to an unreliable RET-He, which in turn leads to an unreliable Delta-He. Such intrinsic dependencies or relationships between hematology parameters make it possible to perform plausibility tests on a reduced set of hematology parameters, such as the subsets explained above, which makes the plausibility tests more time efficient.

[0114] Moreover, not only can plausibility tests be performed on the actual obtained (measured) hematology parameters of the subject's blood, but also on general situations of the blood cell counter system 1 and the reagents used, and thus, the plausibility tests can include additional general plausibility tests for the equipment used. Examples of such general plausibility tests can include one or more of: message format error (which indicates that the format of the message from the hematology analyzer (detection device 2) is invalid), intake error, error flag, function error, suspicious sample, missing test, and / or unsupported parameter unit (which indicates that the result from the hematology analyzer (detection device 2) contains a unit for a blood test that is not supported for the calculation of the ICIS score).

[0115] For each of the above general plausibility checks, a corresponding individual error code can be provided. For example, the "ICIS_Error_Result" indicates an erroneous measurement of the hematology analyzer (detection device 2) and contains a result error flag that does not allow the calculation of the ICIS score.

[0116] Similarly, the error code "ICIS_ACTION_MESSAGE" from the hematology analyzer (detection device 2) can contain an action message that does not allow the calculation of the ICIS score, such as "suspicious sample" or "check sample".

[0117] The above, an embodiment has been described in which the blood cell counter system 1 has the detection apparatus 2, the first diagnostic support apparatus 3, and the second diagnostic support apparatus 3'. In such a configuration, the first diagnostic support apparatus can evaluate the data of the forward scatter light signal, the side scatter light signal, and the fluorescence signal to create corresponding scatter plots, and determine a set of hematological parameters for the blood sample of the subject. Subsequently, the second diagnostic support apparatus 3' obtains the plurality of hematological parameters, and performs the plausibility check as described above.

[0118] According to an alternative embodiment, the blood cell counter system 1 can include the detection apparatus 2 and a single diagnostic support apparatus 3. In such a configuration, the diagnostic support apparatus 3 can evaluate the data of the forward scatter light signal, the side scatter light signal, and the fluorescence signal to create corresponding scatter plots, and determine a set of hematological parameters for the blood sample of the subject, to obtain the plurality of hematological parameters, and perform the plausibility check as described above.

[0119] Such an alternative embodiment can be used to implement the method according to the flowchart of Figure 13 . In particular, according to step S51 of Figure 13 , the CPU 31a of the diagnostic support apparatus 3 can receive, via the communication interface 31g, the data of the forward scatter light signal, the side scatter light signal, and the fluorescence signal output by the detector 5 of the detection apparatus 2, and store such data in the memory 31b. Subsequently, in step S52, the CPU 31a creates corresponding scatter plots, for example at least: a RET scatter plot having a Y-axis of the intensity of the forward scatter light signal and an X-axis of the intensity of the fluorescence signal, both signals being output by the detector 5; a DIFF scatter plot having a Y-axis of the intensity of the fluorescence signal and an X-axis of the intensity of the side scatter light signal, both signals being output by the detector 5; and a WBC / BASO scatter plot having a Y-axis of the intensity of the forward scatter light signal and an X-axis of the intensity of the side scatter light signal, both signals being output by the detector 5.

[0120] Next, the CPU 31a of the diagnostic support apparatus 3 obtains a plurality of hematological parameters for the blood of the subject by using the scatter plots as detection results. Examples of such hematological parameters are NEUT#, NE-SFL, RET#, HFLC#, IG#, EO#, Delta-He, RBC-He, PLT (as described above).

[0121] Next, the CPU 31a of the diagnostic support apparatus 3 also performs the following steps: performing the plausibility check of steps S54 and S55, calculating the ICIS index value (step S56), and outputting diagnostic support information (step S57), or outputting an error code or an error message (step S58). Steps S54-S58 correspond to Figure 6Steps S32-S36. The technicians recognized... Figure 13 All steps are performed by a single diagnostic support device 3, with regard to Figure 6 The described embodiments differ, in Figure 6 In the described embodiments, the workload of determining a set of hematological parameters and conducting rationality checks is shared between two diagnostic support devices.

[0122] Alternative embodiments can also be used to implement according to Figure 14 The flowchart method. (and) Figure 13 In contrast, here, the plausibility checks in steps S54a and S55a are performed on a subset of the hematological parameters, rather than the entire set of hematological parameters obtained from the scatter plot in step S53. As explained above, the entire set of hematological parameters obtained from a single diagnostic support device in step S53 can 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 can correspond to the three parameters NEUT#, NE-SFL, and RET#. Furthermore, in an alternative embodiment using a single diagnostic support device, performing the plausibility check using only a subset of the hematological parameters makes the reliability of the check more efficient.

[0123] The above has described an embodiment of a blood cell counter system 1 having a detection device 2, which includes a detector 5 configured to detect various types of specific information from the blood sample, such as forward and side-scattered light from the cells after they pass through a flow cell, as well as fluorescence. The detection device 2 may additionally or alternatively include different detectors configured to measure the direct current (DC) impedance of a single cell passing through the cell interrogation zone of the blood sample. The detection device 2 may also or alternatively include further different detectors configured to measure the radio frequency (RF) conductivity of a single cell passing through the cell interrogation zone of the blood sample.

[0124] The above describes an example of preparing a WBC / BASO sample for a specific basophil (BASO) assay by diluting the blood and further dissolving it using a specialized reagent for lysing red blood cells. Alternatively, a BASO sample can be prepared by diluting and dissolving the blood and further staining it using a specialized reagent for staining blood cells. In this case, cell classification can be performed by preparing a scatter plot showing the intensity of the forward-scattered light signal on the Y-axis and the intensity of the fluorescence signal on the X-axis.

[0125] The above has described an embodiment in which both the DIFF scatter plot and the WBC / BASO scatter plot are analyzed for classifying white blood cells into the five groups of monocytes (MONO), lymphocytes (LYMPH), neutrophils (NEUT), basophils (BASO), and eosinophils (EO). Alternatively, only the DIFF scatter plot can be analyzed for classifying white blood cells into the five groups.

[0126] The above has described an embodiment in which the ICIS index value is output on the output screen when the plausibility check passes, while the ICIS index value is not output on the output screen when the plausibility check fails. Alternatively, the ICIS index value can be output in the output screen when the plausibility check fails, along with a flag indicating that the ICIS index value is not reliable.

[0127] The above has described an embodiment in which the ICIS index value is output as an indication of the probability of the subject's infectious reaction. Alternatively, a flag or message indicating the probability of the subject's infectious reaction can be output as the indication.

[0128] The above has described an embodiment in which the hematology parameter NE-SFL is used as a parameter reflecting the quality of the reagent mixed with the blood, because NE-SFL reflects the degree of staining of cells by the staining reagent. Alternatively or additionally, a hematology parameter reflecting the quality of the lysing reagent can be used. For example, a white blood cell count (such as a monocyte count or a neutrophil count) can be used as a parameter reflecting the quality of the lysing reagent, because when there is too much debris, an abnormally large number of monocytes or neutrophils will appear, indicating that the lysis is incomplete.

Claims

1. A computer-based method for examining the blood of an object, comprising: - Receive a measurement command for the blood to calculate an index value indicating the likelihood of an infection response in the object; - Based on the measurement command, the value of each of a plurality of hematological parameters for the blood is obtained based on the measurement of the blood; - A reasonableness check of the value of at least one of the hematological parameters is performed by performing a first comparison between the value of at least one of the hematological parameters and a first threshold set for a reasonableness check; - If the rationality check has passed, a second comparison is performed between the value of each of the plurality of hematological parameters and a second threshold set for determining a score value for each of the plurality of hematological parameters, and the index value is calculated by summing the score values, and a first screen for displaying the measurement results of the blood is output, wherein the first screen displays the calculated index value and the value of each of the plurality of hematological parameters; as well as - If the plausibility check fails, a second screen is output to show the measurement results of the blood without performing the second comparison, wherein the second screen shows an error message or error code indicating that the plausibility check failed, and wherein the second screen further shows the value of each of the plurality of hematological parameters, including the hematological parameter that caused the plausibility check to fail.

2. The computer implementation method according to claim 1, wherein... The steps of performing a reasonableness check include checking the values ​​of a subset of the hematological parameters, and not performing a reasonableness check on the values ​​of the hematological parameters other than the subset of the hematological parameters, wherein, The subset of hematological parameters relates to at least one of neutrophils and reticular cells.

3. The computer implementation method according to claim 1 or 2, wherein... The measurements include white blood cell count measurement, and The steps for performing a reasonableness check include checking the values ​​of the hematological parameters obtained from the white blood cell difference measurement.

4. The computer implementation method according to claim 1 or 2, wherein... The steps for performing a rationality check include checking the white blood cell count, which is said to be a hematological parameter.

5. The computer implementation method according to claim 1 or 2, wherein... The hematological parameters include a first hematological parameter and a second hematological parameter, wherein, The value of the first hematological parameter is related to obtaining a reliable value for the second hematological parameter, and The steps for performing a reasonableness check include checking the value of the first hematological parameter.

6. The computer implementation method according to claim 1 or 2, wherein... The steps of performing a reasonableness check include performing a first comparison between the values ​​of two or more hematological parameters and the first threshold, wherein the first threshold is predetermined individually for each of the two or more hematological parameters.

7. The computer implementation method according to claim 1 or 2, wherein... The error message or error code includes information indicating at least one hematological parameter that caused the failure of the rationality check.

8. The computer implementation method according to claim 1 or 2, wherein The steps for performing a rationality check include checking the values ​​of the hematological parameters related to neutrophils.

9. The computer implementation method according to claim 1 or 2, wherein... The steps of performing a rationality check include checking the values ​​of the hematological parameters related to neutrophils and the values ​​of the hematological parameters related to blood cells other than neutrophils.

10. The computer implementation method according to claim 1 or 2, further comprising: Perform a general suitability check on the testing equipment used to measure the blood.

11. The computer implementation method according to claim 1 or 2, wherein... The steps of performing a rationality check include checking the value of the hematological parameter that reflects the operating status of the testing equipment measuring the blood, or the value of the hematological parameter that reflects the quality of the reagent mixed with the blood.

12. The computer implementation method according to claim 1 or 2, further comprising: Based on the calculated index value, it is determined whether the object has an infectious response or a non-infectious response.

13. A diagnostic support device for examining the blood of a subject, comprising: The controller is configured to: - Receive a measurement command for the blood to calculate an index value indicating the likelihood of an infection response in the object; - Based on the measurement command, the value of each of a plurality of hematological parameters for the blood is obtained based on the measurement of the blood; - The reasonableness check of the value of at least one of the hematological parameters is performed by performing a first comparison between the value of at least one of the hematological parameters and a first threshold set for a reasonableness check; - If the rationality check has passed, a second comparison is performed between the value of each of the plurality of hematological parameters and a second threshold set for determining a score value for each of the plurality of hematological parameters, and the index value is calculated by summing the score values, and a first screen for displaying the measurement results of the blood is output, wherein the first screen displays the calculated index value and the value of each of the plurality of hematological parameters; as well as - If the plausibility check fails, a second screen is output to show the measurement results of the blood without performing the second comparison, wherein the second screen shows an error message or error code indicating that the plausibility check failed, and wherein the second screen further shows the value of each of the plurality of hematological parameters, including the hematological parameter that caused the plausibility check to fail.

14. A blood cell counter system, the blood cell counter system comprising a blood detector device for measuring the blood and a diagnostic support device according to claim 13.

15. A computer program product comprising: A computer-readable medium storing instructions that enable a general-purpose computer to perform operations of the computer-implemented method according to any one of claims 1-13.

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