WO2023125939A1 - 血液细胞分析仪、提示感染状态的方法以及感染标志参数的用途 - Google Patents

血液细胞分析仪、提示感染状态的方法以及感染标志参数的用途 Download PDF

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WO2023125939A1
WO2023125939A1 PCT/CN2022/143965 CN2022143965W WO2023125939A1 WO 2023125939 A1 WO2023125939 A1 WO 2023125939A1 CN 2022143965 W CN2022143965 W CN 2022143965W WO 2023125939 A1 WO2023125939 A1 WO 2023125939A1
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infection
parameter
scattered light
intensity distribution
blood cell
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PCT/CN2022/143965
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English (en)
French (fr)
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李进
潘世耀
张晓梅
吴传健
郑文波
叶波
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深圳迈瑞生物医疗电子股份有限公司
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Publication of WO2023125939A1 publication Critical patent/WO2023125939A1/zh

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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/62—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light
    • G01N21/63—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light optically excited
    • G01N21/64—Fluorescence; Phosphorescence
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/483—Physical analysis of biological material
    • G01N33/487—Physical analysis of biological material of liquid biological material
    • G01N33/49—Blood

Definitions

  • the present application relates to the field of in vitro diagnosis, in particular to a blood cell analyzer, a method for prompting the infection status of a subject and the use of infection marker parameters in evaluating the infection status of a subject.
  • Sepsis sepsis
  • Sepsis is a serious infectious disease.
  • the incidence of sepsis is high.
  • Sepsis has surpassed myocardial infarction as the leading cause of death in noncardiac patients in intensive care units.
  • the mortality rate of sepsis is still as high as 30% to 70%.
  • CRP C-reactive protein
  • PCT procalcitonin
  • SAA serum amyloid A
  • Microbial culture is considered to be the most reliable gold standard, which can directly culture and detect bacteria in clinical specimens such as body fluids or blood, thereby interpreting their type and drug resistance, which can directly guide clinical medication.
  • this method has a long reporting cycle, samples are easily contaminated, and the false negative rate is high, which cannot well meet the requirements of rapid and accurate clinical results.
  • CRP inflammatory factor
  • PCT inflammatory factor
  • SAA inflammatory factor-like protein
  • CRP and PCT are widely used in the auxiliary diagnosis of infectious diseases.
  • the specificity of these tests for infectious diseases is weak, and the joint detection of CRP, PCT and SAA is usually required, which increases the economic burden of patients, and CRP and PCT are interfered by specific diseases, so sometimes they cannot be correct Reflects the infection status of the patient.
  • CRP is produced in the liver, and infected patients with liver damage have normal CRP levels, which may lead to false negative results in the diagnosis of infectious diseases.
  • Serum antigen and antibody detection can confirm specific virus types, but it has limited effect on the situation where the type of pathogen is not clear, and the detection cost is high, which increases the economic burden of patients.
  • WBC white blood cell
  • Neu neurotrophil
  • one of the purposes of this application is to provide a low-cost and rapid solution to assess the infection status of the subject, in which a new blood cell morphological parameter is developed using a blood cell analyzer Evaluate the infection status of the subject, including early prediction of sepsis, diagnosis of sepsis, identification of common infection and severe infection, monitoring of infection condition, prognosis analysis of sepsis, identification of bacterial infection and viral infection, or Differentiation of non-infectious inflammation and infectious inflammation, evaluation of sepsis efficacy.
  • this solution does not require additional testing costs, and the assessment of infection status can be achieved while using the existing blood cell analyzer for blood routine testing.
  • the first aspect of the present application provides a blood cell analyzer, which includes:
  • a sample aspirating device used to aspirate the subject's blood sample to be tested
  • a sample preparation device for preparing a measurement sample containing a part of the blood sample to be tested, a hemolyzing agent, and a staining agent for identifying nucleated erythrocytes
  • An optical detection device comprising a flow chamber, a light source and a light detector, the flow chamber is used for the measurement sample to pass through, the light source is used to irradiate the measurement sample passing through the flow chamber with light, and the light detector is used for optical information produced upon detection of said assay sample being irradiated with light as it passes through said flow cell; and
  • Processor configured as:
  • the second aspect of the present application provides a method for prompting the infection status of the subject, the method comprising the following steps:
  • the third aspect of the present application also provides the use of infection marker parameters in evaluating the infection status of a subject, wherein the infection marker parameters are obtained by a method comprising the following steps:
  • An infection marker parameter is obtained based on the at least one white blood cell characteristic parameter.
  • white blood cell characteristic parameters including cell characteristic parameters can be obtained from the detection channel used to identify nucleated red blood cells, thereby enabling doctors to quickly, accurately and efficiently assist doctors in diagnosis and treatment of infectious diseases. prediction or diagnosis.
  • prompt information prompting the subject's infection status can be effectively provided.
  • Fig. 1 is a schematic structural diagram of a blood cell analyzer according to some embodiments of the present application.
  • Fig. 2 is a schematic structural diagram of an optical detection device according to some embodiments of the present application.
  • Fig. 3 is a two-dimensional scattergram of FL-FS of a measurement sample according to some embodiments of the present application.
  • Fig. 4 is a two-dimensional scattergram of SS-FS of a measurement sample according to some embodiments of the present application.
  • Fig. 5 is a three-dimensional scatter diagram of FL-SS-FS of a measurement sample according to some embodiments of the present application.
  • Fig. 6 shows the determination of cell characteristic parameters of leukocyte clusters in a sample according to some embodiments of the present application.
  • Fig. 7 is a schematic flow chart of judging the progress of a patient's condition according to some embodiments of the present application.
  • FIG. 8 , FIG. 9 , and FIG. 10 are scatter diagrams of abnormalities in the measurement samples according to some embodiments of the present application.
  • Fig. 11 shows scatter plots before and after logarithm processing according to some embodiments of the present application.
  • Fig. 12 is a schematic flowchart of a method for prompting a subject's infection status according to some embodiments of the present application.
  • Figures 13-14 are ROC curves in the scenario of early prediction of sepsis according to some embodiments of the present application.
  • ROC curves in the severe infection identification scenario according to some embodiments of the present application.
  • Fig. 19, Fig. 20 and Fig. 21 are graphs showing changes in values of infection marker parameters for monitoring the development of severe infection conditions according to some embodiments of the present application.
  • FIG. 22 and FIG. 23 are graphs showing changes in values of infection marker parameters used for monitoring the progression of sepsis according to some embodiments of the present application.
  • Fig. 24A-Fig. 24D visually show the detection results of using N_WBC_FL_P as a single parameter for the curative effect on sepsis.
  • Fig. 24A shows the measured value of N_WBC_FL_P of each patient in the effective group and the ineffective group before and after 5 days of antibiotic treatment.
  • Figure 24B shows a box and whisker plot of patients in the effective and non-effective groups.
  • Figure 24C shows the comparison of the mean N_WBC_FL_P determination values of the effective group before antibiotic treatment and after 5 days of treatment, and the comparison of the mean N_WBC_FL_P determination values of the ineffective group before antibiotic treatment and after 5 days of treatment.
  • Figure 24D shows the ROC curve for detection of efficacy on sepsis using N_WBC_FL_P as a single parameter.
  • Figure 25A- Figure 25D visually show the detection results of using N_FL_PULWID_MEAN as a single parameter for the efficacy of sepsis.
  • Fig. 25A shows the N_FL_PULWID_MEAN measured value of each patient in the effective group and the ineffective group before and after 5 days of antibiotic treatment.
  • Figure 25B shows a box-and-whisker plot of patients in the responder and responder groups.
  • Figure 25C shows the comparison of the mean N_FL_PULWID_MEAN determination values of the effective group before antibiotic treatment and after 5 days of treatment, and the comparison of the mean N_FL_PULWID_MEAN determination values of the ineffective group before antibiotic treatment and after 5 days of treatment.
  • Figure 25D shows the ROC curve for detection of efficacy on sepsis using N_FL_PULWID_MEAN as a single parameter.
  • Fig. 26A-Fig. 26D visually show the detection results of the curative effect on sepsis using N_FS_PULWID_MEAN as a single parameter.
  • Fig. 26A shows the N_FS_PULWID_MEAN measured value of each patient in the effective group and the ineffective group before and after 5 days of antibiotic treatment.
  • Figure 26B shows box-and-whisker plots of patients in the responder and responder groups.
  • Figure 26C shows the comparison of the mean N_FS_PULWID_MEAN measurement values of the effective group before antibiotic treatment and after 5 days of treatment, and the comparison of the mean N_FS_PULWID_MEAN measurement values of the ineffective group before antibiotic treatment and after 5 days of treatment.
  • Figure 26D shows the ROC curve for detection of efficacy on sepsis using N_FS_PULWID_MEAN as a single parameter.
  • Fig. 27A-Fig. 27D visually show the detection results of the curative effect on sepsis using the combination of the double parameters "N_WBC_FL_P" and "N_WBC_FS_W” as the infection marker parameters.
  • Figure 27A shows the measured values of the dual parameter combination before and after 5 days of antibiotic treatment for each patient in the effective group and the ineffective group.
  • Figure 27B shows box-and-whisker plots of patients in the responder and responder groups.
  • Figure 27C shows the comparison of the combined mean values of the two parameters in the effective group before antibiotic treatment and after 5 days of treatment, and the comparison of the combined mean values of the two parameters in the ineffective group before antibiotic treatment and after 5 days of treatment.
  • Figure 27D shows the ROC curve for the detection of efficacy on sepsis using this two-parameter combination.
  • Fig. 28A-Fig. 28D visually show the detection results of the curative effect on sepsis using the combination of the double parameters "N_WBC_FL_W” and "N_WBC_FS_P" as the infection marker parameters.
  • Figure 28A shows the measured values of the dual parameter combination before and after 5 days of treatment for each patient in the effective group and the ineffective group.
  • Figure 28B shows box-and-whisker plots of patients in the responder and responder groups.
  • Figure 28C shows the comparison of the combined mean values of the two parameters in the effective group before antibiotic treatment and after 5 days of treatment, and the comparison of the combined mean values of the two parameters in the ineffective group before antibiotic treatment and after 5 days of treatment.
  • Figure 28D shows the ROC curve for the detection of efficacy on sepsis using this two-parameter combination.
  • Fig. 29A-Fig. 29D visually show the detection results of the curative effect on sepsis using the combination of the two parameters "N_WBC_FL_P" and "N_WBC_FS_CV" as the infection marker parameters.
  • Figure 29A shows the measured values of the dual parameter combination before and after 5 days of treatment for each patient in the effective group and the ineffective group.
  • Figure 29B shows a box and whisker plot of patients in the responder and responder groups.
  • Figure 29C shows the comparison of the combined mean values of the two parameters in the effective group before antibiotic treatment and after 5 days of treatment, and the comparison of the combined mean values of the two parameters in the ineffective group before antibiotic treatment and after 5 days of treatment.
  • Figure 29D shows the ROC curve for the detection of efficacy on sepsis using this two-parameter combination.
  • Fig. 30A-Fig. 30D visually show the detection results of the curative effect on sepsis using the combination of the double parameters "N_WBC_FL_W” and "D_Neu_FL_W” as the infection marker parameters.
  • Figure 30A shows the measured values of the dual parameter combination before and after 5 days of treatment for each patient in the effective group and the ineffective group.
  • Figure 30B shows the box-and-whisker plots of patients in the responder and responder groups.
  • Figure 30C shows the comparison of the combined mean values of the two parameters in the effective group before antibiotic treatment and after 5 days of treatment, and the comparison of the combined mean values of the two parameters in the ineffective group before antibiotic treatment and after 5 days of treatment.
  • Figure 30D shows the ROC curve for the detection of efficacy on sepsis using this two-parameter combination.
  • Fig. 31A-Fig. 31D visually show the detection results of the curative effect on sepsis using the combination of the double parameters "N_WBC_FL_W” and "D_Neu_FL_CV” as the infection marker parameters.
  • Figure 31A shows the measured values of the dual parameter combination before and after 5 days of antibiotic treatment for each patient in the effective group and the ineffective group.
  • Figure 3 IB shows box and whisker plots of patients in the responder and responder groups.
  • Figure 31C shows the comparison of the combined mean values of the two parameters in the effective group before antibiotic treatment and after 5 days of treatment, and the comparison of the combined mean values of the two parameters in the ineffective group before antibiotic treatment and after 5 days of treatment.
  • Figure 31D shows the ROC curve for the detection of efficacy on sepsis using this two-parameter combination.
  • Fig. 32 is an algorithm calculation step of the area parameter D_NEU_FLSS_Area of the neutrophil population according to some embodiments of the present application.
  • the terms “comprising”, “comprising” or any other variant thereof are intended to cover non-exclusive inclusion, so that a method or device comprising a series of elements not only includes the explicitly stated elements, but also include other elements not explicitly listed, or also include elements inherent in implementing the method or apparatus.
  • an element defined by the statement “comprising a " does not exclude the presence of additional related elements (such as steps in the method or units in the device) in the method or device that includes the element , the unit here may be a part of a circuit, a part of a processor, a part of a program or software, etc.).
  • first ⁇ second ⁇ third involved in the embodiment of this application is only to distinguish similar objects, and does not represent a specific ordering of objects. Understandably, “first ⁇ second ⁇ third Three” are interchangeable in a specific order or sequence where permissible. It should be understood that the objects distinguished by “first ⁇ second ⁇ third” can be interchanged under appropriate circumstances, so that the embodiments of the application described herein can be implemented in sequences other than those illustrated or described herein.
  • the term "at least one" involved in the embodiments of the present application means 1 or more than 1 under reasonable conditions, such as 2, 3, 4, 5 or 10, etc.
  • scatter diagram involved in the embodiments of the present application is a two-dimensional or three-dimensional diagram generated by a blood cell analyzer, on which are distributed two-dimensional or three-dimensional characteristic information of multiple particles, where the X of the scatter diagram is The coordinate axis, Y coordinate axis and Z coordinate axis all represent a characteristic of each particle.
  • the X coordinate axis represents the intensity of forward scattered light
  • the Y coordinate axis represents the fluorescence intensity
  • Z axis coordinates The axis represents the side scattered light intensity.
  • scatter plot used in the present disclosure not only refers to a distribution graph of at least two groups of data in the form of data points in a Cartesian coordinate system, but also includes data arrays, that is, it is not limited by its graphical presentation form.
  • particle cluster or "cell cluster” involved in the embodiments of the present application is distributed in a certain area of the scatter diagram and formed by a plurality of particles with the same cell characteristics, such as white blood cells (including all types of white blood cells) ) clusters, and leukocyte subsets, such as neutrophil clusters, lymphocyte clusters, monocyte clusters, eosinophil clusters or basophil clusters, etc.
  • white blood cells including all types of white blood cells
  • leukocyte subsets such as neutrophil clusters, lymphocyte clusters, monocyte clusters, eosinophil clusters or basophil clusters, etc.
  • ROC curve receiver operator characteristic curve
  • ROC_AUC area under the curve
  • the principle of making the ROC curve is to set a number of different critical values for continuous variables, and calculate the corresponding sensitivity (sensitivity) and specificity (specificity) at each critical value, and then take the sensitivity as the vertical axis, and use 1- The specificity is plotted as a curve on the abscissa.
  • the ROC curve is composed of multiple cut-off values representing their respective sensitivity and specificity, the best diagnostic cut-off value of a certain diagnostic method can be selected by means of the ROC curve.
  • the point on the ROC curve closest to the upper left corner on the ROC curve has the largest sum of sensitivity and specificity, and the value corresponding to this point or its adjacent points is often used as a diagnostic reference value (also called diagnostic threshold or judgment threshold or preset conditions or preset ranges).
  • blood cell analyzers generally count and classify white blood cells through DIFF channels and/or WNB channels.
  • the blood cell analyzer uses the DIFF channel to classify white blood cells into four types of white blood cells, and classify white blood cells into four types: lymphocytes (Lym), monocytes (Mon), neutrophils (Neu), and eosinophils (Eos). leukocyte.
  • the blood cell analyzer recognizes nucleated red blood cells through the WNB channel, and can simultaneously obtain the number of nucleated red blood cells, white blood cells and basophils.
  • the blood cell analyzer used in this application classifies and counts the particles in the blood sample through the flow cytometry technology combining the laser light scattering method and the fluorescent staining method.
  • the principle of the hematology analyzer for detecting blood samples can be, for example, as follows: first draw the blood sample, and treat the blood sample with a hemolytic agent and a fluorescent dye, wherein the red blood cells are destroyed and dissolved by the hemolytic agent, while the white blood cells will not be dissolved, but the fluorescent dye With the help of a hemolytic agent, it can enter the nucleus of the white blood cell and combine with the nucleic acid substances in the nucleus; then the particles in the sample pass through the detection holes irradiated by the laser beam one by one.
  • the characteristics of the particles themselves can block or change the direction of the laser beam, thereby generating scattered light at various angles corresponding to the particle characteristics, and the particle structure can be obtained after the scattered light is received by the signal detector and information about its composition.
  • forward scattered light (Forward scatter, FS) reflects the number and volume of particles
  • side scattered light (Side scatter, SS) reflects the complexity of the internal structure of cells (such as intracellular particles or nuclei)
  • fluorescence (Fluorescence, FL ) reflects the content of nucleic acid substances in cells. Using this light information, the particles in the sample can be classified and counted.
  • Fig. 1 is a schematic structural diagram of a blood cell analyzer according to some embodiments of the present application.
  • the blood cell analyzer 100 includes a sample suction device 110 , a sample preparation device 120 , an optical detection device 130 and a processor 140 .
  • the blood cell analyzer 100 also has a fluid circuit system for communicating with the sample aspirating device 110 , the sample preparation device 120 and the optical detection device 130 , so as to carry out liquid delivery between these devices.
  • the sample aspirating device 110 is used to aspirate the subject's blood sample to be tested.
  • the sample aspirating device 110 has a sampling needle (not shown) for aspirating a blood sample to be tested.
  • the sample aspirating device 110 may further include a driving device, which is used to drive the sampling needle to quantitatively absorb the blood sample to be tested through the nozzle of the sampling needle.
  • the sample suction device 110 can deliver the sucked blood sample to the sample preparation device 120 .
  • the sample preparation device 120 is used to prepare a measurement sample containing a blood sample to be tested, a hemolyzing agent, and a dye for identifying nucleated red blood cells.
  • the hemolytic agent is used to lyse red blood cells in the blood, break the red blood cells into fragments, but keep the shape of the white blood cells basically unchanged.
  • the hemolytic agent may be any one or a combination of cationic surfactants, nonionic surfactants, anionic surfactants, and amphiphilic surfactants.
  • the hemolytic agent may include at least one of alkyl glycosides, triterpene saponins, and steroidal saponins.
  • the hemolytic agent may be selected from octylquinoline bromide, octylisoquinoline bromide, decylquinoline bromide, decylisoquinoline bromide, dodecylquinoline bromide, dodecylquinoline bromide, Isoquinoline bromide, tetradecyl quinoline bromide, tetradecyl isoquinoline bromide, octyltrimethylammonium chloride, octyltrimethylammonium bromide, decyltrimethylammonium chloride Ammonium, Decyltrimethylammonium Bromide, Dodecyltrimethylammonium Chloride, Dodecyltrimethylammonium Bromide, Tetradecyltrimethylammonium Chloride, Tetradecyltrimethylammonium ammonium bromide; dodecyl alcohol polyoxyethylene (23) ether, cetyl alcohol polyoxyethylene (23)
  • the staining agent may be a fluorescent dye capable of binding nucleic acid species in nucleated red blood cells.
  • the following compounds can be used in the examples of this application.
  • the sample preparation device 120 may include at least one reaction cell and a reagent supply device (not shown in the figure).
  • the at least one reaction pool is used to receive the blood sample to be tested sucked by the sample suction device 110, and the reagent supply device provides processing reagents (including hemolyzing agent, staining agent, etc.)
  • the blood sample to be tested sucked by the sample device 110 is mixed with the processing reagent supplied by the reagent supply device in the reaction cell to prepare a measurement sample.
  • the at least one reaction cell may include a first reaction cell and a second reaction cell
  • the reagent supply device may include a first reagent supply part and a second reagent supply part.
  • the sample aspirating device 110 is used for partially distributing the aspirated blood samples to be tested to the first reaction pool and the second reaction pool respectively.
  • the first reagent supply part is used to supply the first hemolyzing agent and the first staining agent used for leukocyte classification to the first reaction pool, so as to distribute the part of the blood sample to be tested in the first reaction pool together with the first hemolyzing agent and the first staining agent.
  • the dyes are mixed and reacted to prepare a first measurement sample.
  • the second reagent supply part is used to supply the second hemolyzing agent and the second staining agent for identifying nucleated erythrocytes to the second reaction pool, so that part of the blood sample to be tested is distributed to the second reaction pool together with the second hemolyzing agent and the second staining agent.
  • the second dye is mixed and reacted to prepare a second measurement sample.
  • commercially available reagents for the four classifications of white blood cells can be used for the first hemolytic agent and the first staining agent of this application, such as M-60LD and M-6FD; commercially available reagents for identifying nucleated red blood cells Reagents can be used for the second hemolytic agent and the second staining agent of this application, such as M-6LN and M-6FN.
  • the optical detection device 130 includes a flow chamber for passing the measurement sample through, a light source for irradiating the measurement sample passing through the flow chamber with light, and a light detector for Optical information generated after the measurement sample is irradiated with light while passing through the flow cell is detected.
  • the first measurement sample and the second measurement sample respectively pass through the flow chamber
  • the light source irradiates the first measurement sample and the second measurement sample respectively passing through the flow chamber
  • the light detector is used to detect the first measurement sample and the second measurement sample.
  • the first optical information and the second optical information generated after the sample is irradiated with light when passing through the flow chamber respectively are measured.
  • the first detection channel also referred to as DIFF channel
  • the second detection channel of nuclear red blood cells also referred to as the WNB channel
  • the WNB channel refers to the detection of the second measurement sample prepared by the sample preparation device 120 by the optical detection device 130 .
  • a flow cell refers to a chamber of focused liquid flow suitable for detection of light scattering and fluorescence signals.
  • a particle such as a blood cell
  • Light detectors can be positioned at one or more different angles relative to the incident light beam to detect light scattered by the particle to obtain a light scatter signal. Since different particles have different light scattering properties, the light scattering signal can be used to distinguish different particle populations.
  • light scatter signals detected near the incident light beam are often referred to as forward light scatter signals or small angle light scatter signals. In some embodiments, the forward light scatter signal may be detected from an angle of about 1° to about 10° from the incident beam.
  • the forward light scatter signal may be detected from an angle of about 2° to about 6° from the incident beam.
  • the light scatter signal detected at about 90° to the incident light beam is often referred to as the side light scatter signal.
  • the side light scatter signal may be detected from an angle of about 65° to about 115° from the incident light beam.
  • the fluorescent signal from blood cells stained with a fluorochrome is also typically detected at about 90° to the incident beam.
  • the photodetectors may include a forward scatter light detector for detecting forward scatter light signals, a side scatter light detector for detecting side scatter light signals, and a fluorescent light detector for detecting fluorescence signals. Detector.
  • the optical information may include measuring forward scattered light signals, side scattered light signals and fluorescent signals of particles in the sample.
  • FIG. 2 shows a specific example of the optical detection device 130 .
  • the optical detection device 130 has a light source 101 , a beam shaping component 102 , a flow chamber 103 and a forward scattered light detector 104 sequentially arranged on a straight line.
  • a dichroic mirror 106 is arranged at an angle of 45° to the straight line.
  • Part of the side light emitted by the particles in the flow chamber 103 passes through the dichroic mirror 106 and is captured by the fluorescence detector 105 arranged at the rear of the dichroic mirror 106 at an angle of 45° with the dichroic mirror 106;
  • the side light is reflected by the dichroic mirror 106 and captured by a side scatter light detector 107 arranged in front of the dichroic mirror 106 at an angle of 45° to the dichroic mirror 106 .
  • the processor 140 is used to process and calculate the data to obtain the required results. For example, a two-dimensional scattergram or a three-dimensional scattergram can be generated according to various optical signals collected, and on the scattergram according to gating ) method for particle analysis.
  • the processor 140 can also perform visualization processing on the intermediate calculation result or the final calculation result, and then display it through the display device 150 .
  • the processor 140 is configured to implement the method steps described in further detail below.
  • the processor includes but is not limited to a central processing unit (Central Processing Unit, CPU), a micro control unit (Micro Controller Unit, MCU), a field programmable gate array (Field-Programmable Gate Array, FPGA) , digital signal processor (DSP) and other devices used to interpret computer instructions and process data in computer software.
  • CPU Central Processing Unit
  • MCU Micro Controller Unit
  • FPGA Field-Programmable Gate Array
  • DSP digital signal processor
  • the processor is used to execute various computer application programs in the computer-readable storage medium, so that the blood cell analyzer 100 executes corresponding detection procedures and analyzes optical information or optical signals detected by the optical detection device 130 in real time.
  • the blood cell analyzer 100 may further include a first housing 160 and a second housing 170 .
  • the display device 150 may be, for example, a user interface.
  • the optical detection device 130 and the processor 140 are disposed inside the second casing 170 .
  • the sample preparation device 120 is, for example, disposed inside the first housing 160
  • the display device 150 is, for example, disposed on the outer surface of the first housing 160 and used to display the detection results of the hematology analyzer.
  • blood routine detection using a blood cell analyzer can prompt the occurrence of infection and identify the type of infection, but the blood routine WBC ⁇ Neu% currently used in clinical practice is affected by many aspects and cannot accurately and timely reflect patient condition. Moreover, the sensitivity and specificity of the existing technology in the diagnosis and treatment of bacterial infection and sepsis are not good.
  • the inventors through in-depth study of the original blood signal characteristics of a large number of infected patients' blood samples, unexpectedly discovered that using the leukocyte characteristic parameters of the WNB channel, for example, through linear discriminant analysis (linear discriminant analysis, LDA) can be used to evaluate the infection with high efficiency. Subject's infection status.
  • linear discriminant analysis linear discriminant analysis, LDA
  • the linear discriminant analysis is a generalization of Fisher's linear discriminant method, which uses statistics , pattern recognition and machine learning methods, by finding two classes of events (e.g., having sepsis or not having sepsis Syndrome, bacterial infection or viral infection, infectious inflammation or non-infectious inflammation, sepsis treatment is effective or ineffective), a multidimensional data is linearly combined to obtain one-dimensional data, so as to be able to characterize or distinguish the two types of events.
  • the coefficients of this linear combination can ensure maximum discrimination between the two types of events.
  • the resulting linear combination can be used to classify subsequent events.
  • the embodiment of the present application proposes a solution for obtaining infection marker parameters by using leukocyte characteristic parameters of the WNB channel for effective infection status assessment.
  • the advantage of the solution provided by the embodiment of the present application is that the infection status can be quickly evaluated to realize early prediction of sepsis, differential diagnosis of sepsis, monitoring of infection condition, prognosis of sepsis, identification of bacterial infection and viral infection, etc. .
  • the identification of bacterial infection and viral infection is carried out by using the blood cell analyzer of the present invention through the method of the present invention.
  • the main cells involved in bacterial infection are neutrophils and monocytes. These two types of cells will undergo morphological changes during bacterial infection, such as increased volume, increased granules, increased number of immature granulocytes, toxic granules, vacuoles, Dürer bodies, etc., and dense nuclei.
  • the blood cell analyzer of the present invention is embodied by detecting the signal intensity of SS, FL and FS directions of neutrophil or monocyte particle clusters.
  • the main cells involved in viral infection are lymphocytes. After virus infection, the number of lymphocytes increased significantly, and atypical lymphocytes appeared, which can be reflected in the FL direction of the scatter plot.
  • a blood cell analyzer including:
  • a sample aspirating device 110 used to aspirate the subject's blood sample to be tested
  • a sample preparation device 120 configured to prepare a measurement sample containing a part of the blood sample to be tested, a hemolyzing agent, and a staining agent for identifying nucleated red blood cells;
  • the optical detection device 130 includes a flow chamber, a light source and a light detector, the flow chamber is used for the measurement sample to pass through, the light source is used to illuminate the measurement sample passing through the flow chamber with light, and the light detection The device is used to detect the optical information generated by the measurement sample after being irradiated with light when passing through the flow chamber;
  • Processor 140 configured to:
  • the cell characteristic parameters of the target particle cluster do not include the cell count or classification parameters of the target particle cluster, but include cell characteristics such as the volume, internal granularity, and internal nucleic acid content of the cells in the target particle cluster.
  • Fig. 3 is a two-dimensional scatter diagram generated based on the forward scattered light signal FS and fluorescence signal FL in the optical information
  • Fig. 3 is a two-dimensional scatter diagram generated based on the forward scattered light signal FS and fluorescence signal FL in the optical information
  • processor 140 is further configured to identify nucleated red blood cells in the assay sample based on the optical information to obtain a count of nucleated red blood cells.
  • the at least one target particle cluster may include at least one cell cluster among the white blood cell cluster Wbc, the neutrophil cluster Neu, and the lymphocyte cluster Lym in the assay sample.
  • the at least one target particle group includes the lymphocyte mass Lym and the white blood cell mass Wbc in the assay sample, or includes the neutrophil mass Neu and the leukocyte mass Wbc in the assay sample, or includes the lymphocyte mass Lym and the leukocyte mass Wbc in the assay sample.
  • Cell mass Lym and neutrophil mass Neu may include measuring one or more parameters of the cell characteristic parameters of the lymphocyte mass Lym, the neutrophil mass Neu and the white blood cell mass Wbc in the sample.
  • the at least one target particle cluster comprises a white blood cell cluster Wbc and/or a neutrophil cluster Neu.
  • Wbc white blood cell cluster
  • neutrophil cluster Neu a neutrophil cluster Neu.
  • the inventors found that it is beneficial to effectively evaluate the infection status by using the cell characteristic parameters of the leukocyte mass Wbc and/or neutrophil mass Neu in the determination sample. More preferably, combining the cell characteristic parameters of neutrophil mass Neu and leukocyte mass Wbc can give infection marker parameters with more diagnostic efficacy.
  • the at least one white blood cell characteristic parameter may include one or more parameters in the following cell characteristic parameters: Forward scattered light intensity distribution width, forward scattered light intensity distribution center of gravity, forward scattered light intensity distribution coefficient of variation, side scattered light intensity distribution width, side scattered light intensity distribution center of gravity, side scattered light intensity distribution coefficient of variation, The width of the fluorescence intensity distribution, the center of gravity of the fluorescence intensity distribution, the coefficient of variation of the fluorescence intensity distribution, and the two-dimensional dispersion of the at least one target particle cluster generated by the two light intensities of the forward scattered light intensity, the side scattered light intensity and the fluorescent intensity
  • the area of the distribution area in the dot diagram, and the volume of the distribution area of the at least one target particle group in the three-dimensional scatter diagram generated by the forward scattered light intensity, side scattered light intensity and fluorescence intensity, such as in Figure 5 The volume of space occupied by a leukocyte mass.
  • the at least one white blood cell characteristic parameter may include one or more of the following cell characteristic parameters:
  • N_WBC_FS_P Center of gravity of forward scattered light intensity distribution of white blood cell mass
  • N_WBC_SS_P center of gravity of side scattered light intensity distribution of white blood cell mass
  • N_WBC_FL_P center of gravity of side fluorescence intensity distribution of white blood cell mass
  • N_WBC_FS_W forward scattered light intensity distribution of white blood cell mass Width
  • N_WBC_SS_W side scattered light intensity distribution width of white blood cell mass
  • N_WBC_FL_W side fluorescence intensity distribution width of white blood cell mass
  • N_WBC_FS_CV forward scattered light intensity distribution coefficient of white blood cell mass
  • N_WBC_SS_CV white blood cell mass Variation coefficient of side scattered light intensity distribution
  • N_WBC_FL_CV lateral fluorescence intensity distribution coefficient of white blood cell mass
  • the volume of the distribution area in the 3D scatter plot generated by the side scattered light intensity and the fluorescence intensity for example: the volume of the distribution area of the leukocyte cluster in the 2D scatter plot generated by the side scattered light intensity and the forward scattered light intensity Area (N_WBC_SSFS_Area), the area of the distribution area of the white blood cell mass in the two-dimensional scatter diagram generated by the side fluorescence intensity and the forward scattered light intensity (N_WBC_FLFS_Area), the white blood cell mass in the distribution area generated by the side fluorescence intensity and the side scattered light intensity
  • N_WBC_FLSS_Area the area of the distribution area in the generated two-dimensional scatter plot
  • the centroid of forward scattered light intensity distribution of neutrophil cluster (N_NEU_FS_P), the centroid of side scattered light intensity distribution of neutrophil cluster (N_NEU_SS_P), the centroid of lateral fluorescence intensity distribution of neutrophil cluster (N_NEU_FL_P), Forward scattered light intensity distribution width of neutrophil cluster (N_NEU_FS_W), side scattered light intensity distribution width of neutrophil cluster (N_NEU_SS_W), lateral fluorescence intensity distribution width of neutrophil cluster (N_NEU_FL_W), The coefficient of variation of the distribution of forward scattered light intensity of neutrophil clusters (N_NEU_FS_CV), the coefficient of variation of distribution of side scattered light intensity of neutrophil clusters (N_NEU_SS_CV), the coefficient of variation of the distribution of side fluorescence intensity of neutrophil clusters ( N_NEU_FL_CV);
  • N_LYM_FS_P The center of gravity of forward scattered light intensity distribution of lymphocyte cluster
  • N_LYM_SS_P the center of gravity of side scattered light intensity distribution of neutrophil cluster
  • N_LYM_FL_P the center of gravity of lateral fluorescence intensity distribution of lymphocyte cluster
  • N_LYM_FS_W Forward scattered light intensity distribution width
  • N_LYM_SS_W side scattered light intensity distribution width of lymphocyte clusters
  • N_LYM_FL_W lateral fluorescence intensity distribution width of lymphocyte clusters
  • N_LYM_FS_CV coefficient of variation of side scattered light intensity distribution of lymphocyte mass
  • N_LYM_FL_CV coefficient of variation of side fluorescence intensity distribution of lymphocyte mass
  • N_LYM_SSFS_Area the area of the distribution area of the lymphocyte cluster in the two-dimensional scatter diagram generated by the side fluorescence intensity and the forward scattered light intensity
  • N_LYM_FLFS_Area the area of the distribution area of the lymphocyte cluster in the distribution area generated by the side fluorescence intensity and the forward scattered light intensity
  • N_LYM_FLSS_Area the area of the distribution area in the two-dimensional scatter plot generated by the side scattered light intensity.
  • the overall distribution characteristics of a certain particle population scatter diagram can be used, such as the width of the forward scattered light intensity distribution of the entire white blood cell cluster, or the characteristics of particle distribution in some areas of a certain particle population, such as The distribution area of the denser part of the neutrophil cluster, or the area that is different from the neutrophil or lymphocyte particle population in the scatter diagram of normal people.
  • the infection marker parameter may be composed of a single white blood cell characteristic parameter, for example, one of the cell characteristic parameters listed above.
  • the infection marker parameter may be a linear or non-linear function of individual leukocyte parameters.
  • the infection marker parameter can also be calculated by combining the at least one white blood cell characteristic parameter and another white blood cell parameter different from the white blood cell characteristic parameter obtained from the optical information, for example, from the above A plurality of cell characteristic parameters in the listed cell characteristic parameters are combined, especially obtained by combining linear functions.
  • processor 140 may be further configured to:
  • At least one leukocyte characteristic parameter (also referred to as a first leukocyte parameter) of a first leukocyte particle cluster in the measurement sample and at least one leukocyte characteristic parameter (also referred to as a first leukocyte parameter) of a second leukocyte particle cluster in the measurement sample are obtained from the optical information.
  • the infection marker parameter is calculated based on the at least one white blood cell characteristic parameter and the at least one second white blood cell parameter.
  • the first leukocyte mass and the second leukocyte mass differ from each other, for example the first leukocyte mass is a leukocyte mass and the second leukocyte mass is a neutrophil mass, or vice versa the first leukocyte mass is a neutrophil mass.
  • the granulocyte mass and the second leukocyte mass are leukocyte mass.
  • the at least one second white blood cell parameter includes a cell characteristic parameter, that is, the at least one second white blood cell parameter includes a cell characteristic parameter of the second white blood cell particle cluster.
  • infection marker parameters with further improved diagnostic efficacy can be provided.
  • the second white blood cell parameter comprises a classification parameter or a count parameter (eg white blood cell count or neutrophil count) of the second white blood cell particle cluster.
  • the processor 140 may be further configured to combine the first white blood cell characteristic parameter and the second white blood cell parameter into an infection marker parameter through a linear function, that is, calculate the infection by the following formula: Flag parameters:
  • Y represents an infection marker parameter
  • X1 represents a first white blood cell parameter
  • X2 represents a second white blood cell parameter
  • A, B, and C are constants.
  • the first white blood cell parameter and the second white blood cell parameter may also be combined into an infection marker parameter through a nonlinear function, which is not specifically limited in this application.
  • the two white blood cell parameters may not be calculated by a function, but the first white blood cell parameter and the second white blood cell parameter may be used in combination, and compared with their respective thresholds , to get the infection flag parameter. For example, set the diagnostic thresholds of two parameters: threshold 1 and threshold 2, and then analyze the diagnostic performance of "parameter 1 ⁇ threshold 1 or parameter 2 ⁇ threshold 2", and analyze the diagnosis of "parameter 1 ⁇ threshold 1 and parameter 2 ⁇ threshold 2" efficacy.
  • the cell characteristic parameters of the particle clusters of the WNB channel and the DIFF channel can also be used in combination.
  • the infection marker parameters may be calculated from white blood cell parameters and other blood cell parameters, that is, the infection marker parameters may be calculated from at least one white blood cell parameter and at least one other blood cell parameter.
  • the other blood cell parameter may be a differential or count parameter of platelets (PLT), nucleated red blood cells (NRBC), or reticulocytes (RET).
  • the processor 140 may also be further configured to:
  • Said infection marker parameter is calculated based on said at least two leukocyte characteristic parameters, in particular by a linear function.
  • FIG. 6 shows cell characteristic parameters of leukocyte clusters in a measurement sample according to some embodiments of the present application.
  • W(N_WBC_FS_W) represents the width of the forward scattered light intensity distribution of the white blood cell cluster in the measurement sample, wherein, N_WBC_FS_W is equal to the upper limit (UP) of the forward scattered light intensity distribution of the white blood cell cluster and the forward scattered light intensity distribution of the white blood cell cluster The difference between the lower limit (DOWN) of the scattered light intensity distribution.
  • N_WBC_FS_P represents the center of gravity of the forward scattered light intensity distribution of the white blood cell mass in the measurement sample, that is, the average position of the white blood cell in the FS direction (at "+" in Figure 6), where N_WBC_FS_P is calculated by the following formula:
  • FS(i) is the forward scattered light intensity of the i-th white blood cell.
  • N_WBC_FS_CV represents the coefficient of variation of the forward scattered light intensity distribution of the leukocyte mass in the measurement sample, wherein N_WBC_FS_CV is equal to dividing N_WBC_FS_W by N_WBC_FS_P.
  • Area (N_WBC_FLFS_Area) in FIG. 6 represents the area of the distribution area of the white blood cell clusters in the measurement sample in the scattergram generated from the forward scattered light intensity and the fluorescence intensity.
  • C represents the contour distribution curve of the white blood cell mass
  • the total number of positions within the contour distribution curve C can be recorded as the area of the white blood cell mass.
  • D_NEU_FLSS_Area can also be implemented through the following algorithm steps ( Figure 32):
  • Construct vector V1 (P1-P2) and take P1 as the starting point of the vector, find another particle P3 in the neutrophil (NEU) particle cluster, and construct vector V2 (P1-P3), so that vector V2 (P1- P3) forms the largest angle with the vector V1 (P1-P2);
  • the D_NEU_FLSS_Area is the product of the major axis a and the minor axis b.
  • the volume parameter of the distribution area of the neutrophil population in the three-dimensional scatter diagram generated by the forward scattered light intensity, side scattered light intensity and fluorescence intensity can also be obtained by a corresponding calculation method.
  • the processor 140 may be further configured to: when the value of the infection flag parameter is outside a preset range, output prompt information indicating that the infection flag parameter is abnormal. For example, when the value of the infection flag parameter increases abnormally, an upward-pointing arrow may be output to indicate the abnormal increase.
  • the processor 140 may also be configured to output the preset range.
  • the processor 140 may be further configured to: output prompt information indicating the infection status of the subject based on the infection flag parameter.
  • the processor 140 may be configured to output the prompt information to a display device for display.
  • the display device here may be the display device 150 of the hematology analyzer 100 , or other display devices communicatively connected with the processor 140 .
  • the processor 140 can output the prompt information to the display device on the user (doctor) side through the hospital information management system.
  • the infection marker parameters can be used for early prediction of sepsis, diagnosis of sepsis, identification of common infection and severe infection, monitoring of infection condition, prognosis analysis of sepsis, bacterial Identification of infection and viral infection or non-infectious inflammation and infectious inflammation, evaluation of the efficacy of sepsis.
  • the processor 140 can be further configured to perform early sepsis prediction, sepsis diagnosis, identification of common infection and severe infection, infection condition monitoring, sepsis prognosis analysis, bacterial Identification of infection and viral infection or non-infectious inflammation and infectious inflammation, evaluation of the efficacy of sepsis.
  • Sepsis is a serious infectious disease with a high incidence rate and high mortality rate. For every 1 hour delay in treatment, the patient's mortality rate increases by 7%. Therefore, early warning of sepsis is particularly important. Early identification and early warning of sepsis can increase valuable diagnosis and treatment time for patients and greatly improve survival rate.
  • the processor 140 may be configured to, when the infection marker parameters meet the first preset condition , outputting prompt information indicating that the subject may develop sepsis within a certain period of time after the blood sample to be tested is collected.
  • the certain period of time is not greater than 48 hours, that is, the embodiment of the present application can predict whether the subject may develop sepsis at most two days in advance. Further, the certain period of time is within 24 hours, that is, the embodiment of the present application can predict whether the subject may develop sepsis one day in advance.
  • the first preset condition may be, for example, that the value of the infection flag parameter is greater than a preset threshold.
  • the preset threshold can be determined according to the specific combination of parameters and the blood cell analyzer.
  • the infection marker parameter used for early prediction of sepsis may be one of the following parameters: N_WBC_FL_W; N_WBC_FS_W; N_WBC_SS_W.
  • the infection marker parameters are calculated by combining two or more white blood cell characteristic parameters of the present invention. From the cell type level, for example, neutrophils and monocytes are the first barrier of the body against infection, and they are both valuable in reflecting the degree of infection, so the characteristic parameters of neutrophils and monocytes are used in combination The characteristic parameters can improve the prediction, diagnosis, evaluation and/or guide treatment efficacy of the present invention.
  • the embodiment of the present disclosure uses the scatter diagram formed by the original optical information, and calculates the characteristics of the leukocyte-related particle population to obtain the leukocyte characteristic parameters, which are used to evaluate the subject based on the leukocyte characteristic parameters.
  • the single white blood cell characteristic parameter can be directly regarded as the infection marker parameter, and the infection marker parameter can also be obtained by calculating the linear or nonlinear function of the single white blood cell characteristic parameter; when based on multiple
  • the white blood cell characteristic parameters are used to obtain the infection marker parameters, they can be used in combination, or combined to calculate the infection marker parameters.
  • the infection marker parameter is compared with a diagnostic threshold to give relevant clinical prompts.
  • infection marker parameters can be calculated by combining the parameters listed in Table 1 for early prediction of sepsis.
  • the combination of N_WBC_FL_P and N_WBC_FS_W, the combination of N_WBC_SS_W and N_WBC_FS_W, or the combination of N_WBC_FL_ and N_NEU_FLSS_Area can be used to calculate the infection marker parameters for early prediction of sepsis.
  • the processor 140 may be configured to output an indication when the infection flag parameters meet the second preset condition A reminder that the subject has sepsis.
  • the second preset condition may also be that the value of the infection flag parameter is greater than a preset threshold.
  • the preset threshold can be determined according to the specific combination of parameters and the blood cell analyzer.
  • the infection marker parameter for sepsis diagnosis can be one of the following parameters: N_WBC_FL_W, N_WBC_FL_P, N_NEU_FL_P, N_NEU_FL_W, N_WBC_SS_W, N_NEU_FLFS_Area, N_WBC_FS_W, N_NEU_FS_W, N_NEU_FLSS_Area, N_NEU_SS_W, N_WBC_SS_ P, N_NEU_SS_P, N_WBC_FLSS_Area, N_NEU_FS_CV , N_WBC_FLFS_Area, N_WBC_FS_P, N_NEU_SSFS_Area.
  • the infection marker parameters can be calculated by combining the parameters listed in Table 2, so as to be used for the diagnosis of sepsis.
  • the combination of N_WBC_FL_P and N_WBC_FS_W, the combination of N_WBC_FL_W and N_NEU_FL_P, the combination of N_WBC_FL_W and N_NEU_FLSS_Area, the combination of N_WBC_FL_W and N_NEU_FL_W, or the combination of N_WBC_SS_P and N_WBC_FL_P can be used to calculate the infection marker parameters for sepsis diagnosis.
  • Patients with bacterial infection can be divided into common infection and severe infection according to their infection severity and organ function status.
  • the clinical treatment methods and nursing measures for the two infections are different, so the identification of common infection and severe infection can help doctors identify life-threatening Patients can also allocate medical resources more reasonably.
  • the processor 140 can be configured to, when the infection flag When the parameter satisfies the third preset condition, a prompt message indicating that the subject suffers from severe infection is output.
  • the third preset condition may also be that the value of the infection flag parameter is greater than a preset threshold.
  • the preset threshold can be determined according to the specific combination of parameters and the blood cell analyzer.
  • the infection marker parameter used for the identification of common infection and severe infection can be one of the following parameters:
  • the infection marker parameters can be calculated by combining the parameters listed in Table 3, so as to distinguish common infection from severe infection.
  • the subject is an infected patient (ie, a patient with infectious inflammation), especially a patient with severe infection or sepsis, for example, the subject is from intensive care patients with severe infection or sepsis.
  • Sepsis is a serious infectious disease with a high incidence and high fatality rate.
  • the condition of patients with sepsis fluctuates greatly, and daily monitoring is required to prevent the aggravation of the patient's condition but not deal with it in time. Therefore, it is very important to judge the progress and treatment effect of patients with sepsis by combining clinical symptoms with laboratory test results.
  • the processor 140 may be configured to monitor the development of an infection in the subject based on the infection marker parameters.
  • processor 140 may be further configured to monitor the development of an infection in the subject by:
  • the processor 140 may be further configured to: when the values of the infection marker parameters obtained through the multiple detections gradually tend to decrease, output a prompt indicating that the condition of the subject is getting better information; and when the values of the infection marker parameters obtained through the multiple detections gradually increase in a trend, output prompt information indicating that the condition of the subject is aggravated.
  • the multiple detections here may be continuous daily detections, or multiple detections at regular intervals.
  • the processor 140 may be further configured to prompt the subject's disease progression in the following manner:
  • the subject is monitored for disease progression based on a comparison of a previous value of the infection marker parameter to a first threshold and a comparison of the previous value of the infection marker parameter to a current value of the infection marker parameter.
  • Fig. 7 is a schematic flow chart of judging the progress of a patient's condition according to some embodiments of the present application.
  • the processor 140 may be further configured to when the previous value of the infection flag parameter is greater than or equal to the first threshold:
  • the output indicates that Prompt information for the exacerbation of the test subject's condition
  • the output indicates that the subject is getting better and the degree of infection is decreasing prompt information
  • the output indicates that the subject is getting better but the infection is still Heavier prompts or no prompts at all;
  • the processor 140 may be configured to: when the previous value of the infection flag parameter is less than the first threshold:
  • the output indicates that the subject is sicker and the infection is severe prompt information
  • the output indicates that the subject's condition is fluctuating or the infection may worsen Prompt message or no prompt message output
  • the first threshold when the infection marker parameters are used to monitor the progress of a patient with a severe infection, the first threshold may be a preset threshold for judging whether the subject has a severe infection. However, when the infection marker parameters are used to monitor the progress of a patient with sepsis, the first threshold may be a preset threshold for judging whether the subject suffers from sepsis.
  • the infection flag parameter used for infection condition monitoring may be one of the following parameters:
  • the combination of N_WBC_FL_P and N_WBC_FS_W can be used to calculate the infection flag parameter for monitoring the infection condition.
  • the subject is a treated sepsis patient, and the infection marker parameters are used to judge whether the subject's sepsis prognosis is good or not.
  • the processor 140 may be further configured to determine whether the prognosis of the subject's sepsis is good or not according to the infection marker parameters. For example, when the infection marker parameter satisfies the fourth preset condition, output prompt information indicating that the subject has a good prognosis of sepsis.
  • the infection marker parameter used for sepsis prognosis analysis can be one of the following parameters: N_WBC_FL_W, N_WBC_FS_W, N_WBC_FLSS_Area, N_WBC_FS_CV, N_WBC_FLFS_Area, N_WBC_SS_W, N_WBC_FL_P, N_WBC_SS_CV, N_WBC_SSFS_Area, N_WBC_SS_P, N _WBC_FS_P, N_WBC_FL_CV.
  • the infection marker parameters can be calculated by combining the parameters listed in Table 4, so as to be used for sepsis prognosis analysis.
  • Infectious diseases can be divided into different infection types such as bacterial infection, viral infection, and fungal infection, among which bacterial infection and viral infection are the most common. Although the clinical symptoms of the two infections are roughly the same, the treatment methods are completely different, so it is helpful to determine the type of infection to choose the correct treatment method.
  • the infection marker parameters are used to identify bacterial infection and viral infection, and the processor 140 may be further configured to determine whether the subject's infection type is viral infection or bacterial infection according to the infection marker parameters.
  • the infection flag parameter for the identification of bacterial infection and viral infection may be one of the following parameters: N_WBC_FS_P, N_WBC_FL_P, N_WBC_FS_W, N_WBC_FL_W, N_WBC_FLFS_Area, N_WBC_FLSS_Area, N_WBC_SS_P, N_WBC_SS_W, N_WBC_FL_CV, N_WBC_FS_CV, N_ WBC_SSFS_Area, N_WBC_SS_CV .
  • the infection marker parameters can be calculated by combining the parameters listed in Table 5, so as to identify bacterial infection and viral infection.
  • inflammation is divided into infectious inflammation caused by pathogenic microorganism infection, and non-infectious inflammation caused by physical factors, chemical factors or tissue necrosis.
  • infectious inflammation caused by pathogenic microorganism infection
  • non-infectious inflammation caused by physical factors, chemical factors or tissue necrosis.
  • the clinical symptoms of the two types of inflammation are roughly the same, and symptoms such as redness and fever will appear, but the treatment methods of the two types of inflammation are not exactly the same, so it is helpful to clarify the factors that cause the patient's inflammatory response for symptomatic treatment .
  • the infection marker parameters are used to distinguish between non-infectious inflammation and infectious inflammation, and the processor 140 may be further configured to determine whether the subject suffers from infectious inflammation or non-infectious inflammation according to the infection marker parameters . For example, when the infection marker parameter satisfies the fifth preset condition, a prompt message indicating that the subject suffers from infectious inflammation is output.
  • the infection marker parameter used for the identification of infectious inflammation and non-infectious inflammation may be one of the following parameters: N_WBC_FL_W, N_WBC_FL_P, N_WBC_SS_W, N_WBC_FS_W, N_WBC_SS_P, N_WBC_FS_P, N_WBC_FS_CV, N_WBC_SS_CV, N_WBC_FL_CV.
  • the infection marker parameters can be calculated by combining the parameters listed in Table 6, so as to distinguish infectious inflammation from non-infectious inflammation.
  • the doctor After the doctor conducts an inquiry and physical examination on the patient, he will generally have one or several preliminary disease diagnoses. Then through laboratory tests, imaging examinations and other means for differential diagnosis or disease diagnosis. Therefore, it can be said that the doctor prescribes the laboratory test list with a purpose. In other words, when the doctor prescribes the order, it is clear which scenario the parameter should be applied to. For example: A patient with fever in an ordinary outpatient clinic has no symptoms of organ damage. The doctor initially judges that it is a common infection, not a severe infection or sepsis. But what kind of medicine to prescribe, it needs to be clear whether it is a viral infection or a bacterial infection, so blood routine is prescribed.
  • the infection marker parameters output in this application are used as a reference for doctors in clinical practice, not for diagnostic purposes.
  • the processor 140 can be further configured to, when the preset characteristic parameters of the target particle cluster meet the first During six preset conditions, the value of the infection flag parameter is not output (that is, the value of the infection flag parameter is shielded), or the value of the infection flag parameter is output and the value indicating that the infection flag parameter is unreliable Prompt information.
  • the processor 140 When the processor 140 is further configured to output prompt information indicating the infection status of the subject based on the infection flag parameter, if the preset characteristic parameter of the target particle cluster satisfies the sixth preset condition, the processor 140 does not Outputting prompt information indicating the infection status of the subject, or outputting prompt information indicating the infection status of the subject and outputting additional information that the prompt information is unreliable.
  • the processor 140 may be configured to not output the value of the infection flag parameter, or output the value of the infection flag parameter and simultaneously output A message indicating that the value of this taint flag parameter is unreliable.
  • the calculation result of the infection flag parameter may be unreliable at this time. For example, as shown in FIG. 8 , if the total number of particles of the leukocyte cluster in the measurement sample is too low, the infection marker parameters calculated from the leukocyte characteristic parameters of the leukocyte cluster may be unreliable.
  • the preset characteristic parameters of the target particle cluster are abnormal based on the optical information, for example, whether the total number of particles of the target particle cluster is lower than a preset threshold.
  • the processor 140 may be configured to not output prompt information indicating the infection status of the subject and not output the value of the infection flag parameter when the target particle cluster overlaps with other particle clusters, Or output the value of the infection flag parameter and at the same time output prompt information indicating that the value of the infection flag parameter is unreliable.
  • the neutrophil cluster in the measurement sample overlaps with other particles, which may lead to unreliable infection marker parameters calculated from the white blood cell characteristic parameters of the neutrophil cluster.
  • optical information can be used to determine whether the target particle cluster overlaps with other particle clusters.
  • the processor 140 when the processor 140 is further configured to output prompt information indicating the infection status of the subject based on the infection flag parameter, if the total number of particles in the target particle cluster is less than a preset threshold, and/or if the target particle cluster overlaps with other particle clusters, the processor 140 does not output prompt information indicating the infection status of the subject, or outputs prompt information indicating the infection status of the subject and outputs additional information that the prompt information is unreliable .
  • the disease status of the subject and abnormal cells may also affect the diagnostic or suggestive efficacy of the infection marker parameters.
  • the processor 140 can be further configured to: according to whether the subject suffers from a specific disease and/or whether there is a preset type of abnormal cells (such as blast cells, abnormal lymphocytes, immature granulocytes) in the blood sample to be tested To determine whether the infection flag parameters are reliable.
  • the processor 140 may be configured to not output the value of the infection marker parameter when the subject suffers from a blood disease or abnormal cells, especially primitive cells, exist in the blood sample to be tested, or Outputting the value of the infection flag parameter and simultaneously outputting prompt information indicating that the value of the infection flag parameter is unreliable. Understandably, subjects with hematological disorders have abnormal hematograms, making the indication of infection marker parameters unreliable.
  • the processor 140 may acquire whether the subject suffers from a blood disease according to the identity information of the subject.
  • the processor 140 may be configured to determine whether abnormal cells, especially primitive cells, exist in the blood sample to be tested according to the optical information.
  • the processor 140 can also be configured to perform data processing on the white blood cell characteristic parameters before calculating the infection marker parameters, such as denoising (as shown in FIG. 10 ) or logarithmic processing (as shown in FIG. 11 ). ), in order to more accurately calculate the parameters of infection markers, such as avoiding signal changes caused by different instruments and different reagents.
  • data processing on the white blood cell characteristic parameters before calculating the infection marker parameters, such as denoising (as shown in FIG. 10 ) or logarithmic processing (as shown in FIG. 11 ).
  • the processor 140 may be further configured to: configure a priority for each infection flag parameter set according to at least one of infection diagnosis efficacy, parameter stability, and parameter limitation.
  • the processor 140 may be further configured to: configure a priority for each infection flag parameter group at least according to the infection diagnosis effectiveness. For example, the processor 140 may configure priority for each infection flag parameter group only according to the effectiveness of infection diagnosis; for another example, the processor 140 may configure priority for each infection flag parameter group according to infection diagnosis effectiveness and parameter stability; another example , the processor 140 may configure a priority for each infection flag parameter group according to infection diagnosis efficacy, parameter stability, and parameter limitation.
  • the infection marker parameter set of the present application can be used for the assessment of various infection states, such as early prediction of sepsis, diagnosis of sepsis, Identification of common infection and severe infection, monitoring of infection condition, prognosis analysis of sepsis, identification of bacterial infection and viral infection, evaluation of curative effect of sepsis or identification of non-infectious inflammation and infectious inflammation.
  • the infection diagnostic efficiency includes the diagnostic efficiency for the identification of common infection and severe infection.
  • the infection flag parameter set of the present application when only set for a certain infection status assessment, such as only for the identification of severe infection, it can be used for each infection according to the diagnostic effectiveness of the infection status assessment, such as the identification of severe infection.
  • Flag parameter group configuration priority when the infection flag parameter set of the present application is only set for a certain infection status assessment, such as only for the identification of severe infection, it can be used for each infection according to the diagnostic effectiveness of the infection status assessment, such as the identification of severe infection. Flag parameter group configuration priority.
  • the processor 140 may be further configured to: configure priority for each infection flag parameter group according to the area ROC_AUC enclosed by the ROC curve and the horizontal coordinate axis of each infection flag parameter group, wherein the greater the ROC_AUC, The corresponding infection flag parameter group has higher priority.
  • the ROC curve is a receiver operating characteristic curve drawn on the ordinate of the true positive rate and the abscissa of the false positive rate
  • the ROC_AUC of each infection marker parameter group can reflect the infection diagnostic efficacy of the infection marker parameter group.
  • the parameter stability includes at least one of numerical repeatability, aging stability, temperature stability, and machine-to-machine consistency.
  • numerical repeatability refers to the consistency of the values of the infection marker parameter groups used when the same instrument is used in the same environment to perform multiple repeated tests on the same blood sample to be tested in a short period of time
  • aging stability is Refers to the stability of the value of the infection marker parameter set used when the same instrument is used to detect the same blood sample at different time points in the same environment
  • temperature stability refers to the use of the same instrument under different temperature environments.
  • the stability of the value of the infection marker parameter group used refers to that when the same blood sample to be tested is tested on different instruments in the same environment, Consistency of values for the infection flags parameter set used.
  • the higher the stability of the value of the infection marker parameter set used that is, the smaller the fluctuation of the value
  • the higher the aging stability the higher the priority of the infection flag parameter group.
  • the higher the stability of the value of the infection marker parameter set used that is, the smaller the fluctuation of the value
  • the parameter limitations refer to the range of subjects to which the infection marker parameters are applicable. In some examples, if the scope of subjects to which the infection flag parameter set is applicable is larger, it means that the parameters of the infection flag parameter set are less limited, and accordingly, the priority of the infection flag parameter set is higher.
  • the priorities of the plurality of infection marker parameter sets acquired by the processor 140 are preset, for example, preset according to at least one of infection diagnosis efficacy, parameter stability, and parameter limitation.
  • the processor 140 may configure a priority for each infection flag parameter group according to the preset.
  • the priorities of the multiple infection flag parameter sets may be pre-stored in a memory, and the processor 140 may recall the priorities of the multiple infection flag parameter sets from the memory.
  • the inventors of the present application have found through research that there may be abnormal classification results and/or abnormal cells in the blood sample of the subject, which makes the infection marker parameter set used unreliable. Therefore, the blood analyzer provided by the present application can calculate the credibility of the obtained multiple infection marker parameter groups, so as to screen out the More robust set of infection flag parameters.
  • the processor 140 may be configured to calculate the credibility of each infection flag parameter set as follows:
  • the reliability of the infection marker parameter set is calculated according to the classification result of at least one target particle cluster used to obtain the infection marker parameter set and/or according to the abnormal cells in the blood sample to be tested.
  • the classification results may include the count value of the target cluster, the percentage of the count value of the target cluster and another cluster, the degree of overlap between the target cluster and its adjacent clusters (also referred to as adhesion). at least one of the degrees).
  • the degree of overlap between a target cluster and its neighbors may be determined by the distance between the center of gravity of the target cluster and the centers of gravity of its neighbors.
  • the infection flag parameter set obtained through the relevant parameters of the target particle cluster Possibly unreliable, so the confidence level for this set of infection flags parameters is low.
  • the processor 140 may be configured to calculate the credibility of all the infection flag parameter groups in the plurality of infection flag parameter groups once, and then calculate the The credibility selects at least one infection flag parameter set therefrom and outputs its parameter value.
  • the processor 140 may be configured to perform the following steps to screen the infection flag parameter set and output its parameter value:
  • the parameter value of the infection flag parameter set is output and the calculation and judgment are stopped.
  • the processor 140 may be further configured to: when the parameter value of the selected infection flag parameter set is greater than the infection positive threshold, output an alarm prompt.
  • normalization processing may be performed on each infection flag parameter group to ensure that the infection positive thresholds of each infection flag parameter are consistent.
  • the processor 140 may also be configured to acquire a plurality of parameters of at least one target particle cluster in the measurement sample from the optical information,
  • the processor may be further configured to:
  • For each infection marker parameter set calculate the reliability of the infection marker parameter set according to the classification result of at least one target particle cluster used to obtain the infection marker parameter set and/or according to the abnormal cells in the blood sample to be tested .
  • the classification result may include, for example, at least one of the count value of the target cluster, the count value percentage of the target cluster and another cluster, and the degree of overlap between the target cluster and its adjacent clusters.
  • processor is further configured to:
  • the processor 140 may also be configured to determine whether there is an abnormality affecting the evaluation of the infection state in the blood sample to be tested according to the optical information; The information captures infection marker parameters that match the abnormality and are used to assess the infection status of the subject.
  • the optical information can be acquired to exclude Multiple parameters of cell masses other than monocyte mass and neutrophil mass (e.g., lymphocyte mass) and deriving infection markers for assessing infection status of a subject from multiple parameters of other cell mass parameter.
  • a plurality of cell clusters other than cell clusters affected by the blast cells can be obtained from the optical information. parameters, and obtain the infection marker parameters for evaluating the infection status of the subject from the multiple parameters of other cell clusters.
  • the processor may be further configured to:
  • the white blood cell count of the measurement sample is obtained based on the optical information, and when the white blood cell count is less than a predetermined Outputting a retest instruction for remeasurement of the subject's blood sample when a threshold is set, wherein the measured amount of the sample based on the measurement of the retest instruction is greater than the measured amount of the sample for the measurement used to obtain the optical information; as well as
  • the processor is further configured to obtain at least another leukocyte characteristic parameter of at least another target particle cluster from the optical information measured based on the retest instruction, and to obtain at least another leukocyte characteristic parameter for evaluating Infection marker parameters of the subject's infection status.
  • This application also provides another blood analyzer, including a measuring device and a controller:
  • a measurement device for preparing a measurement sample by mixing a subject's blood sample to be tested, a hemolyzing agent, and a staining agent, and optically measuring the measurement sample to obtain optical information of the measurement sample;
  • a controller configured to: receive a mode setting instruction,
  • control the measurement device When the mode setting instruction indicates that the blood routine detection mode is selected, control the measurement device to perform optical measurement on the measurement sample of the first measurement amount, so as to obtain the optical information of the measurement sample, and obtain and obtain the optical information based on the optical information. output the blood routine parameters of the determination sample,
  • the measurement device When the mode setting instruction indicates that the sepsis detection mode is selected, controlling the measurement device to perform optical measurement on a measurement sample of a second measurement amount greater than the first measurement amount, so as to obtain optical information of the measurement sample, obtaining at least one white blood cell characteristic parameter of at least one target particle cluster in the assay sample from the optical information, obtaining an infection marker parameter for assessing the infection status of the subject based on the at least one white blood cell characteristic parameter, and outputting the infection flag parameter.
  • the sample analyzer can be controlled to perform a retest action, so as to obtain more accurate infection marker parameters for evaluating the subject infection status.
  • the embodiment of the present application also proposes a method for prompting the infection status of the subject.
  • the method 200 includes the following steps:
  • the method 200 proposed in the embodiment of the present application is especially implemented by the above-mentioned blood cell analyzer 100 proposed in the embodiment of the present application.
  • the method may further comprise: identifying nucleated red blood cells in the assay sample based on the optical information to obtain a count of nucleated red blood cells.
  • the at least one target particle cluster can be selected from one or more of leukocyte clusters, neutrophil clusters, and lymphocyte clusters; preferably, the at least one target particle cluster includes leukocyte clusters and/or neutral cell clusters. granulocyte clusters.
  • the infection marker parameter can be selected from one of the following cell characteristic parameters or can be obtained by combining multiple cell characteristic parameters in the following cell characteristic parameters, especially by combining linear functions:
  • the area of the distribution area of the neutrophil cluster in the two-dimensional scatter diagram generated based on the two light intensities of the forward scattered light intensity, the side scattered light intensity and the side fluorescence intensity, and the distribution area of the neutrophil cluster by The volume of the distribution area in the three-dimensional scatter diagram generated by forward scattered light intensity, side scattered light intensity and fluorescence intensity;
  • evaluating the infection status of the subject according to the infection marker parameters may include: performing early prediction of sepsis, diagnosis of sepsis, common infection and severe infection based on the infection marker parameters Identification, infection monitoring, prognosis analysis of sepsis, identification of bacterial infection and viral infection, or identification of non-infectious inflammation and infectious inflammation.
  • step S260 may include: when the infection marker parameter satisfies the first preset condition, outputting an indication that the subject may develop infection within a certain period of time after the blood sample to be tested is collected. Sepsis reminder information; preferably, the certain period of time is not more than 48 hours, especially within 24 hours.
  • step S260 may include: outputting prompt information indicating that the subject suffers from sepsis when the infection marker parameter satisfies a second preset condition.
  • step S260 may include: when the infection flag parameter satisfies a third preset condition, outputting prompt information indicating that the subject suffers from severe infection.
  • step S260 may include: monitoring the development of the subject's infection condition according to the infection marker parameters.
  • monitoring the development of the subject's infection condition according to the infection marker parameters includes:
  • Whether the condition of the subject is getting better is judged according to the change trend of the values of the infection marker parameters obtained through the multiple consecutive detections, preferably when the infection markers obtained through the multiple consecutive detections When the value of the parameter decreases gradually, a prompt message indicating that the condition of the subject is getting better is output.
  • monitoring the development of the subject's infection condition according to the infection marker parameters includes:
  • the subject is monitored for progression based on a comparison of a previous value of the infection marker parameter to a first threshold and a comparison of the previous value of the infection marker parameter to a current value of the infection marker parameter.
  • step S260 may include: judging whether the sepsis prognosis of the subject is good according to the infection marker parameters. For example, when the infection marker parameter satisfies the fourth preset condition, output prompt information indicating that the subject has a good prognosis for sepsis
  • step S260 may include: judging whether the subject's infection type is a viral infection or a bacterial infection according to the infection marker parameters.
  • step S260 may include: judging whether the subject suffers from infectious inflammation or non-infectious inflammation according to the infection marker parameters. For example, when the infection marker parameter satisfies the fifth preset condition, a prompt message indicating that the subject suffers from infectious inflammation is output.
  • the method may further include: when the preset characteristic parameter of the target particle cluster satisfies a sixth preset condition, for example, when the total number of particles of the target particle cluster is less than a preset threshold, and/or or when the target particle cluster overlaps with other particle clusters, do not output the value of the infection flag parameter, or output the value of the infection flag parameter and at the same time output a prompt message indicating that the value of the infection flag parameter is unreliable .
  • a sixth preset condition for example, when the total number of particles of the target particle cluster is less than a preset threshold, and/or or when the target particle cluster overlaps with other particle clusters
  • the method may further comprise: when the subject suffers from a blood disease or there are abnormal cells, especially primitive cells, in the blood sample to be tested, for example, when according to the optical information
  • the value of the parameter of the infection marker is not output, or the value of the parameter of the marker of infection is output and at the same time it is output indicating that the value of the parameter of the marker of infection is unreliable Prompt information.
  • the embodiment of the present application also proposes the use of infection marker parameters in evaluating the infection status of a subject, wherein the infection marker parameters are obtained by the following method:
  • An infection marker parameter is calculated based on the at least one white blood cell characteristic parameter.
  • true positive rate %, false positive rate %, true negative rate % and false negative rate % of the embodiment of the present application are calculated by the following formula:
  • TP is the number of true positive individuals
  • FP is the number of false positive individuals
  • TN is the number of true negative individuals
  • FN is the number of false negative individuals.
  • the inclusion criteria of these 152 donors adult ICU patients with existing or suspected acute infection. Exclusion criteria: pregnant women, myelosuppressed patients undergoing chemotherapy, patients treated with immunosuppressants, and patients with hematological diseases.
  • the donors of the sepsis samples have suspicious or definite infection sites, positive laboratory culture results, and organ failure; suspected or confirmed acute infections, and SOFA ⁇ 2 points, and suspected infections are those with the following 1 ⁇ 3 Any item and 4 have no definite results; or have any of the following 1 ⁇ 3 and 5.
  • Table 7 shows the infection marker parameters used and their corresponding diagnostic efficacy
  • FIG. 13 and FIG. 14 show the ROC curves corresponding to the infection marker parameters in Table 7.
  • Combination parameter 1 0.00174639 ⁇ N_WBC_FL_P+0.00788254 ⁇ N_WBC_FS_W-10.4569;
  • Combination parameter 2 0.00160514 ⁇ N_WBC_SS_W+0.00480886 ⁇ N_WBC_FS_W-6.62685;
  • Combination parameter 3 0.00278754 ⁇ N_WBC_FL_W+0.00010201 ⁇ N_NEU_FLSS_Area.
  • Table 7 The efficacy of different infection marker parameters for early prediction of sepsis risk
  • D_Neu_SS_W refers to the distribution width of the side scattered light intensity of the neutrophil cluster in the DIFF channel scatter diagram
  • D_Neu_FL_W refers to the distribution width of the fluorescence intensity of the neutrophil cluster in the DIFF channel scatter diagram
  • D_Neu_FS_W refers to the distribution width of the forward scattered light intensity of the neutrophil cluster in the DIFF channel scattergram.
  • the infection marker parameters proposed in this application can be used to predict the risk of sepsis more effectively one day in advance, and can predict the risk of sepsis one day in advance when the patient does not have symptoms of sepsis. It will develop into sepsis, and its diagnostic efficacy is better than that of the existing PCT standard. Surprisingly, the characteristics of the scattergram of the WNB channel using blood routine are better than the characteristics of the scattergram of the DIFF channel. diagnostic performance.
  • the function of the DIFF channel is the four classifications of leukocytes, which can more accurately distinguish various leukocyte subsets, and it is easier to find infection-related features in the scatter plot data, while the hemolysis intensity of the WNB channel is relatively weak, and different types of leukocytes Subpopulations are not as discriminative as DIFF channels, and it is not easy to find features associated with infection.
  • the WNB channel can find more useful features than the DIFF channel to predict the development of sepsis.
  • the inventor speculates that after the cells are treated with the reagents of the WNB channel, the Related monocytes, immature granulocytes, and atypical lymphocytes are all distributed in the positions where the fluorescence signal is relatively strong and the side scattered light signal is relatively strong in the scatter plot. After the patient is infected, the number and position of these cells in the scatter diagram will change significantly, while other cells not related to the infection will not change significantly, so the changes in the scatter diagram of the WNB channel after infection will be more significant, And it is easier to be captured by the detection device.
  • Inclusion criteria of 1548 donors in this example adult ICU patients with existing or suspected acute infection. Exclusion criteria: pregnant women, myelosuppressed patients undergoing chemotherapy, patients treated with immunosuppressants, and patients with hematological diseases.
  • Table 9 shows the infection marker parameters used and their corresponding diagnostic powers, and FIG. 15 and FIG. 16 show the ROC curves corresponding to the infection marker parameters in Table 9.
  • Table 9 shows the infection marker parameters used and their corresponding diagnostic powers, and FIG. 15 and FIG. 16 show the ROC curves corresponding to the infection marker parameters in Table 9.
  • Combination parameter 1 0.003755 ⁇ N_WBC_FL_P+0.009192 ⁇ N_WBC_FS_W-15.0973;
  • Combination parameter 2 0.005945 ⁇ N_WBC_FL_W+0.000248 ⁇ N_NEU_FL_P-6.62685;
  • Combination parameter 3 0.005249 ⁇ N_WBC_SS_P+0.005132 ⁇ N_NEU_FL_W ⁇ 13.216.
  • True positive means that the prompt result learned in this embodiment matches the clinical condition of the patient and they are all patients with severe infection; false positive means that the prompt result learned in this embodiment is severe infection, but the actual condition of the patient is common infection;
  • the prompt results obtained in the embodiment are consistent with the clinical conditions of the patients, and they are all patients with common infections; false negatives mean that the prompt results obtained in the examples are common infections, but the actual condition of the patients is severe infection.
  • Table 10 shows the effectiveness of using other individual white blood cell characteristic parameters as infection marker parameters in this embodiment to identify common infections and severe infections
  • Tables 11-1 to 11-4 show the effectiveness of using other combination parameters in this embodiment
  • N_NEU_FL_P 0.8106 >1599.2285 23.9 74 76.1 26 N_NEU_FL_W 0.8079 >1360 25 71.3 75 28.7 N_NEU_FL_P 0.8013 >1715.2215 28.4 76.8 71.6 23.2 N_NEU_FLFS_Area 0.7859 >7459.84 21.1 66.7 78.9 33.3 N_WBC_SS_W 0.7821 >1328 23.6 70.1 76.4 29.9 N_WBC_FS_W 0.7786 >944 30.8 72.8 69.2 27.2 N_NEU_FS_W 0.7705 >624 30.3 68.1 69.7 31.9 N_WBC_FLSS_Area 0.7651 >12835.84 28.8 69.2 71.2 30.8 N_NEU_SS_W 0.7618 >1168 28.4
  • the prior art has been reported (Crouser E, Parrillo J, Seymour C et al. Improved Early Detection of Sepsis in the ED With a Novel Monocyte Distribution Width Biomarker. CHEST.2017; 152(3):518-526), in BCI
  • the blood routine scattergram of the DIFF channel of the blood analyzer uses the distribution width of neutrophils to distinguish between common infection and severe infection.
  • the ROC_AUC is 0.79, the judgment threshold is >20.5, the false positive rate is 27%, and the true positive rate is 77.0%. , the true negative rate was 73%, and the false negative rate was 23%. From the reported data, it is similar to Mindray's DIFF channel in distinguishing common infection from severe infection.
  • Embodiment 3 sepsis diagnosis
  • Table 12 shows the infection marker parameters used and their corresponding diagnostic powers, and FIGS. 17 and 18 show the ROC curves corresponding to the infection marker parameters in Table 12.
  • Table 12 shows the infection marker parameters used and their corresponding diagnostic powers, and FIGS. 17 and 18 show the ROC curves corresponding to the infection marker parameters in Table 12.
  • Table 12 shows the infection marker parameters used and their corresponding diagnostic powers, and FIGS. 17 and 18 show the ROC curves corresponding to the infection marker parameters in Table 12.
  • Combination parameter 1 0.004088 ⁇ N_WBC_FL_P+0.009059 ⁇ N_WBC_FS_W;
  • Combination parameter 2 0.006086 ⁇ N_WBC_FL_W-0.00017 ⁇ N_NEU_FL_W;
  • Combination parameter 3 0.007722 ⁇ N_WBC_SS_P+0.003547 ⁇ N_WBC_FL_P.
  • Table 12 The effectiveness of different infection marker parameters in the diagnosis of sepsis
  • Table 13 shows the effectiveness of using other individual white blood cell characteristic parameters as infection marker parameters in this embodiment for the diagnosis of sepsis
  • Table 14 shows the use of other parameter combinations in this embodiment as infection marker parameters for diagnosis
  • Table 15 shows the used infection marker parameters and their corresponding experimental data (the average value of the infection marker parameter values of two groups of patients), and Fig. 19 shows the dynamic trend change diagram that adopts single parameter N_WBC_FL_P to monitor as the infection marker parameter, Fig. 20 Shows the dynamic trend change chart adopting single parameter N_WBC_FS_W as the infection flag parameter to monitor, and Fig.
  • N_WBC_FL_P*0.003755+N_WBC_FS_W*0.009192 the horizontal axis is the number of days after the diagnosis of severe infection
  • the average value of the infection marker parameter values of the two groups of patients is the vertical axis.
  • Embodiment 5 sepsis condition monitoring
  • Embodiment 6 sepsis prognosis analysis
  • Example 2 Using the BC-6800Plus blood cell analyzer produced by Shenzhen Mindray Biomedical Electronics Co., Ltd. according to the steps of Example 1 of this application, 270 blood samples were tested, and the prognosis of sepsis was analyzed based on the aforementioned method based on the scatter plot. Among them, 68 positive samples died within 28 days, and 202 negative samples survived within 28 days.
  • Table 17 shows the effectiveness of using a single white blood cell characteristic parameter as an infection marker parameter in this embodiment to judge whether the prognosis of sepsis is good
  • Table 18 shows the use of parameter combinations in this embodiment as an infection marker parameter for judging sepsis Whether the prognosis of toxicity is good or not
  • N_WBC_FL_W 0.7964 >2128 21.3 67.6 78.7 32.4 N_WBC_FS_W 0.7371 >1040 26.7 70.6 73.3 29.4 N_WBC_FLSS_Area 0.7118 >14494.72 39.1 70.6 60.9 29.4 N_WBC_FS_CV 0.7073 >0.7875 32.7 66.2 67.3 33.8 N_WBC_FLFS_Area 0.7033 >10726.4 30.2 63.2 69.8 36.8
  • Example 1 of this application Using the BC-6800Plus blood cell analyzer produced by Shenzhen Mindray Biomedical Electronics Co., Ltd. according to the steps in Example 1 of this application, 491 blood samples were tested, and the infection type was judged by the aforementioned method based on the scatter plot. Among them, there were 237 cases of bacterial infection samples (ie positive samples), and 254 cases of virus infection samples.
  • Inclusion criteria for these cases adult ICU patients with existing or suspected acute infection. Exclusion criteria: pregnant women, myelosuppressed patients undergoing chemotherapy, patients treated with immunosuppressants, and patients with hematological diseases.
  • the bacterial infection sample there is a suspicious or definite infection site, and the bacterial culture result in the laboratory is positive, that is, it meets 1-3 at the same time
  • the virus-infected sample there is a suspicious or definite infection site, and the virus antigen or antibody test is positive.
  • the virus antigen or antibody test is positive.
  • Table 19 shows the effectiveness of using a single white blood cell characteristic parameter as an infection marker parameter for distinguishing bacterial infection and viral infection in this embodiment
  • WNB channel parameters have similar or even better diagnostic and therapeutic efficacy than PCT in the identification of bacterial infections. It also has better diagnostic performance in the differential diagnosis of infection.
  • the BC-6800Plus blood cell analyzer produced by Shenzhen Mindray Biomedical Electronics Co., Ltd. was used to detect 515 blood samples according to the steps of Example 1 of the present application, and the aforementioned method was used to identify infectious inflammation based on the scatter plot. Among them, 399 were infectious inflammation samples, that is, positive samples, and 116 were non-infectious inflammation samples, that is, negative samples.
  • Inclusion criteria for these cases adult ICU patients with present or suspected acute inflammation.
  • Exclusion criteria pregnant women, myelosuppressed patients undergoing chemotherapy, patients treated with immunosuppressants, and patients with hematological diseases.
  • infectious inflammation sample there is evidence of bacterial and/or viral infection; and there is inflammation (any one of the following can be met)
  • Tissue damage damage caused by physical or chemical factors such as high temperature, low temperature, radioactive substances and ultraviolet rays, etc.
  • Tissue necrosis tissue necrosis damage caused by ischemia or hypoxia
  • the non-infectious inflammatory sample inflammatory response caused by physical, chemical and other factors, satisfying both 1 and 2:
  • Tissue damage damage caused by physical or chemical factors such as high temperature, low temperature, radioactive substances and ultraviolet rays, etc.
  • Tissue necrosis tissue necrosis damage caused by ischemia or hypoxia
  • Table 21 shows the effectiveness of using a single white blood cell characteristic parameter as an infection marker parameter in this embodiment for judging infectious inflammation
  • Table 22-1 shows the use of parameter combinations in this embodiment as an infection marker parameter for judging infectivity
  • Table 22-1 The effectiveness of dual parameters for differentiating infectious and non-infectious inflammation
  • WNB channel parameters have similar or even better diagnostic efficacy than PCT in differentiating infectious inflammation from non-infectious inflammation; It has a better diagnostic performance in the identification of infectious inflammation and non-infectious inflammation.
  • N_FL_PULWID_MEAN refers to the average value of the pulse width of the side fluorescence signal of the particles in the white blood cell cluster in the WNB channel scatter diagram
  • N_FS_PULWID_MEAN refers to the pulse width of the forward scattered light signal of the particles in the white blood cell cluster in the WNB channel scatter diagram Average value
  • N_SS_PULWID_MEAN refers to the average value of the pulse width of the side scattered light signal of the particles in the WNB channel scatter diagram of white blood cell clusters
  • N_WBC_FL_R refers to the right boundary value of the WNB channel scatter diagram of white blood cell cluster lateral fluorescence intensity distribution (such as Figure 6).
  • Fig. 24A-Fig. 24D visually show the detection results of using N_WBC_FL_P as a single parameter for the curative effect on sepsis.
  • Figure 25A- Figure 25D visually show the detection results of using N_FL_PULWID_MEAN as a single parameter for the efficacy of sepsis.
  • Fig. 26A-Fig. 26D visually show the detection results of the curative effect on sepsis using N_FS_PULWID_MEAN as a single parameter.
  • Table 24 shows that in this embodiment, the combination of the two parameters "N_WBC_FL_P" and “N_WBC_FS_W” is used as the infection marker parameter to judge the curative effect on sepsis.
  • the physical meaning of the combination of the two parameters is to combine the center of gravity position of the nucleic acid content inside the WBC particles of the first detection channel with the distribution width of the WBC particle volume of the first detection channel.
  • the two-argument combination passes through the function
  • Y 0.0040875 ⁇ N_WBC_FL_P+0.00905881 ⁇ N_WBC_FS_W ⁇ 16.60028217 to calculate the infection flag parameter, where Y represents the infection flag parameter.
  • Fig. 27A-Fig. 27D visually show the detection results of the curative effect on sepsis using the combination of the double parameters "N_WBC_FL_P" and “N_WBC_FS_W” as the infection marker parameters.
  • Table 25 shows that in this embodiment, the combination of the two parameters "N_WBC_FL_W” and “N_WBC_FS_P” is used as the infection marker parameter to judge the curative effect on sepsis.
  • the physical meaning of the combination of the two parameters is to combine the distribution width of the nucleic acid content inside the WBC particle of the first detection channel with the position of the center of gravity of the WBC particle volume of the first detection channel.
  • the two-argument combination passes through the function
  • Y 0.00609253 ⁇ N_WBC_FL_W+0.00587667 ⁇ N_WBC_FS_P ⁇ 20.07103538 to obtain the infection flag parameter, where Y represents the infection flag parameter.
  • Fig. 28A-Fig. 28D visually show the detection results of the curative effect on sepsis using the combination of the double parameters "N_WBC_FL_W” and “N_WBC_FS_P" as the infection marker parameters.
  • Table 26 shows that in this embodiment, the combination of the two parameters "N_WBC_FL_P" and “N_WBC_FS_CV” is used as the infection marker parameter to judge the curative effect on sepsis.
  • the physical meaning of the combination of the two parameters is to combine the central position of the nucleic acid content inside the WBC particle of the first detection channel and the degree of dispersion of the volume of the WBC particle of the first detection channel.
  • the two-argument combination passes through the function
  • Y 0.00462573 ⁇ N_WBC_FL_P+12.43796108 ⁇ N_WBC_FS_CV-18.03119401 to obtain the infection flag parameter, where Y represents the infection flag parameter.
  • Fig. 29A-Fig. 29D visually show the detection results of the curative effect on sepsis using the combination of the two parameters "N_WBC_FL_P" and “N_WBC_FS_CV" as the infection marker parameters.
  • Table 27 shows that in this embodiment, the combination of DIFF+WNB dual-channel parameters "N_WBC_FL_W” and “D_Neu_FL_W” is used as the infection marker parameter to judge the curative effect on sepsis.
  • the physical meaning of the combination of the two parameters is to combine the distribution width of the nucleic acid content inside the WBC particles in the first detection channel and the distribution width of the nucleic acid content inside the neutrophils in the second detection channel.
  • the two-argument combination passes through the function
  • Y 0.00623272 ⁇ N_WBC_FL_W+0.01806527 ⁇ D_Neu_FL_W ⁇ 16.84312131 to obtain the infection flag parameter, where Y represents the infection flag parameter.
  • Fig. 30A-Fig. 30D visually show the detection results of the curative effect on sepsis using the combination of the double parameters "N_WBC_FL_W” and “D_Neu_FL_W” as the infection marker parameters.
  • Table 28 shows that in this embodiment, the combination of DIFF+WNB dual-channel parameters "N_WBC_FL_W” and “D_Neu_FL_CV” is used as the infection marker parameter to judge the curative effect on sepsis.
  • the physical meaning of the combination of the two parameters is to combine the distribution width of the nucleic acid content inside the WBC particles in the first detection channel and the dispersion degree of the nucleic acid content inside the neutrophils in the second detection channel.
  • the two-argument combination passes through the function
  • Y 0.00688519 ⁇ N_WBC_FL_W+11.27099282 ⁇ D_Neu_FL_CV-19.2998686 to obtain the infection flag parameter, where Y represents the infection flag parameter.
  • Fig. 31A-Fig. 31D visually show the detection results of the curative effect on sepsis using the combination of the double parameters "N_WBC_FL_W” and “D_Neu_FL_CV" as the infection marker parameters.
  • Table 29 shows the infection marker parameters used and their corresponding diagnostic powers
  • FIG. 33 shows the ROC curves corresponding to the infection marker parameters in Table 29.
  • Combination parameter 1 -0.61535116*Mon#+0.00766353*N_WBC_FL_W-15.04738706;
  • Combination parameter 2 -0.03077968*HGB+0.08933918*N_WBC_FL_W-5.72270269;
  • Combination parameter 3 -0.00395999*PLT+0.00606333*N_WBC_FL_W-11.55000862.
  • Table 29 The efficacy of different infection marker parameters in the diagnosis of sepsis

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Abstract

涉及血液细胞分析仪(100)、方法以及感染标志参数的用途。血液细胞分析仪(100)包括用于吸取受试者待测血液样本的吸样装置(110)、用于制备含一部分待测血液样本、溶血剂和用于识别有核红细胞的测定试样的样本制备装置(120)、用于检测测定试样以获得光学信息的光学检测装置(130)和处理器(140)。处理器(140)被配置为：从光学信息获得测定试样中的至少一个目标粒子团的至少一个白细胞特征参数；基于至少一个白细胞特征参数获得用于评估受试者的感染状态的感染标志参数；以及输出感染标志参数。由此能够快速地为用户提供准确有效的感染标志参数，以有效地辅助用户评估受试者的感染状态。

Description

血液细胞分析仪、提示感染状态的方法以及感染标志参数的用途 技术领域
本申请涉及体外诊断领域，尤其是涉及血液细胞分析仪、用于提示受试者的感染状态的方法以及感染标志参数在评估受试者的感染状态中的用途。
背景技术
感染性疾病是临床上常见的疾病，其中脓毒症(Sepsis)属于严重的感染性疾病。脓毒症发生率高，全球每年有超过1800万严重脓毒症病例，并且脓毒症的病情凶险，病死率高，全球每天约14,000人死于其并发症，据国外流行病学调查显示，脓毒症的病死率已经超过心肌梗死，成为重症监护病房内非心脏病患者死亡的主要原因。近年来，尽管抗感染治疗和器官功能支持技术取得了长足的进步，脓毒症的病死率仍高达30％～70％。脓毒症治疗花费高，医疗资源消耗大，严重影响人类的生活质量，已经对人类健康造成巨大威胁。临床医生需要及时诊断患者是否发生感染，并查找病原体，才能制定有效治疗方案。因此如何快速、早期筛查和诊断感染性疾病成为了临床实验室迫切需要解决的问题。
针对感染性疾病的快速鉴别诊断，业界现有解决方案包括：微生物培养，C反应蛋白(c-reactive protein,CRP)、降钙素原(procalcitonin,PCT)和血清淀粉样蛋白A(serum amyloid A,SAA)等炎症标志物，血清抗原抗体检测和血常规检测。
微生物培养被认为是最可靠的金标准，能直接培养检测出体液或血液等临床标本中的细菌，从而判读其类型和耐药性，可直接指导临床用药。但该方法报告周期长、标本易受污染且假阴性率高，不能很好的满足临床快速准确出结果的要求。
由于炎症因子如CRP、PCT和SAA等有较好的灵敏度，被广泛应用于感染性疾病的辅助诊断。但这些检测对感染性疾病的特异性较弱，且通常需要进行CRP、PCT和SAA这三项的联合检测，增加了患者的经济负担，并且CRP和PCT受特定疾病所干扰，因而有时不能正确反映患者的感染状态。例如，CRP生成于肝脏，肝损伤的感染患者CRP水平正常，会在感染性疾病的诊断中出现假阴性结果。
血清抗原抗体检测能确认特定的病毒类型，但对病原体种类不明确的情境下，作用有限，且检测费用高，增加了患者的经济负担。
血常规检测能够在一定程度上提示感染发生和感染类型鉴别。但血常规结果中的白细胞(White Blood Cell，简写为“WBC”)\中性粒细胞(Neu)％等受多方面影响，如容易受其他非感染性炎症反应、机体正常生理波动等影响，不能准确及时地反映患者病情，在感染性疾病中的诊疗价值不佳。
发明内容
为了解决上述技术问题，本申请的目的之一在于提供一种能够低成本且快速地评估受试者的感染状态的解决方案，在该解决方案中利用血液细胞分析仪开发出新的血细胞形态参数对受试者的感染状态进行评估，包括进行脓毒症早期预测、脓毒症诊断、普通感染与重症感染的鉴别、感染病情监控、脓毒症预后分析、细菌感染和病毒感染的鉴别、或者非感染性炎症和感染性炎症的鉴别、脓毒症疗效的评估。
此外，该解决方案不需要额外的测试成本，在利用现有血液细胞分析仪进行血常规检测的同时就能够实现感染状态的评估。
为了实现本申请的上述目的，本申请第一方面提供一种血液细胞分析仪，其包括：
吸样装置，用于吸取受试者的待测血液样本；
样本制备装置，用于制备含有所述待测血液样本的一部分、溶血剂和用于识别有核红细胞的染色剂的测定试样；
光学检测装置，包括流动室、光源和光检测器，所述流动室用于供所述测定试样通过，所述光源用于用光照射通过所述流动室的测定试样，所述光检测器用于检测所述测定试样在通过所述流动室时被光照射后所产生的光学信息；以及
处理器，被配置为：
从所述光学信息计算所述测定试样中的至少一个目标粒子团的至少一个白细胞特征参数；
基于所述至少一个白细胞特征参数获得用于评估所述受试者的感染状态的感染标志参数；并且
输出所述感染标志参数。
为了实现本申请的上述目的，本申请第二方面提供一种用于提示受试者的感染状态的方法，所述方法包括以下步骤：
获取所述受试者的待测血液样本；
制备含有所述待测血液样本的一部分、溶血剂和用于识别有核红细胞的染色剂的测定试样；
使所述测定试样中的粒子逐个通过被光照射的流动室的光学检测区，以获得所述测定试样中的粒子在被光照射后所产生的光学信息；
从所述光学信息计算所述测定试样中的至少一个目标粒子团的至少一个白细胞特征参数；
基于所述至少一个白细胞特征参数获得感染标志参数；并且
根据所述感染标志参数提示所述受试者的感染状态。
为了实现本申请的上述目的，本申请第三方面还提供感染标志参数在评估受试者的感染状态中的用途，其中，所述感染标志参数通过包含如下步骤的方法获得：
获取通过流式细胞术对测定试样检测得到的至少一个目标粒子团的至少一个白细胞特征参数，所述测定试样含有来自受试者的待测血液样本、溶血剂和用于识别有核红细胞的染色剂；并且
基于所述至少一个白细胞特征参数获得感染标志参数。
在本申请各方面提供的技术方案中，可以从用于识别有核红细胞的检测通道中获得包括细胞特征参数的白细胞特征参数，由此能够实现快速、准确且高效地辅助医生进行感染性疾病的预测或诊断。尤其是，基于该感染标志参数能够有效地提供提示受试者感染状态的提示信息。
附图说明
图1为根据本申请一些实施例的血液细胞分析仪的结构示意图。
图2为根据本申请一些实施例的光学检测装置的结构示意图。
图3为根据本申请一些实施例的测定试样的FL-FS二维散点图。
图4为根据本申请一些实施例的测定试样的SS-FS二维散点图。
图5为根据本申请一些实施例的测定试样的FL-SS-FS三维散点图。
图6示出根据本申请一些实施例的测定试样中的白细胞团的细胞特征参数。
图7为根据本申请一些实施例判断患者病情发展的示意性流程图。
图8、图9、图10为根据本申请一些实施例的测定试样的存在异常情况的散点图。
图11示出根据本申请一些实施例的取对数处理前后的散点图。
图12为根据本申请一些实施例的用于提示受试者的感染状态的方法的示意性流程图。
图13-图14为根据本申请一些实施例的在脓毒症早期预测场景下的ROC曲线。
图15-图16为根据本申请一些实施例的在重症感染鉴别场景下的ROC曲线。
图17-图18为根据本申请一些实施例的在脓毒症诊断场景下的ROC曲线。
图19、图20和图21为根据本申请一些实施例的用于监控重症感染病情发展的感染标 志参数的数值变化曲线图。
图22和图23为根据本申请一些实施例的用于监控脓毒症病情发展的感染标志参数的数值变化曲线图。
图24A－图24D直观地显示了使用N_WBC_FL_P作为单参数对脓毒症疗效的检测结果。图24A显示了有效组和无效组中每个患者使用抗生素治疗前和治疗5天后的N_WBC_FL_P测定值。图24B显示了有效组和无效组中患者的盒须图(box and whisker plot)。图24C显示了有效组在抗生素治疗前和治疗5天后的N_WBC_FL_P测定值均值的比较，以及无效组在抗生素治疗前和治疗5天后的N_WBC_FL_P测定值均值的比较。图24D显示了使用N_WBC_FL_P作为单参数对脓毒症疗效的检测的ROC曲线。
图25A－图25D直观地显示了使用N_FL_PULWID_MEAN作为单参数对脓毒症疗效的检测结果。图25A显示了有效组和无效组中每个患者使用抗生素治疗前和治疗5天后的N_FL_PULWID_MEAN测定值。图25B显示了有效组和无效组中患者的盒须图。图25C显示了有效组在抗生素治疗前和治疗5天后的N_FL_PULWID_MEAN测定值均值的比较，以及无效组在抗生素治疗前和治疗5天后的N_FL_PULWID_MEAN测定值均值的比较。图25D显示了使用N_FL_PULWID_MEAN作为单参数对脓毒症疗效的检测的ROC曲线。
图26A－图26D直观地显示了使用N_FS_PULWID_MEAN作为单参数对脓毒症疗效的检测结果。图26A显示了有效组和无效组中每个患者使用抗生素治疗前和治疗5天后的N_FS_PULWID_MEAN测定值。图26B显示了有效组和无效组中患者的盒须图。图26C显示了有效组在抗生素治疗前和治疗5天后的N_FS_PULWID_MEAN测定值均值的比较，以及无效组在抗生素治疗前和治疗5天后的N_FS_PULWID_MEAN测定值均值的比较。图26D显示了使用N_FS_PULWID_MEAN作为单参数对脓毒症疗效的检测的ROC曲线。
图27A－图27D直观地显示了使用双参数“N_WBC_FL_P”和“N_WBC_FS_W”组合作为感染标志参数对脓毒症疗效的检测结果。图27A显示了有效组和无效组中每个患者使用抗生素治疗前和治疗5天后的该双参数组合测定值。图27B显示了有效组和无效组中患者的盒须图。图27C显示了有效组在抗生素治疗前和治疗5天后的该双参数组合均值的比较，以及无效组在抗生素治疗前和治疗5天后的该双参数组合均值的比较。图27D显示了使用该双参数组合对脓毒症疗效的检测的ROC曲线。
图28A－图28D直观地显示了使用双参数“N_WBC_FL_W”和“N_WBC_FS_P”组合作为感染标志参数对脓毒症疗效的检测结果。图28A显示了有效组和无效组中每个患者使用抗生素治疗前和治疗5天后的该双参数组合测定值。图28B显示了有效组和无效组中患者的盒须图。图28C显示了有效组在抗生素治疗前和治疗5天后的该双参数组合均值的比较，以及无效组在抗生素治疗前和治疗5天后的该双参数组合均值的比较。图28D显示了使用该双参数组合对脓毒症疗效的检测的ROC曲线。
图29A－图29D直观地显示了使用双参数“N_WBC_FL_P”和“N_WBC_FS_CV”组合作为感染标志参数对脓毒症疗效的检测结果。图29A显示了有效组和无效组中每个患者使用抗生素治疗前和治疗5天后的该双参数组合测定值。图29B显示了有效组和无效组中患者的盒须图。图29C显示了有效组在抗生素治疗前和治疗5天后的该双参数组合均值的比较，以及无效组在抗生素治疗前和治疗5天后的该双参数组合均值的比较。图29D显示了使用该双参数组合对脓毒症疗效的检测的ROC曲线。
图30A－图30D直观地显示了使用双参数“N_WBC_FL_W”和“D_Neu_FL_W”组合作为感染标志参数对脓毒症疗效的检测结果。图30A显示了有效组和无效组中每个患者使用抗生素治疗前和治疗5天后的该双参数组合测定值。图30B显示了有效组和无效组中患者的盒须图。图30C显示了有效组在抗生素治疗前和治疗5天后的该双参数组合均值的比较，以及无效组在抗生素治疗前和治疗5天后的该双参数组合均值的比较。图30D显示了使用该双参数组合对脓毒症疗效的检测的ROC曲线。
图31A－图31D直观地显示了使用双参数“N_WBC_FL_W”和“D_Neu_FL_CV”组合作为感染标志参数对脓毒症疗效的检测结果。图31A显示了有效组和无效组中每个患者使用抗生素治疗前和治疗5天后的该双参数组合测定值。图31B显示了有效组和无效组中患者的盒须图。图31C显示了有效组在抗生素治疗前和治疗5天后的该双参数组合均值的比较，以及无效组在抗生素治疗前和治疗5天后的该双参数组合均值的比较。图31D显示了使用该双参数组合对脓毒症疗效的检测的ROC曲线。
图32为根据本申请一些实施例的中性粒细胞群的面积参数D_NEU_FLSS_Area的一种算法计算步骤。
具体实施方式
下面将结合本申请实施例中的附图，对本申请实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例仅仅是本申请的一部分实施例，而不是全部的实施例。基于本申请中的实施例，本领域普通技术人员在没有作出创造性劳动前提下所获得的所有其他实施例，都属于本申请保护的范围。
在整个说明书中，除非另有特别说明，本文使用的术语应理解为如本领域中通常所使用的含义。因此，除非另有定义，本文使用的所有技术和科学术语具有与本申请所属领域技术人员的一般理解相同的含义。若存在矛盾，则本说明书的描述优先。
需要说明的是，在本申请实施例中，术语“包括”、“包含”或者其任何其他变体意在涵盖非排他性的包含，从而使得包括一系列要素的方法或者装置不仅包括所明确记载的要素，而且还包括没有明确列出的其他要素，或者是还包括为实施方法或者装置所固有的要素。在没有更多限制的情况下，由语句“包括一个……”限定的要素，并不排除在包括该要素的方法或者装置中还存在另外的相关要素(例如方法中的步骤或者装置中的单元，这里的单元可以是部分电路、部分处理器、部分程序或软件等等)。
需要说明的是，本申请实施例所涉及的术语“第一\第二\第三”仅仅是区别类似的对象，不代表针对对象的特定排序，可以理解地，“第一\第二\第三”在允许的情况下可以互换特定的顺序或先后次序。应该理解“第一\第二\第三”区分的对象在适当情况下可以互换，以使这里描述的本申请实施例能够以除了在这里图示或描述的那些以外的顺序实施。
需要说明的是，本申请实施例所涉及的术语“至少一个”意指在合理条件下的1个或超过1个，例如2个、3个、4个、5个或10个等。
本申请实施例所涉及的术语“散点图”是由血液细胞分析仪生成的一种二维或三维图，其上分布有多个粒子的二维或三维特征信息，其中散点图的X坐标轴、Y坐标轴和Z坐标轴均表征每个粒子的一种特性，例如在一个例示的散点图中，X坐标轴表征前向散射光强度，Y坐标轴表征荧光强度，Z轴坐标轴表征侧向散射光强度。本公开中使用的术语“散点图”不仅指至少两组数据以数据点的形式在直角坐标系中的分布图，也包括数据阵列，即不受其图形呈现形式的局限。
本申请实施例所涉及的术语“粒子团”或“细胞团”是分布在散点图的某一区域，由具有相同细胞特征的多个粒子形成的粒子群体，例如白细胞(包括所有类型的白细胞)团，以及白细胞亚群、例如中性粒细胞团、淋巴细胞团、单核细胞团、嗜酸性粒细胞团或嗜碱性粒细胞团等。
本申请实施例所涉及的术语“ROC曲线(receiver operator characteristic curve)”是一种受试者工作特征曲线，是根据一系列不同的二分类方式(分界阈值)，以真阳性率为纵坐标，假阳性率为横坐标绘制的曲线，ROC_AUC(area under the curve)代表ROC曲线与水平坐标轴围成的面积。
ROC曲线的制作原理是将连续变量设定出多个不同的临界值，在每个临界值处计算出相应的灵敏度(sensitivity)和特异度(specificity)，再以灵敏度为纵坐标，以1-特异度为横坐标绘制成曲线。
由于ROC曲线是由多个代表各自灵敏度和特异度的临界值构成的，可以借助ROC曲线选择出某一诊断方法最佳的诊断界限值。ROC曲线越是靠近左上角，试验灵敏度越高，误判率越低，则诊断方法的性能越好。可知ROC曲线上最靠近左上角的ROC曲线上的点，其灵敏度和特异度之和最大，这个点或是其邻近点对应的值常被用作诊断参考值(也称为诊断阈值或判断阈值或预设条件或预设范围)。
目前，血液细胞分析仪一般通过DIFF通道和/或WNB通道对白细胞进行计数和分类。其中，血液细胞分析仪通过DIFF通道对白细胞进行白细胞四分类，将白细胞分类为淋巴细胞(Lym)、单核细胞(Mon)、中性粒细胞(Neu)、嗜酸性粒细胞(Eos)四类白细胞。血液细胞分析仪通过WNB通道对有核红细胞进行识别，能够同时得到有核红细胞计数、白细胞计数和嗜碱性粒细胞计数。
本申请所使用的血液细胞分析仪通过结合激光散射法和荧光染色法的流式细胞技术对血液样本中的粒子进行分类和计数。在此，血液细胞分析仪检测血液样本的原理例如可以为：首先吸取血液样本，用溶血剂和荧光染料处理血液样本，其中，红细胞被溶血剂破坏溶解，而白细胞不会被溶解，但荧光染料可在溶血剂的帮助下进入白细胞的细胞核并与细胞核中的核酸物质结合；接着样本中的粒子逐个通过被激光束照射的检测孔，当激光束照射粒子时，粒子本身的特性(如体积、染色程度、细胞内容物大小及含量、细胞核密度等)可阻挡或改变激光束的方向，从而产生与粒子特征相应的各种角度的散射光，这些散射光经信号检测器接收后可以获得粒子结构和组成的相关信息。其中，前向散射光(Forward scatter，FS)反映粒子的数量和体积，侧向散射光(Side scatter，SS)反映细胞内部结构(如细胞内颗粒或细胞核)的复杂程度，荧光(Fluorescence，FL)反映细胞中核酸物质的含量。利用这些光信息可以对样本中的粒子进行分类和计数。
图1为本申请一些实施例的血液细胞分析仪的结构示意图。该血液细胞分析仪100包括吸样装置110、样本制备装置120、光学检测装置130和处理器140。血液细胞分析仪100还具有用于连通吸样装置110、样本制备装置120及光学检测装置130的液路系统，以便在这些装置之间进行液体输送。
吸样装置110用于吸取受试者的待测血液样本。
在一些实施例中，吸样装置110具有用于吸取待测血液样本的采样针(未示出)。此外，吸样装置110例如还可以包括驱动装置，该驱动装置用于驱动采样针通过采样针的针嘴定量吸取待测血液样本。吸样装置110可将吸取的血液样本输送至样本制备装置120。
样本制备装置120用于制备含有待测血液样本、溶血剂和用于识别有核红细胞的染色剂的测定试样。
在本申请实施例中，溶血剂用于溶解血液中的红细胞，将红细胞裂解为碎片，但能够保持白细胞的形态基本不变。
在一些实施例中，溶血剂可以是阳离子表面活性剂、非离子表面活性剂、阴离子表面活性剂、两亲性表面活性剂中的任意一种或几种的组合。在另一些实施例中，溶血剂可以包括烷基糖苷、三萜皂苷、甾族皂苷中的至少一种。例如，溶血剂可以选自辛基溴化喹啉、辛基溴化异喹啉、癸基溴化喹啉、癸基溴化异喹啉、十二烷基溴化喹啉、十二烷基溴化异喹啉、十四烷基溴化喹啉、十四烷基溴化异喹啉、辛基三甲基氯化铵、辛基三甲基溴化铵、癸基三甲基氯化铵、癸基三甲基溴化铵、十二烷基三甲基氯化铵、十二烷基三甲基溴化铵、十四烷基三甲基氯化铵、十四烷基三甲基溴化铵；十二烷基醇聚氧乙烯(23)醚、十六烷基醇聚氧乙烯(25)醚、十六烷基醇聚氧乙烯(30)醚等。
在一些实施例中，染色剂可以是能够结合有核红细胞中的核酸物质的荧光染料。例如，下面的化合物，可以用于本申请的实施例。
Figure PCTCN2022143965-appb-000001
在一些实施例中，样本制备装置120可以包括至少一个反应池和试剂供应装置(图中未示出)。所述至少一个反应池用于接收由吸样装置110吸取的待测血液样本，所述试剂供应装置将处理试剂(包括溶血剂、染色剂等)提供给所述至少一个反应池，从而由吸样装置110所吸取的待测血液样本与由所述试剂供应装置提供的处理试剂在所述反应池中混合，以制备成测定试样。
例如，所述至少一个反应池可以包括第一反应池和第二反应池，所述试剂供应装置可以包括第一试剂供给部和第二试剂供给部。吸样装置110用于将所吸取的待测血液样本分别部分地分配至第一反应池和第二反应池。第一试剂供给部用于将第一溶血剂和用于白细胞分类的第一染色剂提供给第一反应池，从而分配给第一反应池的部分待测血液样本与第一溶血剂和第一染色剂混合并反应，制备成第一测定试样。第二试剂供给部用于将第二溶血剂和用于识别有核红细胞的第二染色剂提供给第二反应池，从而分配给第二反应池的部分待测血液样本与第二溶血剂和第二染色剂混合并反应，制备成第二测定试样。目前商品化市售的用于白细胞四分类的试剂，可以用于本申请的第一溶血剂和第一染色剂，例如M-60LD和M-6FD；商品化市售的用于识别有核红细胞的试剂，可以用于本申请的第二溶血剂和第二染色剂，例如M-6LN和M-6FN。
光学检测装置130包括流动室、光源和光检测器，所述流动室用于供所述测定试样通过，所述光源用于用光照射通过所述流动室的测定试样，所述光检测器用于检测所述测定试样在通过所述流动室时被光照射后所产生的光学信息。
例如，第一测定试样和第二测定试样分别通过流动室，光源照射分别通过流动室的第一测定试样和第二测定试样，光检测器用于检测第一测定试样和第二测定试样在分别通过流动室时被光照射后所产生的第一光学信息和第二光学信息。
在此可以理解的是，用于白细胞分类的第一检测通道(也称为DIFF通道)是指光学检测装置130对由样本制备装置120制备的第一测定试样的检测，而用于识别有核红细胞的第二检测通道(也称为WNB通道)是指光学检测装置130对由样本制备装置120制备的第二测定试样的检测。
在本文中，流动室指适于检测光散射信号和荧光信号的聚焦液流的腔室。当一粒子、如一血细胞通过流动室的检测孔时，该粒子将来自光源的被导向该检测孔的入射光束向各方向散射。可以在相对于该入射光束的一个或多个不同角度设置光检测器，以检测被该粒 子散射的光，从而得到光散射信号。由于不同的粒子具有不同的光散射特性，因此光散射信号可以用于区分不同的粒子群体。具体地，在入射光束附近所检测的光散射信号通常被称为前向光散射信号或小角度光散射信号。在一些实施例中，该前向光散射信号可以从与入射光束约1°至约10°的角度上进行检测。在其他一些实施例中，该前向光散射信号可以从与入射光束约2°至约6°的角度上进行检测。在与入射光束呈约90°的方向所检测的光散射信号通常被称为侧向光散射信号。在一些实施例中，该侧向光散射信号可以是从与入射光束呈约65°至约115°的角度上进行检测。通常地，来自被荧光染料染色的血细胞所发出的荧光信号一般也在与入射光束呈约90°的方向上进行检测。
在一些实施例中，光检测器可以包括用于检测前向散射光信号的前向散射光检测器、用于检测侧向散射光信号的侧向散射光检测器和用于检测荧光信号的荧光检测器。相应地，光学信息可以包括测定试样中的粒子的前向散射光信号、侧向散射光信号和荧光信号。
图2示出光学检测装置130的一个具体示例。该光学检测装置130具有依次布置在一条直线上的光源101、光束整形组件102、流动室103和前向散射光检测器104。在流动室103的一侧，与所述直线成45°角布置有二向色镜106。通过流动室103中的粒子发出的侧向光，一部分透过二向色镜106，被与二向色镜106成45°角布置在二向色镜106后面的荧光检测器105捕获；另一部分侧向光被二向色镜106反射，被与二向色镜106成45°角布置在二向色镜106前面的侧向散射光检测器107捕获。
处理器140用于对数据进行处理和运算，得到所要求的结果，例如可以根据收集的各种光信号生成二维散点图或三维散点图，并在散点图上根据设门(gating)的方法进行粒子分析。处理器140还可以对中间运算结果或最终运算结果进行可视化处理，然后通过显示装置150显示出来。在本申请实施例中，处理器140被配置用于实施以下进一步详细描述的方法步骤。
在本申请的实施例中，处理器包括但不限于中央处理器(Central Processing Unit，CPU)、微控制单元(Micro Controller Unit，MCU)、现场可编程门阵列(Field-Programmable Gate Array，FPGA)、数字信号处理器(DSP)等用于解释计算机指令以及处理计算机软件中的数据的装置。例如，处理器用于执行计算机可读存储介质中的各计算机应用程序，从而使血液细胞分析仪100执行相应的检测流程并实时地分析光学检测装置130所检测到的光学信息或者说光学信号。
此外，血液细胞分析仪100还可以包括第一机壳160和第二机壳170。显示装置150例如可以为用户界面。光学检测装置130及处理器140设置在第二机壳170的内部。样本制备装置120例如设置在第一机壳160的内部，显示装置150例如设置在第一机壳160的外表面并且用于显示血液细胞分析仪的检测结果。
如背景技术中所提到的，利用血液细胞分析仪实现的血常规检测能够提示感染发生和感染类型鉴别，但临床当前应用的血常规WBC\Neu％等受多方面影响，不能准确及时地反映患者病情。而且现有技术在进行细菌感染和脓毒症诊疗方面的灵敏度和特异性均不佳。
基于此背景，发明人通过深入研究大量感染患者血液样本的血常规原始信号特征，意外发现了利用WNB通道的白细胞特征参数通过例如线性判别分析(linear discriminant analysis，LDA)可以实现高效力地评估受试者感染状态。所述线性判别分析是对费舍尔的线性鉴别方法的归纳，这种方法使用 统计学、 模式识别和机器学习方法，通过找到两类事件(例如，患有脓毒症或者不患有脓毒症、细菌感染或病毒感染、感染性炎症或非感染性炎症、脓毒症治疗有效或无效)的特征的一个线性组合，将一个多维数据通过线性组合得到一维数据，从而能够特征化或区分所述两类事件。通过该线性组合的系数可以确保所述两类事件的区分度最大。所得的线性组合可以用来进行后续事件的分类。
在此，本申请实施例提出了一种利用WNB通道的白细胞特征参数来获得感染标志参数以进行有效的感染状态评估的解决方案。本申请实施例所提供的解决方案优点在于可以 快速地对感染状态进行评估以实现脓毒症早期预测、脓毒症鉴别诊断、感染病情监控、脓毒症预后、细菌感染和病毒感染的鉴别等。
在一个实施例中，采用本发明的血液细胞分析仪通过本发明的方法进行了细菌感染和病毒感染的鉴别。不希望受理论约束，发明人发现在细菌感染中的主要作用细胞是中性粒细胞和单核细胞。这两种细胞在细菌感染期间会发生形态变化，如体积增大，颗粒增多，幼稚粒细胞数量增多，出现中毒颗粒，空泡，杜勒小体等，细胞核变致密等特征，这些特征可以在本发明的血液细胞分析仪通过检测中性粒细胞或单核细胞粒子团SS、FL、FS方向信号强度体现。病毒感染中的主要作用细胞是淋巴细胞。病毒感染后，淋巴细胞数量明显升高，出现异型淋巴细胞，在散点图FL方向能体现。
因此，本申请实施例首先提出一种血液细胞分析仪，包括：
吸样装置110，用于吸取受试者的待测血液样本；
制样装置120，用于制备含有所述待测血液样本的一部分、溶血剂和用于识别有核红细胞的染色剂的测定试样；
光学检测装置130，包括流动室、光源和光检测器，所述流动室用于供所述测定试样通过，所述光源用于用光照射通过所述流动室的测定试样，所述光检测器用于检测所述测定试样在通过所述流动室时被光照射后所产生的光学信息；
处理器140，被配置为：
从所述光学信息获得所述测定试样中的至少一个目标粒子团的至少一个白细胞特征参数；
基于所述至少一个白细胞特征参数获得用于评估所述受试者的感染状态的感染标志参数；并且
输出所述感染标志参数。
在此应理解的是，目标粒子团的细胞特征参数不包括目标粒子团的细胞计数或分类参数，而是包括反映该目标粒子团中的细胞的体积、内部颗粒度、内部核酸含量等细胞特征的特征参数。
在一些实施例中，可以基于光学信息中的前向散射光信号(或前向散射光强度)FS、侧向散射光信号(或侧向散射光强度)SS和荧光信号(或荧光强度)FL将测定试样中的白细胞团Wbc(包括所有类型的白细胞)识别出来，同时能够将测定试样中的白细胞中的中性粒细胞团Neu和淋巴细胞团Lym识别出来，如图3至5所示。其中，图3为基于光学信息中的前向散射光信号FS和荧光信号FL生成的二维散点图，图4为基于光学信息中的前向散射光信号FS和侧向散射光信号SS生成的二维散点图，图5为基于光学信息中的前向散射光信号FS、侧向散射光信号SS和荧光信号FL生成的三维散点图。此外，处理器140被进一步配置为根据光学信息识别测定试样中的有核红细胞以获得有核红细胞计数。
相应地，在一些实施例中，所述至少一个目标粒子团可以包括测定试样中的白细胞团Wbc、中性粒细胞团Neu和淋巴细胞团Lym中的至少一个细胞团。例如，所述至少一个目标粒子团包括测定试样中的淋巴细胞团Lym和白细胞团Wbc，或者包括测定试样中的中性粒细胞团Neu和白细胞团Wbc，或者包括测定试样中的淋巴细胞团Lym和中性粒细胞团Neu。也就是说，所述至少一个白细胞特征参数可以包括测定试样中的淋巴细胞团Lym、中性粒细胞团Neu和白细胞团Wbc的细胞特征参数中的一个或多个参数。
优选地，所述至少一个目标粒子团包括白细胞团Wbc和/或中性粒细胞团Neu。发明人在研究大量受试者样本的血常规检测原始信号过程中发现，使用测定试样中的白细胞团Wbc和/或中性粒细胞团Neu的细胞特征参数对于有效评估感染状态是有利的。更优选的是，将中性粒细胞团Neu和白细胞团Wbc的细胞特征参数组合能够给出更有诊断效力的感染标志参数。
在一些实施例中，所述至少一个白细胞特征参数可以包括如下细胞特征参数中的一个 或多个参数：所述至少一个目标粒子团(例如中性粒细胞团Neu和/或白细胞团Wbc)的前向散射光强度分布宽度、前向散射光强度分布重心、前向散射光强度分布变异系数、侧向散射光强度分布宽度、侧向散射光强度分布重心、侧向散射光强度分布变异系数、荧光强度分布宽度、荧光强度分布重心、荧光强度分布变异系数以及所述至少一个目标粒子团在由前向散射光强度、侧向散射光强度和荧光强度中的两种光强度生成的二维散点图中的分布区域的面积、和所述至少一个目标粒子团在由前向散射光强度、侧向散射光强度和荧光强度生成的三维散点图中的分布区域的体积，例如图5中白细胞团所占据的空间的体积。
在一些具体的示例中，所述至少一个白细胞特征参数可以包括如下细胞特征参数中的一个或多个参数：
白细胞团的前向散射光强度分布重心(N_WBC_FS_P)、白细胞团的侧向散射光强度分布重心(N_WBC_SS_P)、白细胞团的侧向荧光强度分布重心(N_WBC_FL_P)、白细胞团的前向散射光强度分布宽度(N_WBC_FS_W)、白细胞团的侧向散射光强度分布宽度(N_WBC_SS_W)、白细胞团的侧向荧光强度分布宽度(N_WBC_FL_W)、白细胞团的前向散射光强度分布变异系数(N_WBC_FS_CV)、白细胞团的侧向散射光强度分布变异系数(N_WBC_SS_CV)、白细胞团的侧向荧光强度分布变异系数(N_WBC_FL_CV)；
白细胞团在基于前向散射光强度、侧向散射光强度和侧向荧光强度中的两种光强度生成的二维散点图中的分布区域的面积、白细胞团在由前向散射光强度、侧向散射光强度和荧光强度生成的三维散点图中的分布区域的体积，例如：白细胞团在由侧向散射光强度和前向散射光强度生成的二维散点图中的分布区域的面积(N_WBC_SSFS_Area)、白细胞团在由侧向荧光强度和前向散射光强度生成的二维散点图中的分布区域的面积(N_WBC_FLFS_Area)、白细胞团在由侧向荧光强度和侧向散射光强度生成的二维散点图中的分布区域的面积(N_WBC_FLSS_Area)；
中性粒细胞团的前向散射光强度分布重心(N_NEU_FS_P)、中性粒细胞团的侧向散射光强度分布重心(N_NEU_SS_P)、中性粒细胞团的侧向荧光强度分布重心(N_NEU_FL_P)、中性粒细胞团的前向散射光强度分布宽度(N_NEU_FS_W)、中性粒细胞团的侧向散射光强度分布宽度(N_NEU_SS_W)、中性粒细胞团的侧向荧光强度分布宽度(N_NEU_FL_W)、中性粒细胞团的前向散射光强度分布变异系数(N_NEU_FS_CV)、中性粒细胞团的侧向散射光强度分布变异系数(N_NEU_SS_CV)、中性粒细胞团的侧向荧光强度分布变异系数(N_NEU_FL_CV)；
中性粒细胞团在基于前向散射光强度、侧向散射光强度和侧向荧光强度中的两种光强度生成的二维散点图中的分布区域的面积、中性粒细胞团在由前向散射光强度、侧向散射光强度和荧光强度生成的三维散点图中的分布区域的体积，例如：中性粒细胞团在由侧向散射光强度和前向散射光强度生成的二维散点图中的分布区域的面积(N_NEU_SSFS_Area)、中性粒细胞团在由侧向荧光强度和前向散射光强度生成的二维散点图中的分布区域的面积(N_NEU_FLFS_Area)、中性粒细胞团在由侧向荧光强度和侧向散射光强度生成的二维散点图中的分布区域的面积(N_NEU_FLSS_Area)；
淋巴细胞团的前向散射光强度分布重心(N_LYM_FS_P)、中性粒细胞团的侧向散射光强度分布重心(N_LYM_SS_P)、淋巴细胞团的侧向荧光强度分布重心(N_LYM_FL_P)、淋巴细胞团的前向散射光强度分布宽度(N_LYM_FS_W)、淋巴细胞团的侧向散射光强度分布宽度(N_LYM_SS_W)、淋巴细胞团的侧向荧光强度分布宽度(N_LYM_FL_W)、淋巴细胞团的前向散射光强度分布变异系数(N_LYM_FS_CV)、淋巴细胞团的侧向散射光强度分布变异系数(N_LYM_SS_CV)、淋巴细胞团的侧向荧光强度分布变异系数(N_LYM_FL_CV)；以及
淋巴细胞团在基于前向散射光强度、侧向散射光强度和侧向荧光强度中的两种光强度生成的二维散点图中的分布区域的面积、淋巴细胞团在由前向散射光强度、侧向散射光强 度和荧光强度生成的三维散点图中的分布区域的体积，例如：淋巴细胞团在由侧向散射光强度和前向散射光强度生成的二维散点图中的分布区域的面积(N_LYM_SSFS_Area)、淋巴细胞团在由侧向荧光强度和前向散射光强度生成的二维散点图中的分布区域的面积(N_LYM_FLFS_Area)、淋巴细胞团在由侧向荧光强度和侧向散射光强度生成的二维散点图中的分布区域的面积(N_LYM_FLSS_Area)。
本领域技术人员能够理解，可以利用某个粒子群落散点图整体的分布特征，例如整个白细胞团的前向散射光强度分布宽度，也可以是某个粒子群落中部分区域粒子分布的特征，例如中性粒细胞团中间密度较高的部分的分布面积，或者与正常人散点图中性粒细胞或淋巴细胞粒子群有差异的区域。
在一些实施例中，感染标志参数可以由单个白细胞特征参数构成，例如由以上列举的细胞特征参数之一构成。或者，感染标志参数可以是单个白细胞参数的线性函数或非线性函数。
备选地，在另一些实施例中，感染标志参数也可以由所述至少一个白细胞特征参数和从所述光学信息获得的不同于此白细胞特征参数的另一个白细胞参数组合计算得到，例如由以上列列举的细胞特征参数中的多个细胞特征参数组合得到、尤其是通过线性函数组合得到。
例如，在一些示例中，处理器140可以被进一步配置为：
从所述光学信息获得所述测定试样中的第一白细胞粒子团的至少一个白细胞特征参数(也称为第一白细胞参数)和所述测定试样中的第二白细胞粒子团的至少一个第二白细胞参数；以及
基于所述至少一个白细胞特征参数和所述至少一个第二白细胞参数计算所述感染标志参数。
在此，第一白细胞粒子团和第二白细胞粒子团彼此不同，例如第一白细胞粒子团为白细胞团且第二白细胞粒子团为中性粒细胞团，或者反过来第一白细胞粒子团为中性粒细胞团且第二白细胞粒子团为白细胞团。
在此优选的是，所述至少一个第二白细胞参数包括细胞特征参数，即，所述至少一个第二白细胞参数包括第二白细胞粒子团的细胞特征参数。由此能够提供诊断效力进一步提高的感染标志参数。
当然，也可能的是，第二白细胞参数包括第二白细胞粒子团的分类参数或计数参数(例如白细胞计数或中性粒细胞计数)。
在上述实施例中，优选的是，处理器140可以被进一步配置为，通过线性函数将所述第一白细胞特征参数和所述第二白细胞参数组合成感染标志参数，即，通过如下公式计算感染标志参数：
Y＝A×X1+B×X2+C
其中，Y表示感染标志参数，X1表示第一白细胞参数，X2表示第二白细胞参数，A、B、C为常数。
当然，在其他实施例中，也可以通过非线性函数将所述第一白细胞参数和所述第二白细胞参数组合成感染标志参数，本申请对此不做具体限定。本领域技术人员能够理解，在其他实施例中，也可以不将这两个白细胞参数通过函数计算，而是联合使用所述第一白细胞参数和所述第二白细胞参数，分别与各自的阈值比较，获得感染标志参数。例如分别设定两个参数的诊断阈值：阈值1和阈值2，然后分析“参数1≥阈值1or参数2≥阈值2”的诊断效能，分析“参数1≥阈值1and参数2≥阈值2”的诊断效能。
在一些实施例中，还可以联合使用WNB通道和DIFF通道的粒子团的细胞特征参数。
在另一些实施例中，所述感染标志参数可以由白细胞参数与其他血细胞参数计算而成，即，感染标志参数可以是至少一个白细胞参数与至少一个其他血细胞参数计算而成。所述其他血细胞参数可以为血小板(PLT)、有核红细胞(NRBC)、或网织红细胞(RET)的 分类或计数参数。
在另一些实施例中，处理器140也可以被进一步配置为：
从所述光学信息获得所述测定试样中的一个白细胞粒子团的至少两个白细胞特征参数；并且
基于所述至少两个白细胞特征参数计算、尤其是通过线性函数计算所述感染标志参数。
在此，借助图6说明分布宽度、分布重心、变异系数以及分布区域的面积或体积的含义，其中，图6示出根据本申请一些实施例的测定试样中的白细胞团的细胞特征参数。
如图6所示，W(N_WBC_FS_W)代表测定试样中的白细胞团的前向散射光强度分布宽度，其中，N_WBC_FS_W等于白细胞团的前向散射光强度分布上限(UP)与白细胞团的前向散射光强度分布下限(DOWN)的差值。N_WBC_FS_P代表测定试样中的白细胞团的前向散射光强度分布重心、即白细胞在FS方向的平均位置(图6中的“+”处)，其中，N_WBC_FS_P通过如下公式计算：
Figure PCTCN2022143965-appb-000002
其中，FS(i)为第i个白细胞的前向散射光强度。
N_WBC_FS_CV代表测定试样中的白细胞团的前向散射光强度分布变异系数，其中，N_WBC_FS_CV等于N_WBC_FS_W除以N_WBC_FS_P。
此外，图6中的Area(N_WBC_FLFS_Area)代表测定试样中的白细胞团在由前向散射光强度和荧光强度生成的散点图中的分布区域的面积。
在一些实施例中，如图6所示，C表示白细胞团的轮廓分布曲线，例如可以将位于轮廓分布曲线C内的位置总数记为该白细胞团的面积。本领域技术人员能够理解，利用通常的血液分析仪的分类算法，或者图像处理技术，容易得到粒子群落的轮廓分布曲线。
在另一些实施例中，D_NEU_FLSS_Area还可以通过如下算法步骤实现(图32)：
从中性粒细胞(NEU)粒子团中随机选取一个粒子P1，并找出与P1距离最远的一个粒子P2；
构建向量V1(P1-P2)，并以P1为向量起点，在中性粒细胞(NEU)粒子团中再找出一个粒子P3，并构建向量V2(P1-P3)，使得向量V2(P1-P3)与向量V1(P1-P2)成最大夹角；
再以P1为向量起点，在中性粒细胞(NEU)粒子团中再找出一个粒子P4，并构建向量V3(P1-P4)，使得向量V3(P1-P4)与向量V1(P1-P2)成最大夹角；
以此类推，分别得到中性粒细胞(NEU)粒子团最外侧的一组粒子P1,P2,P3,P4,…Pn；
使用椭圆拟合粒子点P1,P2,P3,P4,…Pn，并获得该椭圆的长轴a、短轴b；
所述D_NEU_FLSS_Area为所述长轴a和所述短轴b的乘积。
类似的，所述中性粒细胞群在由前向散射光强度、侧向散射光强度和荧光强度生成的三维散点图中的分布区域的体积参数也可以由相应的计算方式得到。
在此可以理解的是，其他细胞特征参数的定义可以以相应的方式参考图6和图32所示的实施例。
在一些实施例中，处理器140可以被进一步配置为：当所述感染标志参数的值处于预设范围之外时，输出指示所述感染标志参数异常的提示信息。例如，当所述感染标志参数的值异常升高时，可以输出向上指向的箭头指示异常升高。
可选地，处理器140还可以被配置为输出所述预设范围。
在一些实施例中，处理器140可以被进一步配置为：基于所述感染标志参数输出指示所述受试者的感染状态的提示信息。例如，处理器140可以被配置为将提示信息输出给显示装置进行显示。这里的显示装置可以是血液细胞分析仪100的显示装置150，也可以是与处理器140通信连接的其他显示装置。例如处理器140可以通过医院信息管理系统将提 示信息输出至用户(医生)侧的显示装置。
接下来描述本申请提出的感染标志参数的一些应用场景，但本申请不限于此。
在一些实施例中，所述感染标志参数可以用于对对受试者进行脓毒症早期预测、脓毒症诊断、普通感染与重症感染的鉴别、感染病情监控、脓毒症预后分析、细菌感染和病毒感染的鉴别或者非感染性炎症和感染性炎症的鉴别、脓毒症的疗效评估。例如，处理器140可以被进一步配置用于基于感染标志参数对受试者进行脓毒症早期预测、脓毒症诊断、普通感染与重症感染的鉴别、感染病情监控、脓毒症预后分析、细菌感染和病毒感染的鉴别或者非感染性炎症和感染性炎症的鉴别、脓毒症的疗效评估。
脓毒症属于严重的感染性疾病，其发生率高，病死率高，每延缓1小时治疗，患者的死亡率上升7％。因此，脓毒症的早期预警显得尤为重要，脓毒症的早期识别和预警，能为患者增加宝贵的诊疗时间，大大提高生存率。
为此，在脓毒症早期预测的应用场景中，即，所述感染标志参数被用于脓毒症的早期预测，处理器140可以被配置为，当感染标志参数满足第一预设条件时，输出指示受试者在被采集待测血液样本之后的一定时间段内可能进展为脓毒症的提示信息。
在一些实施例中，所述一定时间段不大于48小时，即，本申请实施例能够最多提前两天预测受试者是否可能进展为脓毒症。进一步地，所述一定时间段在24小时之内，即，本申请实施例能够提前一天预测受试者是否可能进展为脓毒症。
在此，所述第一预设条件例如可以为感染标志参数的值大于预设阈值。该预设阈值可以根据具体的参数组合和血液细胞分析仪来确定。
在一些实施例中，用于脓毒症早期预测的感染标志参数可以为以下参数之一：N_WBC_FL_W；N_WBC_FS_W；N_WBC_SS_W。
在另一些实施例中，通过对本发明的两个或多个白细胞特征参数进行组合来计算感染标志参数。从细胞类型层面，例如，中性粒细胞和单核细胞均是机体抗感染的第一道屏障，在反映感染程度上都很有价值，因此组合使用中性粒细胞的特征参数和单核细胞的特征参数能够提高本发明的预测、诊断、评估和/或指导治疗功效。
本领域技术人员能够理解，本公开的实施方式，是利用原始光学信息形成的散点图，计算的白细胞相关粒子群的特征得到白细胞特征参数，基于白细胞特征参数获得用于评估所述受试者的感染状态的感染标志参数。当基于单个白细胞特征参数得到感染标志参数时，可以直接将单个白细胞特征参数视为感染标志参数，也可以对该单个白细胞特征参数进行线性或非线性函数的计算获得感染标志参数；当基于多个白细胞特征参数得到感染标志参数时，可以联合使用，或者组合计算得到感染标志参数。优选的，该感染标志参数与诊断阈值比较，给出相关的临床提示。
在一些实施例中，可以通过在表1中列出的各参数组合计算感染标志参数，以用于脓毒症早期预测。
表1 用于脓毒症早期预测的参数组合
Figure PCTCN2022143965-appb-000003
Figure PCTCN2022143965-appb-000004
在此优选可以采用N_WBC_FL_P与N_WBC_FS_W的组合、N_WBC_SS_W与N_WBC_FS_W的组合、或N_WBC_FL_与N_NEU_FLSS_Area的组合来计算用于脓毒症早期预测的感染标志参数。
脓毒症前期的临床症状与普通/重症感染相似，脓毒症患者易被误诊为普通/重症感染性疾病，延误治疗时机。因此，脓毒症的鉴别诊断显得尤为重要。
为此，在脓毒症诊断的应用场景中，即，所述感染标志参数被用于脓毒症鉴别，处理器140可以被配置为，当感染标志参数满足第二预设条件时，输出指示受试者患有脓毒症的提示信息。在此，第二预设条件同样可以为感染标志参数的值大于预设阈值。该预设阈值可以根据具体的参数组合和血液细胞分析仪来确定。
在一些实施例中，用于脓毒症诊断的感染标志参数可以为以下参数之一：N_WBC_FL_W、N_WBC_FL_P、N_NEU_FL_P、N_NEU_FL_W、N_WBC_SS_W、N_NEU_FLFS_Area、N_WBC_FS_W、N_NEU_FS_W、N_NEU_FLSS_Area、N_NEU_SS_W、N_WBC_SS_P、N_NEU_SS_P、N_WBC_FLSS_Area、N_NEU_FS_CV、N_WBC_FLFS_Area、N_WBC_FS_P、N_NEU_SSFS_Area。
在另一些实施例中，可以通过在表2中列出的各参数组合计算感染标志参数，以用于脓毒症诊断。
表2 用于脓毒症诊断的参数组合
Figure PCTCN2022143965-appb-000005
Figure PCTCN2022143965-appb-000006
Figure PCTCN2022143965-appb-000007
Figure PCTCN2022143965-appb-000008
在此优选可以采用N_WBC_FL_P与N_WBC_FS_W的组合、N_WBC_FL_W与N_NEU_FL_P的组合、N_WBC_FL_W与N_NEU_FLSS_Area的组合、N_WBC_FL_W与N_NEU_FL_W的组合、或N_WBC_SS_P与N_WBC_FL_P的组合来计算用于脓毒症诊断的感染标志参数。
细菌感染患者根据其感染严重度和器官功能状态，可分为普通感染和重症感染，两种感染的临床治疗手段和护理措施不一样，所以普通感染与重症感染的鉴别能协助医生识别有生命危险的患者，也能更合理的分配医疗资源。
为此，在普通感染与重症感染的鉴别的应用场景中，即，所述感染标志参数被用于判断所述受试者患普通感染还是重症感染，处理器140可以被配置为，当感染标志参数满足第三预设条件时，输出指示受试者患有重症感染的提示信息。在此，第三预设条件同样可以为感染标志参数的值大于预设阈值。该预设阈值可以根据具体的参数组合和血液细胞分析仪来确定。
在一些实施例中，用于普通感染与重症感染的鉴别的感染标志参数可以为以下参数中的一个：
N_WBC_FL_W；N_WBC_FL_P；N_NEU_FL_W；N_NEU_FL_P；N_NEU_FLFS_Area；N_WBC_SS_W；N_WBC_FS_W；N_NEU_FLSS_Area；N_NEU_FS_W；N_WBC_FLSS_Area；N_N EU_SS_W；N_WBC_FLFS_Area；N_NEU_FS_CV；N_WBC_SS_P；N_NEU_SS_P；N_WBC_FS_CV；N_NEU_SSFS_Area；N_WBC_FS_P；N_WBC_SS_CV。
在另一些实施例中，可以通过在表3中列出的各参数组合计算感染标志参数，以用于普通感染与重症感染的鉴别。
表3 用于普通感染与重症感染的鉴别的参数组合
Figure PCTCN2022143965-appb-000009
Figure PCTCN2022143965-appb-000010
Figure PCTCN2022143965-appb-000011
Figure PCTCN2022143965-appb-000012
在感染病情监控的应用场景中，受试者为感染患者(即，患有感染性炎症的患者)、尤其是患有重症感染或患有脓毒症的患者，例如，受试者来自重症监护室的重症感染或患有脓毒症的患者。脓毒症属于严重的感染性疾病，其发生率高，病死率高。脓毒症患者病情波动较大，需要日常监护，防止患者病情加重但又没有及时处理。因此，临床症状结合实验室检查结果判断脓毒症患者病情进展情况和治疗效果十分重要。
为此，处理器140可以被配置为根据感染标志参数监控受试者的感染病情发展。
在一些实施例中，处理器140可以被进一步配置为通过如下方式监控受试者的感染病情发展，即：
获取通过多次检测、尤其是至少三次检测在不同时间点来自受试者的血液样本而获得的所述感染标志参数的值；并且
根据通过所述多次的检测而获得的所述感染标志参数的值的变化趋势来判断所述受 试者病情是否好转。
在具体的示例中，处理器140可以被进一步配置为：当通过所述多次的检测而获得的所述感染标志参数的值逐渐趋势性减低时，输出指示所述受试者病情好转的提示信息；而当通过所述多次的检测而获得的所述感染标志参数的值逐渐趋势性增加时，输出指示所述受试者病情加重的提示信息。这里的多次的检测，可以连续每天检测，也可以是有规律地间隔多次的检测。
例如，获取患者在确诊脓毒症之后连续多日、例如连续7日的感染标志参数值，当这些感染标志参数值呈现降低趋势时，认为患者病情好转，因此给出病情好转的提示。
在另一些实施例中，处理器140还可以被进一步配置为通过如下方式提示受试者的病情发展：
获取通过对来自受试者的当前血液样本的当前检测而获得的所述感染标志参数的当前值，并且获取通过对来自受试者的前一次血液样本的前一次检测而获得的所述感染标志参数的在先值；并且
根据所述感染标志参数的在先值与第一阈值的比较以及所述感染标志参数的在先值与所述感染标志参数的当前值的比较来监控受试者的病情发展。
图7为根据本申请一些实施例判断患者病情发展的示意性流程图。
如图7所示，处理器140可以被进一步配置为当感染标志参数的在先值大于等于第一阈值时：
如果感染标志参数的当前值(即图7中的本次结果)大于感染标志参数的在先值(即图7中的前一次结果)且两者的差值大于第二阈值，则输出指示受试者病情加重的提示信息；
如果感染标志参数的当前值小于感染标志参数的在先值且两者的差值大于第二阈值，并且感染标志参数的当前值小于第一阈值，则输出指示受试者病情好转并且感染程度下降的提示信息；
如果感染标志参数的当前值小于感染标志参数的在先值且两者的差值大于第二阈值，但感染标志参数的当前值大于等于第一阈值，则输出指示受试者病情好转但感染仍然较重的提示信息或者不输出任何提示信息；以及
如果感染标志参数的当前值与感染标志参数的在先值的差值不大于第二阈值，则输出指示受试者病情无明显好转且感染仍然较重的提示信息或者不输出任何提示信息。
进一步地，如图7所示，处理器140可以被配置为：当感染标志参数的在先值小于第一阈值时：
如果感染标志参数的当前值小于感染标志参数的在先值且两者的差值大于第二阈值，则输出指示受试者病情好转并且感染程度下降的提示信息；
如果感染标志参数的当前值大于感染标志参数的在先值且两者的差值大于第二阈值，并且感染标志参数的当前值大于第一阈值，则输出指示受试者病情加重并且感染较重的提示信息；
如果感染标志参数的当前值大于感染标志参数的在先值且两者的差值大于第二阈值，但感染标志参数的当前值小于第一阈值，则输出指示受试者病情波动或感染可能加重的提示信息或者不输出提示信息；以及
如果感染标志参数的当前值与感染标志参数的在先值的差值不大于第二阈值，则输出指示受试者感染未加重的提示信息或者不输出提示信息。
在图7所示的实施例中，当感染标志参数用于监控重症感染患者的病情发展时，第一阈值可以是用于判断受试者是否患重症感染的预设阈值。而当感染标志参数用于监控脓毒症患者的病情发展时，第一阈值可以是用于判断受试者是否患脓毒症的预设阈值。
在一些实施例中，用于感染病情监控的感染标志参数可以为以下参数中的一个：
N_WBC_SS_P、N_WBC_SS_W、N_WBC_SS_CV、N_WBC_FL_P、N_WBC_FL_W、N_WBC_FL_CV、N_WBC_FS_P、N_WBC_FS_W、N_WBC_FS_CV、N_WBC_FLFS_Area、N_WBC_FLSS_Area、N_WBC_SSFS_Area。
在另一些实施例中，可以采用N_WBC_FL_P与N_WBC_FS_W的组合计算感染标志参数，以用于感染病情监控。
在脓毒症预后分析的应用场景中，受试者为接受了治疗的脓毒症患者，所述感染标志参数被用于判断所述受试者的脓毒症预后是否良好。对此，处理器140可以被进一步配置为，根据感染标志参数判断受试者的脓毒症预后是否良好。例如，当所述感染标志参数满足第四预设条件时，输出指示所述受试者脓毒症预后良好的提示信息。
在一些实施例中，用于脓毒症预后分析的感染标志参数可以为以下参数中的一个：N_WBC_FL_W、N_WBC_FS_W、N_WBC_FLSS_Area、N_WBC_FS_CV、N_WBC_FLFS_Area、N_WBC_SS_W、N_WBC_FL_P、N_WBC_SS_CV、N_WBC_SSFS_Area、N_WBC_SS_P、N_WBC_FS_P、N_WBC_FL_CV。
在另一些实施例中，可以通过在表4中列出的各参数组合计算感染标志参数，以用于脓毒症预后分析。
表4 用于脓毒症预后分析的参数组合
Figure PCTCN2022143965-appb-000013
Figure PCTCN2022143965-appb-000014
感染性疾病可分为细菌感染、病毒感染、真菌感染等不同感染类型，其中细菌感染和病毒感染最为常见。虽然两种感染的临床症状大致相同，但治疗方法完全不一样，所以明确感染的类型对选择正确的治疗方法是有帮助的。为此，所述感染标志参数被用于细菌感染和病毒感染的鉴别，处理器140可以被进一步配置为，根据所述感染标志参数判断所述受试者的感染类型是病毒感染还是细菌感染。
在一些实施例中，用于细菌感染和病毒感染的鉴别的感染标志参数可以为以下参数中的一个：N_WBC_FS_P、N_WBC_FL_P、N_WBC_FS_W、N_WBC_FL_W、N_WBC_FLFS_Area、N_WBC_FLSS_Area、N_WBC_SS_P、N_WBC_SS_W、N_WBC_FL_CV、N_WBC_FS_CV、N_WBC_SSFS_Area、N_WBC_SS_CV。
在另一些实施例中，可以通过在表5中列出的各参数组合计算感染标志参数，以用于细菌感染和病毒感染的鉴别。
表5 用于细菌感染和病毒感染的鉴别的参数组合
Figure PCTCN2022143965-appb-000015
Figure PCTCN2022143965-appb-000016
此外，炎症分为由病原微生物感染所致的感染性炎症，和由物理因素、化学因素或组织坏死所致的非感染性炎症。两种炎症所表现的临床症状大致相同，都会出现发红和发热等症状，但两种炎症的治疗方式不完全一样，所以明确患者的炎症反应是由何种因素引起对于对症治疗是有帮助的。
为此，所述感染标志参数被用于非感染性炎症和感染性炎症的鉴别，处理器140可以被进一步配置为，根据感染标志参数判断受试者是患有感染性炎症还是非感染性炎症。例如，当所述感染标志参数满足第五预设条件时，输出指示所述受试者患感染性炎症的提示信息。
在一些实施例中，用于感染性炎症和非感染性炎症的鉴别的感染标志参数可以为以下参数中的一个：N_WBC_FL_W、N_WBC_FL_P、N_WBC_SS_W、N_WBC_FS_W、N_WBC_SS_P、N_WBC_FS_P、N_WBC_FS_CV、N_WBC_SS_CV、N_WBC_FL_CV。
在另一些实施例中，可以通过在表6中列出的各参数组合计算感染标志参数，以用于感染性炎症和非感染性炎症的鉴别。
表6 用于感染性炎症和非感染性炎症的鉴别的参数组合
Figure PCTCN2022143965-appb-000017
医生在对患者进行问诊和查体后，一般会有一个或者几个初步的疾病诊断。然后通过实验室检测，影像学检查等手段进行鉴别诊断或者疾病确诊。因此，可以说医生是带着目的去开化验检查单。换句话说，医生开单的时候就已经明确了参数应该应用在哪个场景。举个例子：一个普通门诊发热患者就诊，无器官损伤症状，医生初步判断是普通感染，而不是重症感染或者脓毒症。但具体开什么药物，需要明确是病毒感染还是细菌感染，所以开了血常规。结果出来，会关注参数是否大于“细菌感染VS病毒感染”的阈值，而不是“脓毒症诊断”的阈值。所以，本申请中的输出的感染标志参数，在临床上作为医生的参考，并非诊断目的。
接下来描述一些用于进一步确保基于感染标志参数的诊断或提示可靠的实施例，但应理解，本申请实施例不限于此。
为了避免用于计算感染标志参数的白细胞特征参数本身对诊断或提示可靠性造成干扰，在一些实施例中，处理器140可以被进一步配置为，当所述目标粒子团的预设特征参数满足第六预设条件时，不输出所述感染标志参数的值(即，屏蔽所述感染标志参数的值)，或者输出所述感染标志参数的值并且同时输出指示该感染标志参数的值不可靠的提示信息。
当处理器140被进一步配置为基于所述感染标志参数输出指示所述受试者的感染状态的提示信息时，如果目标粒子团的预设特征参数满足第六预设条件，则处理器140不输出指示所述受试者的感染状态的提示信息，或者输出指示受试者的感染状态的提示信息并且输出该提示信息不可靠的附加信息。
在一些具体的示例中，处理器140可以被配置为，当目标粒子团的粒子总数小于预设阈值时，不输出所述感染标志参数的值，或者输出所述感染标志参数的值并且同时输出指示该感染标志参数的值不可靠的提示信息。
也就是说，当目标粒子团的粒子总数小于预设阈值时，即目标粒子团的粒子较少，粒子表征的信息量有限，此时感染标志参数的计算结果可能不可靠。例如，如图8所示，测定试样中的白细胞团的粒子总数太低，可能导致通过该白细胞团的白细胞特征参数计算的感染标志参数不可靠。
在此，例如可以通过所述光学信息判断目标粒子团的预设特征参数是否异常，例如目标粒子团的粒子总数是否低于预设阈值。
在另一些示例中，处理器140可以被配置为，当目标粒子团与其他粒子团存在交叠时，不输出指示受试者的感染状态的提示信息，不输出所述感染标志参数的值，或者输出所述感染标志参数的值并且同时输出指示该感染标志参数的值不可靠的提示信息。
例如，如图9所示，测定试样中的中性粒细胞团与其他粒子交叠，可能导致通过该中性粒细胞团的白细胞特征参数计算的感染标志参数不可靠。在此，例如可以通过光学信息判断目标粒子团与其他粒子团是否存在交叠。
类似地，当处理器140被进一步配置为基于所述感染标志参数输出指示所述受试者的感染状态的提示信息时，如果目标粒子团的粒子总数小于预设阈值，和/或如果目标粒子团与其他粒子团存在交叠，则处理器140不输出指示所述受试者的感染状态的提示信息，或者输出指示受试者的感染状态的提示信息并且输出该提示信息不可靠的附加信息。
此外，受试者的疾病状况以及受试者血液中的异常细胞(例如原始细胞、异常淋巴细胞、幼稚粒细胞)也可能影响感染标志参数的诊断或提示效力。为此，处理器140可以被进一步配置为：根据受试者是否患有特定疾病和/或根据待测血液样本是否存在预设类型的异常细胞(例如原始细胞、异常淋巴细胞、幼稚粒细胞)来确定所述感染标志参数是否可靠。
在一些具体的示例中，处理器140可以被配置为：当受试者患有血液疾病或者待测血液样本中存在异常细胞、尤其是原始细胞时，不输出所述感染标志参数的值，或者输出所述感染标志参数的值并且同时输出指示该感染标志参数的值不可靠的提示信息。可以理解地，患有血液疾病的受试者的血象异常，导致感染标志参数的提示不可靠。
处理器140例如可以根据受试者的身份信息来获取该受试者是否患有血液疾病。
在一些实施例中，处理器140可以被配置为根据所述光学信息判断待测血液样本中是否存在异常细胞、尤其是原始细胞时。
在一些实施例中，处理器140还可以被配置为在计算感染标志参数之前对白细胞特征参数进行数据处理、例如去噪声干扰(如图10所示)或取对数处理(如图11所示)，以便更准确地计算感染标志参数，例如避免不同仪器、不同试剂所引起的信号变化。
下面结合如下一些实施例对处理器140为每个感染标志参数组配置优先级的方式进行说明。
在一些实施例中，处理器140可以被进一步配置为：根据感染诊断效力、参数稳定性和参数局限性中的至少一种为每个感染标志参数组配置优先级。
在此优选地，处理器140可以被进一步配置为：至少根据所述感染诊断效力为每个感染标志参数组配置优先级。例如，处理器140可以仅根据感染诊断效力为每个感染标志参数组配置优先级；又例如，处理器140可以根据感染诊断效力和参数稳定性为每个感染标志参数组配置优先级；再例如，处理器140可以根据感染诊断效力、参数稳定性以及参数局限性为每个感染标志参数组配置优先级。
在一些实施例中，本申请的感染标志参数组可以用于多种感染状态的评估，例如基于所述感染标志参数对所述受试者进行脓毒症的早期预测、脓毒症的诊断、普通感染与重症感染的鉴别、感染病情监控、脓毒症的预后分析、细菌感染和病毒感染的鉴别、脓毒症的疗效评估或非感染性炎症和感染性炎症的鉴别。相应地，以普通感染与重症感染的鉴别场景为例，所述感染诊断效力包括针对普通感染与重症感染鉴别的诊断效力。例如，当本申请的感染标志参数组仅设置用于某一种感染状态评估、例如仅用于重症感染鉴别时，可以根据针对该感染状态评估、例如重症感染的鉴别的诊断效力为每个感染标志参数组配置优先级。
作为一些实现方式，处理器140可以被进一步配置为：按照每个感染标志参数组的ROC曲线与水平坐标轴围成的面积ROC_AUC为每个感染标志参数组配置优先级，其中，ROC_AUC越大，相应的感染标志参数组的优先级越高。其中，ROC曲线是以真阳性率为纵坐标、假阳性率为横坐标绘制的受试者工作特征曲线，每个感染标志参数组的ROC_AUC可以反映该感染标志参数组的感染诊断效力。
在一些实施例中，所述参数稳定性包括数值重复性、老化稳定性、温度稳定性和机间一致性中的至少一个。其中，数值重复性是指，在同一环境下使用同一仪器在短时间内对同一待测血液样本进行多次的重复检测时，所使用的感染标志参数组的数值的一致性；老化稳定性是指，在同一环境下使用同一仪器在不同时间点对同一待测血液样本进行检测时，所使用的感染标志参数组的数值的稳定性；温度稳定性是指，在不同的温度环境下使用同一仪器对同一待测血液样本进行检测时，所使用的感染标志参数组的数值的稳定性；机间一致性是指，在同一环境下使用不同的仪器上对同一待测血液样本进行检测时，所使用的感染标志参数组的数值的一致性。
在一些示例中，若在同一环境下使用同一仪器在短时间内对同一待测血液样本进行多次的重复检测时，所使用的感染标志参数组的数值的一致性越高，即数值重复性越高，则该感染标志参数组的优先级越高。
备选或附加地，若在同一环境下使用同一仪器在不同时间点对同一待测血液样本进行检测时，所使用的感染标志参数组的数值的稳定性越高(即数值的波动程度越小)，即老化稳定性越高，则该感染标志参数组的优先级越高。
备选或附加地，若在不同的温度环境下使用同一仪器对同一待测血液样本进行检测时，所使用的感染标志参数组的数值的稳定性越高(即数值的波动程度越小)，即温度稳定性越高，则该感染标志参数组的优先级越高。
备选或附加地，在同一环境下使用不同的仪器上对同一待测血液样本进行检测时，所使用的感染标志参数组的数值的一致性越高，即机间一致性越高，则该感染标志参数组的优先级越高。
在一些实施例中，所述参数局限性是指感染标志参数所适用的受试者范围。在一些示例中，若感染标志参数组所适用的受试者范围越大，则说明该感染标志参数组的参数局限性越小，相应地，该感染标志参数组的优先级越高。
在一些实施例中，处理器140所获取的所述多个感染标志参数组的优先级是预先设置的，例如根据感染诊断效力、参数稳定性和参数局限性中的至少一个预先设置的。在此，处理器140可以根据该预先设置为每个感染标志参数组配置优先级。例如，可以将所述多个感染标志参数组的优先级预先存储在存储器中，处理器140可以从存储器调用所述多个感染标志参数组的优先级。
接着，结合如下一些实施例对处理器140计算感染标志参数组的可信度的方式进行进一步说明。
本申请的发明人经研究发现，受试者的血液样本中可能存在分类结果异常和/或异常细胞，从而导致所使用的感染标志参数组不可靠。因此，本申请提供的血液分析仪可以为获取的多个感染标志参数组计算其可信度，以便根据每个感染标志参数组的优先级和可信度从多个感染标志参数组中筛选出更可靠的感染标志参数组。
在一些实施例中，处理器140可以被配置为按照如下方式计算每个感染标志参数组的可信度：
根据用于获取感染标志参数组的至少一个目标粒子团的分类结果和/或根据待测血液样本中的异常细胞计算该感染标志参数组的可信度。
在一些实施例中，所述分类结果可以包括目标粒子团的计数值、目标粒子团与另一粒子团的计数值百分比、目标粒子团与其相邻粒子团的交叠程度(也可称为粘连程度)中的至少一个。例如，目标粒子团与其相邻粒子团的交叠程度可以由目标粒子团的重心与其相邻粒子团的重心之间的距离确定。例如，如果目标粒子团的粒子总数、即计数值小于预设阈值，即目标粒子团的粒子较少，粒子表征的信息量有限，此时通过该目标粒子团的相关参数获得的感染标志参数组可能不可靠，因此该感染标志参数组的可信度较低。
接着，结合一些实施例对处理器140筛选感染标志参数组的方式进行进一步说明。
在本申请实施例中，处理器140可以被配置为，一次计算出所述多个感染标志参数组中的所有感染标志参数组的可信度，然后再根据所有感染标志参数组的优先级和可信度从其中选择至少一个感染标志参数组并输出其参数值。
在另一些实施例中，处理器140可以被配置为执行如下步骤以筛选感染标志参数组并输出其参数值：
从光学信息获取测定试样中的至少一个目标粒子团的多个参数；
从多个参数中获取用于评估所述受试者的感染状态的多个感染标志参数组；
按照多个感染标志参数组的优先级，依次计算多个感染标志参数组的可信度并判断该可信度是否达到相应的可信度阈值；
当当前感染标志参数组的可信度达到相应的可信度阈值时，输出该感染标志参数组的参数值并且停止计算和判断。
在一些实施例中，处理器140可以被进一步配置为：当所选择的感染标志参数组的参数值大于感染阳性阈值时，输出报警提示。
在此，例如也可以对各个感染标志参数组做归一化处理，确保各个感染标志参数的感染阳性阈值一致。
在另一些实施例中，处理器140还可以被配置为，从所述光学信息获取所述测定试样中的至少一个目标粒子团的多个参数，
从所述多个参数中获取用于评估所述受试者的感染状态的多个感染标志参数组，
计算所述多个感染标志参数组中的每个感染标志参数组的可信度，根据所述多个感染标志参数组的可信度从所述多个感染标志参数组中选择至少一个感染标志参数组并输出其参数值。
在一些实施例中，所述处理器可以被进一步配置为：
对于每个感染标志参数组，根据用于获取该感染标志参数组的至少一个目标粒子团的 分类结果和/或根据所述待测血液样本中的异常细胞计算该感染标志参数组的可信度。
所述分类结果例如可以包括目标粒子团的计数值、目标粒子团与另一粒子团的计数值百分比、目标粒子团与其相邻粒子团的交叠程度中的至少一个。
进一步地，所述处理器被进一步配置为：
当所选择的感染标志参数组的参数值大于感染阳性阈值时，输出报警提示。
在另一些实施例中，处理器140还可以被配置为，根据光学信息判断待测血液样本是否存在影响感染状态评估的异常；当判断待测血液样本存在影响感染状态评估的异常时，从光学信息获取与所述异常匹配的且用于评估受试者的感染状态的感染标志参数。
在一个示例中，若判断待测血液样本中存在影响感染状态评估的分类结果异常、例如待测血液样本中单核细胞团与中性粒细胞团存在交叠时，则可以从光学信息获取除单核细胞团和中性粒细胞团之外的其他细胞团(例如淋巴细胞团)的多个参数，并从其他细胞团的多个参数中获取用于评估受试者的感染状态的感染标志参数。
在另一个示例中，若判断待测血液样本中存在影响感染状态评估的异常细胞、例如原始细胞时，则可以从光学信息获取除了受原始细胞影响的细胞团之外的其他细胞团的多个参数，并从其他细胞团的多个参数中获取用于评估受试者的感染状态的感染标志参数。
接着，结合一些实施例对处理器140控制重测的方式进行进一步说明。
在一些实施例中，所述处理器可以被进一步配置为：
在从所述光学信息获得所述测定试样中的至少一个目标粒子团的至少一个白细胞特征参数之前，基于所述光学信息获得所述测定试样的白细胞计数，并且当所述白细胞计数小于预设阈值时输出对所述受试者的血液样本进行重新测定的重测指令，其中，基于所述重测指令的测定的样本测定量大于用于获取所述光学信息的测定的样本测定量；以及
所述处理器被进一步配置为，从基于所述重测指令测得的光学信息获得至少另一个目标粒子团的至少另一个白细胞特征参数，并且基于所述至少另一个白细胞特征参数获得用于评估所述受试者的感染状态的感染标志参数。
本申请还提供了再另一种血液分析仪，包括测定装置和控制器：
测定装置，用于将受试者的待测血液样本、溶血剂和染色剂混合以制备测定试样并且对该测定试样进行光学测定，以获取所述测定试样的光学信息；
控制器，被配置为：接收模式设定指令，
当模式设定指令表明选择了血常规检测模式时，控制所述测定装置对第一测定量的测定试样进行光学测定，以获取所述测定试样的光学信息，以及基于该光学信息获取并输出所述测定试样的血常规参数，
当模式设定指令表明选择了脓毒症检测模式时，控制所述测定装置对大于第一测定量的第二测定量的测定试样进行光学测定，以获取所述测定试样的光学信息，从所述光学信息获得所述测定试样中的至少一个目标粒子团的至少一个白细胞特征参数，基于所述至少一个白细胞特征参数获得用于评估所述受试者的感染状态的感染标志参数，以及输出所述感染标志参数。
为此，可以在当样本中的白细胞计数小于预设阈值导致测试的参数结果不可靠时，控制样本分析仪执行重测动作，从而获得更准确的感染标志参数，用于评估所述受试者的感染状态。
本申请实施例还提出一种用于提示受试者的感染状态的方法。如图12所示，所述方法200包括下列步骤：
S210，获取采集的所述受试者的待测血液样本；
S220，制备含有所述待测血液样本、溶血剂和用于识别有核红细胞的染色剂的测定试样；
S230，使所述测定试样中的粒子逐个通过被光照射的光学检测区，以获得所述测定试 样中的粒子在被光照射后所产生光学信息；
S240，从所述光学信息获得所述测定试样中的至少一个目标粒子团的至少一个白细胞特征参数；
S250，基于所述至少一个白细胞特征参数计算感染标志参数；并且
S260，根据所述感染标志参数评估所述受试者的感染状态并可选地输出指示所述受试者的感染状态的提示信息。
本申请实施例提出的方法200尤其是由本申请实施例提出的上述血液细胞分析仪100来实施。
在一些实施例中，所述方法可以进一步包括：根据所述光学信息识别所述测定试样中的有核红细胞以获得有核红细胞计数。
在一些实施例中，所述至少一个目标粒子团可以选自白细胞团、中性粒细胞团、淋巴细胞团中的一个或多个；优选所述至少一个目标粒子团包括白细胞团和/或中性粒细胞团。
在一些实施例中，所述感染标志参数可以选自以下细胞特征参数中的一个或者可以由以下细胞特征参数中的多个细胞特征参数组合得到、尤其是通过线性函数组合得到：
白细胞团的前向散射光强度分布重心、侧向散射光强度分布重心、侧向荧光强度分布重心、前向散射光强度分布宽度、侧向散射光强度分布宽度、侧向荧光强度分布宽度、前向散射光强度分布变异系数、侧向散射光强度分布变异系数、侧向荧光强度分布变异系数；
白细胞团在基于前向散射光强度、侧向散射光强度和侧向荧光强度中的两种光强度生成的二维散点图中的分布区域的面积、白细胞团在由前向散射光强度、侧向散射光强度和荧光强度生成的三维散点图中的分布区域的体积；
中性粒细胞团的前向散射光强度分布重心、侧向散射光强度分布重心、侧向荧光强度分布重心、前向散射光强度分布宽度、侧向散射光强度分布宽度、侧向荧光强度分布宽度、前向散射光强度分布变异系数、侧向散射光强度分布变异系数、侧向荧光强度分布变异系数；
中性粒细胞团在基于前向散射光强度、侧向散射光强度和侧向荧光强度中的两种光强度生成的二维散点图中的分布区域的面积、中性粒细胞团在由前向散射光强度、侧向散射光强度和荧光强度生成的三维散点图中的分布区域的体积；
淋巴细胞团的前向散射光强度分布重心、侧向散射光强度分布重心、侧向荧光强度分布重心、前向散射光强度分布宽度、侧向散射光强度分布宽度、侧向荧光强度分布宽度、前向散射光强度分布变异系数、侧向散射光强度分布变异系数、侧向荧光强度分布变异系数；以及
淋巴细胞团在基于前向散射光强度、侧向散射光强度和侧向荧光强度中的两种光强度生成的二维散点图中的分布区域的面积、淋巴细胞团在由前向散射光强度、侧向散射光强度和荧光强度生成的三维散点图中的分布区域的体积。
在一些实施例中，根据所述感染标志参数评估所述受试者的感染状态可以包括：基于所述述感染标志参数进行脓毒症的早期预测、脓毒症诊断、普通感染与重症感染的鉴别、感染病情监控、脓毒症的预后分析、细菌感染和病毒感染的鉴别、或非感染性炎症和感染性炎症的鉴别。
在一些实施例中，步骤S260可以包括：当所述感染标志参数满足第一预设条件时，输出指示所述受试者在被采集所述待测血液样本之后的一定时间段内可能进展为脓毒症的提示信息；优选的，所述一定时间段不大于48小时、尤其是在24小时之内。
在一些实施例中，步骤S260可以包括：当所述感染标志参数满足第二预设条件时，输出指示所述受试者患有脓毒症的提示信息。
在一些实施例中，步骤S260可以包括：当所述感染标志参数满足第三预设条件时，输出指示所述受试者患有重症感染的提示信息。
在一些实施例中，所述受试者为感染患者、尤其是患有重症感染或患有脓毒症的患者。相应地，步骤S260可以包括：根据所述感染标志参数监控所述受试者的感染病情发展。
在一些具体的示例中，根据所述感染标志参数监控所述受试者的感染病情发展包括：
获取通过连续多次检测在不同时间点来自受试者的血液样本而获得的所述感染标志参数的值；
根据通过所述连续多次的检测而获得的所述感染标志参数的值的变化趋势来判断所述受试者病情是否好转，优选当通过所述连续多次的检测而获得的所述感染标志参数的值逐渐趋势性减低时，输出指示所述受试者病情好转的提示信息。
在另一些的示例中，根据所述感染标志参数监控所述受试者的感染病情发展包括：
获取通过对来自受试者的当前血液样本的当前检测而获得的所述感染标志参数的当前值并且获取通过对来自受试者的前一次血液样本的前一次检测而获得的所述感染标志参数的在先值，例如前一天的血常规检测中获得的先值；并且
根据所述感染标志参数的在先值与第一阈值的比较以及所述感染标志参数的在先值与所述感染标志参数的当前值的比较来来监控所述受试者的病情发展。
此外，所述受试者可以为接受了治疗的脓毒症患者。相应地，步骤S260可以包括：根据所述感染标志参数判断所述受试者的脓毒症预后是否良好。例如，当所述感染标志参数满足第四预设条件时，输出指示所述受试者脓毒症预后良好的提示信息
在一些实施例中，步骤S260可以包括：根据所述感染标志参数判断所述受试者的感染类型是病毒感染还是细菌感染。
在一些实施例中，步骤S260可以包括：根据所述感染标志参数判断所述受试者是患感染性炎症还是非感染性炎症。例如，当所述感染标志参数满足第五预设条件时，输出指示所述受试者患感染性炎症的提示信息。
在一些实施例中，所述方法还可以包括：当所述目标粒子团的预设特征参数满足第六预设条件时，例如当所述目标粒子团的粒子总数小于预设阈值时，和/或当所述目标粒子团与其他粒子团存在交叠时，不输出所述感染标志参数的值，或者输出所述感染标志参数的值并且同时输出指示该感染标志参数的值不可靠的提示信息。
备选地或附加地，所述方法还可以包括：当所述受试者患有血液疾病或者在所述待测血液样本中存在异常细胞、尤其是原始细胞时，例如当根据所述光学信息判断所述待测血液样本中存在异常细胞、尤其是原始细胞时，不输出所述感染标志参数的值，或者输出所述感染标志参数的值并且同时输出指示该感染标志参数的值不可靠的提示信息。
本申请实施例提出的方法200的更多实施例和优点可参考上述对本申请实施例提出的血液细胞分析仪100的描述、尤其是对处理器140所实施的方法步骤的描述，在此不再赘述。
本申请实施例还提出感染标志参数在评估受试者的感染状态中的用途，其中，通过如下方法获得所述感染标志参数：
获取通过流式细胞术对含有来自受试者的待测血液样本、溶血剂和用于识别有核红细胞的染色剂的测定试样检测得到的至少一个目标粒子团的至少一个白细胞特征参数；并且
基于所述至少一个白细胞特征参数计算感染标志参数。
本申请实施例提出的感染标志参数在评估受试者的感染状态中的用途的更多实施例和优点可参考上述对本申请实施例提出的血液细胞分析仪100的描述、尤其是对处理器140所实施的方法步骤的描述，在此不再赘述。
接下来通过一些具体的实施例来进一步说明本申请及其优点。
本申请实施例的真阳率％、假阳率％、真阴率％以及假阴率％通过如下公式计算：
真阳率％＝TP/(TP+FN)×100％；
真阴率％＝TN/(FP+TN)×100％；
假阳率％＝1-真阴率％；
假阴率％＝1-真阳率％；
其中，TP为真阳性个体数，FP为假阳性个体数，TN为真阴性个体数，FN为假阴性个体数。
实施例1脓毒症早期预测
使用深圳迈瑞生物医疗电子股份有限公司生产的BC-6800Plus血液细胞分析仪，使用迈瑞公司的配套的溶血剂M-60LD、M-6LN和染色剂M-6FD、M-6FN，对来自152例供者的血液样本进行血常规检测，得到WNB通道和DIFF通道的散点图，按照本申请实施例提出的方法进行脓毒症早期预测。第二天，这些样本中，87例血液样本被临床诊断为脓毒症的阳性样本，65例血液样本为阴性样本(不发展为脓毒症)。
这152例供者的纳入标准：存在或疑似急性感染的成年ICU患者。排除标准：妊娠人群、化疗骨髓抑制者、免疫抑制剂治疗者、血液系统疾病患者。
所述脓毒症样本的供者：有可疑或有明确感染部位，实验室培养结果阳性，存在器官功能衰竭；疑似或确定急性感染，并且SOFA≥2分，其中疑似感染为具有以下①～③中任一项和④无确定性结果；或具有以下①～③中任一项和⑤。
①有急性(72h之内)发热或低体温；
②白细胞总数增高或降低；
③CRP、IL-6升高
④PCT、SAA及HBP升高；
⑤有可疑的感染部位。
SOFA评分标准如下表A所示：
Figure PCTCN2022143965-appb-000018
表7示出所使用的感染标志参数及其相应的诊断效力，图13和图14示出对应表7中的感染标志参数的ROC曲线。在表7中：
组合参数1＝0.00174639×N_WBC_FL_P+0.00788254×N_WBC_FS_W-10.4569；
组合参数2＝0.00160514×N_WBC_SS_W+0.00480886×N_WBC_FS_W-6.62685；
组合参数3＝0.00278754×N_WBC_FL_W+0.00010201×N_NEU_FLSS_Area。
表7 不同感染标志参数用于早期预测脓毒症风险的效力
Figure PCTCN2022143965-appb-000019
Figure PCTCN2022143965-appb-000020
此外，表8-1至8-4示出了本实施例中采用其他参数组合用于早期预测脓毒症风险的效力,其中，基于表中的参数组合通过函数Y＝A×X1+B×X2+C计算感染标志参数，其中，Y表示感染标志参数，X1表示第一白细胞参数，X2表示第二白细胞参数，A、B、C为常数。
表8-1 含N_WBC_SS_W的参数组合用于早期预测脓毒症风险的效力
Figure PCTCN2022143965-appb-000021
Figure PCTCN2022143965-appb-000022
表8-2 含N_WBC_FL_W的参数组合用于早期预测脓毒症风险的效力
Figure PCTCN2022143965-appb-000023
表8-3 含N_WBC_FS_W的参数组合用于早期预测脓毒症风险的效力
Figure PCTCN2022143965-appb-000024
Figure PCTCN2022143965-appb-000025
表8-4 其余参数组合用于早期预测脓毒症风险的效力
Figure PCTCN2022143965-appb-000026
Figure PCTCN2022143965-appb-000027
表8-5，使用现有技术的PCT(降钙素原)，以及DIFF通道的参数用于早期预测脓毒症风险的效力
Figure PCTCN2022143965-appb-000028
由表8-5与表8-1、8-2、8-3、8-4比较可知，WNB通道参数较DIFF通道参数和PCT用于脓毒症预测有更优的诊断性能。表中D_Neu_SS_W是指，DIFF通道散点图中中性粒细胞团的侧向散射光强度的分布宽度；D_Neu_FL_W是指，DIFF通道散点图中中性粒细胞团的荧光强度的分布宽度；D_Neu_FS_W是指，DIFF通道散点图中中性粒细胞团的前 向散射光强度的分布宽度。
表8-6，以3个参数为例说明本实施例使用的统计方法和检验方法
Figure PCTCN2022143965-appb-000029
由表8-6可知，该参数通过Welch检验分析，两组间存在显著统计学差异(p＜0.0001.)
由表7和表8-1至8-6可知，本申请提出的感染标志参数能够用于较有效地提前一天预测脓毒症风险，可以在病人未出现脓毒症的症状的提前一天预测病人将发展为脓毒症，诊疗效能优于现有的PCT标准物，且令人意外地，利用血常规的WNB通道的散点图的特征，相较DIFF通道散点图的特征，具有很好的诊疗效能。通常认为，DIFF通道的功能是白细胞四分类，能更精准地区分各类白细胞亚群，更容易在散点图数据中找到与感染相关的特征，而WNB通道溶血强度相对比较弱，不同类型白细胞亚群的区分度不如DIFF通道，不容易找到与感染相关的特征。但发明人通过深入研究意外发现，WNB通道可以找到比DIFF通道更好用的特征来预测脓毒症的发展，虽然不希望受理论约束，发明人推测细胞经WNB通道的试剂处理后，与感染相关的单核细胞、未成熟粒细胞、异型淋巴细胞均分布于散点图中荧光信号比较强且侧向散射光信号比较强的位置。病人发生感染后，这些细胞在散点图中的数量和位置均会发生显著变化，同时其他与感染无关的细胞没有发生明显改变，因此感染后WNB通道的散点图发生的变化会更加显著，且更容易被检测装置捕捉到。
实施例2普通感染与重症感染的鉴别
使用深圳迈瑞生物医疗电子股份有限公司生产的BC-6800Plus血液细胞分析仪按照本申请实施例1类似的步骤，对来自1548例供者的血液样本进行血常规检测，基于散点图采用前述的方法进行重症感染鉴别。其中，重症感染样本、即阳性样本756例，非重症感染样本、即阴性样本792例。
本实施例中1548例供者的纳入标准：存在或疑似急性感染的成年ICU患者。排除标准：妊娠人群、化疗骨髓抑制者、免疫抑制剂治疗者、血液系统疾病患者。
其中所述重症感染样本的供者：有可疑或有明确感染部位，实验室培养结果阳性，存在器官功能损伤，其符合以下任意一项或多项：
①存在全身性、广泛性、体腔播散性感染证据
②存在危及生命的特殊部位感染
③感染引起至少一项感染引起的器官功能指标异常
其他为非重症感染样本。
表9示出所使用的感染标志参数及其相应的诊断效力，图15和图16示出对应表9中的感染标志参数的ROC曲线。在表9中：
组合参数1＝0.003755×N_WBC_FL_P+0.009192×N_WBC_FS_W-15.0973；
组合参数2＝0.005945×N_WBC_FL_W+0.000248×N_NEU_FL_P-6.62685；
组合参数3＝0.005249×N_WBC_SS_P+0.005132×N_NEU_FL_W-13.216。
表9 不同感染标志参数用于鉴别普通感染与重症感染的效力
Figure PCTCN2022143965-appb-000030
Figure PCTCN2022143965-appb-000031
真阳是指该实施例获知的提示结果与病人临床情况吻合均为重症感染患者；假阳是指该实施例获知的提示结果为重症感染，但病人实际情况是普通感染；真阴是指该实施例获得的提示结果与病人临床情况吻合均为普通感染患者；假阴是指该实施例获知的提示结果为普通感染，但病人实际情况是重症感染。
此外，表10示出了本实施例中采用其他单个白细胞特征参数作为感染标志参数来鉴别普通感染与重症感染的效力，表11-1至11-4示出了本实施例中采用其他组合参数作为感染标志参数来鉴别普通感染与重症感染的效力,其中，基于表11中的参数组合通过函数Y＝A×X1+B×X2+C计算感染标志参数，其中，Y表示感染标志参数，X1表示第一白细胞参数，X2表示第二白细胞参数，A、B、C为常数。
表10 其他单个白细胞特征参数用于鉴别普通感染与重症感染的效力
单参数 ROC_AUC 判断阈值 假阳率％ 真阳率％ 真阴率％ 假阴率％
N_WBC_FL_P 0.8106 >1599.2285 23.9 74 76.1 26
N_NEU_FL_W 0.8079 >1360 25 71.3 75 28.7
N_NEU_FL_P 0.8013 >1715.2215 28.4 76.8 71.6 23.2
N_NEU_FLFS_Area 0.7859 >7459.84 21.1 66.7 78.9 33.3
N_WBC_SS_W 0.7821 >1328 23.6 70.1 76.4 29.9
N_WBC_FS_W 0.7786 >944 30.8 72.8 69.2 27.2
N_NEU_FS_W 0.7705 >624 30.3 68.1 69.7 31.9
N_WBC_FLSS_Area 0.7651 >12835.84 28.8 69.2 71.2 30.8
N_NEU_SS_W 0.7618 >1168 28.4 70.5 71.6 29.5
N_WBC_FLFS_Area 0.7555 >9620.48 24.6 65.3 75.4 34.7
N_NEU_FS_CV 0.7495 >0.4405 28 65.6 72 34.4
N_NEU_SS_P 0.7406 >1162.0325 33.2 68.9 66.8 31.1
N_WBC_FS_CV 0.7152 >0.7445 29.4 62.1 70.6 37.9
N_NEU_SSFS_Area 0.7148 >6814.72 29.8 62.2 70.2 37.8
N_WBC_FS_P 0.7125 >1284.492 33.8 66.4 66.2 33.6
N_WBC_SS_CV 0.7108 >1.1665 25.9 61.3 74.1 38.7
表11-1，含N_WBC_FL_W的参数组合用于鉴别普通感染与重症感染的效力
Figure PCTCN2022143965-appb-000032
Figure PCTCN2022143965-appb-000033
表11-2，含N_NEU_FLSS_Area的参数组合用于鉴别普通感染与重症感染的效力
Figure PCTCN2022143965-appb-000034
Figure PCTCN2022143965-appb-000035
表11-3，含N_WBC_SS_P的参数组合用于鉴别普通感染与重症感染的效力
Figure PCTCN2022143965-appb-000036
Figure PCTCN2022143965-appb-000037
表11-4，其余参数组合用于鉴别普通感染与重症感染的效力
Figure PCTCN2022143965-appb-000038
Figure PCTCN2022143965-appb-000039
Figure PCTCN2022143965-appb-000040
Figure PCTCN2022143965-appb-000041
表11-5，使用现有技术的PCT(降钙素原)，以及DIFF通道的参数用于鉴别普通感染与重症感染的效力
感染标志参数 ROC_AUC 判断阈值 假阳率 真阳率 真阴率 假阴率
PCT 0.806 >0.46 31.8％ 80.5％ 68.2％ 19.5％
D_Neu_SS_W 0.664 >259.324 39.3％ 633.3％ 60.7％ 36.7％
D_Neu_FL_W 0.758 >220.767 13.6％ 54.3％ 86.4％ 45.7％
D_Neu_FS_W 0.542 >572.274 34.3％ 41.9％ 65.7％ 58.1％
现有技术有报道(Crouser E,Parrillo J,Seymour C et al.Improved Early Detection of Sepsis in the ED With a Novel Monocyte Distribution Width Biomarker.CHEST.2017；152(3):518-526)，在BCI的血液分析仪DIFF通道血常规散点图，利用中性粒细胞的分布宽度鉴别普通感染与重症感染，其ROC_AUC为0.79，判断阈值为>20.5，假阳率为27％，真阳率为77.0％，真阴率为73％，假阴率为23％。从报道的数据看，与迈瑞的DIFF通道 用于鉴别普通感染与重症感染的效力类似。
由表11-5与表9、10、11-1、11-2、11-3、11-4比较可知，WNB通道参数在重症感染鉴别诊断上，具有和PCT类似甚至更优的诊断效能，有可能代替PCT标志物，实现利用血常规的检测数据，无成本地给出鉴别普通感染与重症感染的提示；另外，WNB通道参数较DIFF通道参数用于重症感染鉴别诊断有更优的诊断性能。
表11-6，以三个参数为例说明本实施例使用的统计方法和检验方法
Figure PCTCN2022143965-appb-000042
由表11-6可知，该参数通过Welch检验分析，两组间存在显著统计学差异(p＜0.0001)
由表9、10、表11-1至11-6可知，本申请提出的感染标志参数能够用于较有效地判断受试者是否患有重症感染。基于和实施例1相同的原因，本申请通过深入研究意外发现，WNB通道可以找到比DIFF通道更好用的特征来鉴别重症感染和普通感染。
实施例3脓毒症诊断
使用深圳迈瑞生物医疗电子股份有限公司生产的BC-6800Plus血液细胞分析仪按照本申请实施例1类似的步骤对1748例血液样本进行血常规检测，基于散点图采用前述的方法进行脓毒症诊断。其中，脓毒症样本、即阳性样本506例，非脓毒症样本、即阴性样本1242例。
这1748例的纳入标准：存在或疑似急性感染的成年ICU患者。排除标准：妊娠人群、化疗骨髓抑制者、免疫抑制剂治疗者、血液系统疾病患者。
表12示出所使用的感染标志参数及其相应的诊断效力，图17和18示出对应表12中的感染标志参数的ROC曲线。在表12中：
组合参数1＝0.004088×N_WBC_FL_P+0.009059×N_WBC_FS_W；
组合参数2＝0.006086×N_WBC_FL_W-0.00017×N_NEU_FL_W；
组合参数3＝0.007722×N_WBC_SS_P+0.003547×N_WBC_FL_P。
表12 不同感染标志参数用于诊断脓毒症的效力
感染标志参数 ROC_AUC 判断阈值 假阳率 真阳率 真阴率 假阴率
N_WBC_FL_W 0.873 >1872 20.8％ 80％ 79.2％ 20％
N_NEU_FL_W 0.806 >1360 28.7％ 75.3％ 71.3％ 24.7％
N_NEU_FLSS_Area 0.772 >10951.68 25.2％ 67.8％ 74.8％ 32.2％
组合参数1 0.881 >-1.0161 19.4％ 81.2％ 80.6％ 18.8％
组合参数2 0.874 >-1.1218 21.3％ 81.2％ 78.7％ 18.8％
组合参数3 0.851 >-1.1751 25.5％ 82％ 74.5％ 18％
此外，表13示出了本实施例中采用其他单个白细胞特征参数作为感染标志参数用于诊断脓毒症的效力，表14示出了本实施例中采用其他参数组合作为感染标志参数用于诊断脓毒症的效力,其中，基于表14中的参数组合通过函数Y＝A×X1+B×X2+C计算感染标志参数，其中，Y表示感染标志参数，X1表示第一白细胞参数，X2表示第二白细胞参数，A、B、C为常数。
表13 其他单个参数用于诊断脓毒症的效力
参数 ROC_AUC 判断阈值 假阳率 真阳率 真阴率 假阴率
      ％ ％ ％ ％
N_WBC_FL_P 0.832 >1638.1165 23.3 76.2 76.7 23.8
N_NEU_FL_P 0.8246 >1812.7065 21.5 73.5 78.5 26.5
N_WBC_SS_W 0.7869 >1328 27.7 75.5 72.3 24.5
N_NEU_FLFS_Area 0.78 >7429.12 26.2 70.8 73.8 29.2
N_WBC_FS_W 0.7782 >976 21.6 67.6 78.4 32.4
N_NEU_FS_W 0.7738 >624 31.4 72.7 68.6 27.3
N_NEU_SS_W 0.7641 >1168 31.5 74.3 68.5 25.7
N_WBC_SS_P 0.7595 >1145.5385 30.6 71.3 69.4 28.7
N_NEU_SS_P 0.7578 >1162.0325 33.2 74.3 66.8 25.7
N_WBC_FLSS_Area 0.7543 >12876.8 30.3 71.1 69.7 28.9
N_NEU_FS_CV 0.7515 >0.4405 30.5 68.3 69.5 31.7
表14-1，含N_WBC_FL_W的参数组合用于诊断脓毒症的效力
Figure PCTCN2022143965-appb-000043
表14-2，含N_NEU_FL_W的参数组合用于诊断脓毒症的效力
Figure PCTCN2022143965-appb-000044
Figure PCTCN2022143965-appb-000045
表14-3，含N_NEU_FLSS_Area的参数组合用于诊断脓毒症的效力
Figure PCTCN2022143965-appb-000046
Figure PCTCN2022143965-appb-000047
表14-4含N_WBC_FL_P的参数组合用于诊断脓毒症的效力
Figure PCTCN2022143965-appb-000048
表14-5，其余参数组合用于诊断脓毒症的效力
Figure PCTCN2022143965-appb-000049
Figure PCTCN2022143965-appb-000050
Figure PCTCN2022143965-appb-000051
Figure PCTCN2022143965-appb-000052
表14-6，使用现有技术的PCT(降钙素原)，以及DIFF通道的参数用于诊断脓毒症的效力
感染标志参数 ROC_AUC 判断阈值 假阳率 真阳率 真阴率 假阴率
PCT 0.787 0.64 37.3％ 81.0％ 62.7％ 19.0％
D_Neu_SS_W 0.687 252.764 45.4％ 74.1％ 54.6％ 25.9％
D_Neu_FL_W 0.791 213.465 22.8％ 68.0％ 77.2％ 32.0％
D_Neu_FS_W 0.545 586.385 22.6％ 32.2％ 77.4％ 67.8％
由表14-6与表14-1至14-5比较可知，WNB通道参数较DIFF通道参数和PCT用于脓毒症诊断有更优的诊断性能。
表14-7，以三个参数为例说明本实施例使用的统计方法和检验方法
Figure PCTCN2022143965-appb-000053
由表14-7可知，该参数通过Welch检验分析，两组间存在显著统计学差异(p＜0.0001)
由表12至表14可知，本申请提出的感染标志参数能够用于较有效地判断受试者是否患有脓毒症。基于和实施例1相同的原因，本申请通过深入研究意外发现，WNB通道可以找到比DIFF通道更好用的特征来诊断脓毒症。
实施例4重症感染病情监控
使用深圳迈瑞生物医疗电子股份有限公司生产的BC-6800Plus血液细胞分析仪按照本 申请实施例1的步骤，对50例重症感染患者的血液样本进行连续血常规检测，基于散点图采用前述的方法监控重症感染病情发展。根据患者重症感染诊断后第7天病情状况对50例重症感染患者进行分组。若诊断后第7天患者感染程度好转且病情稳定纳入好转组(阳性样本N＝26)。若感染程度无明显好转，患者仍处于重症感染阶段或患者死亡则纳入加重组(阴性样本N＝24)。表15示出所使用的感染标志参数及其相应实验数据(两组患者的感染标志参数值的平均值)，图19示出采用单参数N_WBC_FL_P作为感染标志参数进行监控的动态趋势变化图，图20示出采用单参数N_WBC_FS_W作为感染标志参数进行监控的动态趋势变化图，以及图21示出采用N_WBC_FL_P与N_WBC_FS_W的线性组合参数(N_WBC_FL_P*0.003755+N_WBC_FS_W*0.009192)作为感染标志参数进行监控的动态趋势变化图，其中，以重症感染诊断后天数为横轴，两组患者的感染标志参数值的平均值为纵轴。
表15 不同感染标志参数及其相应实验数据
Figure PCTCN2022143965-appb-000054
由表15以及图19-21可知，本申请提出的感染标志参数能够用于较有效地监控重症感染患者的感染发展状况。
实施例5脓毒症病情监控
使用深圳迈瑞生物医疗电子股份有限公司生产的BC-6800Plus血液细胞分析仪按照本申请实施例1的步骤，对76例脓毒症患者的血液样本进行连续血常规检测，基于散点图采用前述的方法监控脓毒症病情发展。根据患者脓毒症诊断后第7天病情状况对76例脓毒症患者进行分组。若诊断后第7天患者感染程度好转且病情稳定纳入好转组(阳性样本N＝55)。若感染程度无明显好转，患者仍处于重症感染阶段或患者死亡则纳入加重组(阴性样本N＝21)。表16示出所使用的感染标志参数及其相应实验数据(两组患者的感染标志参数值的中位数)。图22示出采用N_WBC_FL_W作为感染标志参数进行监控的动态趋势变化图，图23示出采用N_WBC_FL_P和N_WBC_FS_W的线性组合(0.0040875*N_WBC_FL_P+0.00905881*N_WBC_FS_W)作为感染标志参数进行监控的动态趋势变化图，其中，以脓毒症诊断后天数为横轴，两组患者的感染标志参数值的中位数为纵轴。
表16 不同感染标志参数及其相应实验数据
Figure PCTCN2022143965-appb-000055
由表16以及图22和图23可知，本申请提出的感染标志参数能够用于较有效地监控受试者的脓毒症发展状况。
实施例6脓毒症预后分析
使用深圳迈瑞生物医疗电子股份有限公司生产的BC-6800Plus血液细胞分析仪按照本申请实施例1的步骤，对270例血液样本进行检测，基于散点图采用前述的方法进行脓毒症预后分析。其中，28天死亡的阳性样本68例，28天存活的阴性样本202例。表17示出了本实施例中采用单个白细胞特征参数作为感染标志参数用于判断脓毒症预后是否良好的效力，表18示出了本实施例中采用参数组合作为感染标志参数用于判断脓毒症预后是否良好的效力，其中，基于表18中的参数组合通过函数Y＝A×X1+B×X2+C计算感染标志参数，其中，Y表示感染标志参数，X1表示第一白细胞参数，X2表示第二白细胞参数，A、B、C为常数。
表17 单参数用于判断脓毒症预后是否良好的效力
单参数 ROC_AUC 判断阈值 假阳率％ 真阳率％ 真阴率％ 假阴率％
N_WBC_FL_W 0.7964 >2128 21.3 67.6 78.7 32.4
N_WBC_FS_W 0.7371 >1040 26.7 70.6 73.3 29.4
N_WBC_FLSS_Area 0.7118 >14494.72 39.1 70.6 60.9 29.4
N_WBC_FS_CV 0.7073 >0.7875 32.7 66.2 67.3 33.8
N_WBC_FLFS_Area 0.7033 >10726.4 30.2 63.2 69.8 36.8
表18 双参数用于判断脓毒症预后是否良好的效力
Figure PCTCN2022143965-appb-000056
Figure PCTCN2022143965-appb-000057
由表17和表18可知，本申请提出的感染标志参数能够用于较有效地判断患者脓毒症预后情况。
实施例7细菌感染和病毒感染的鉴别
使用深圳迈瑞生物医疗电子股份有限公司生产的BC-6800Plus血液细胞分析仪按照本申请实施例1的步骤，对491例血液样本进行检测，基于散点图采用前述的方法进行感染类型判断。其中，细菌感染样本,(即阳性样本)237例，病毒感染样本254例。
这些病例的纳入标准：存在或疑似急性感染的成年ICU患者。排除标准：妊娠人群、化疗骨髓抑制者、免疫抑制剂治疗者、血液系统疾病患者。
所述细菌感染样本：有可疑或明确感染部位，实验室细菌培养结果阳性，即，同时满足①-③
①细菌感染证据：(符合以下1－4中任一项即可)
1.有明确的感染部位
2.炎症指标(WBC、CRP和PCT等)升高
3.微生物培养阳性
4.影像学结果提示感染
②SOFA评分较基线变化＜2分
③临床公认的器官衰竭指标评分变化＜2分
所述病毒感染样本：有可疑或明确感染部位，病毒抗原或抗体检测阳性。例如，符合以下任一项即可：
①甲型流感病毒或乙型流感病毒抗体检测阳性
②EB病毒抗体检测阳性
③巨细胞病毒抗体检测阳性。
表19示出了本实施例中采用单个白细胞特征参数作为感染标志参数用于鉴别细菌感染和病毒感染的效力，表20-1示出了本实施例中采用参数组合作为感染标志参数用于鉴别细菌感染和病毒感染的效力,其中，基于表20-1中的参数组合通过函数Y＝A×X1+B×X2+C计算感染标志参数，其中，Y表示感染标志参数，X1表示第一白细胞参数，X2表示第二白细胞参数，A、B、C为常数。
表19 单参数用于鉴别细菌感染和病毒感染的效力
单参数 ROC_AUC 判断阈值 假阳率％ 真阳率％ 真阴率％ 假阴率％
N_WBC_FS_P 0.879 >1318.3915 18.9 78 81.1 22
N_WBC_FL_P 0.8648 >1450.1095 19.3 79.2 80.7 20.8
N_WBC_FS_W 0.8501 >1008 15 73.7 85 26.3
N_WBC_FL_W 0.8442 >1744 22.4 73.3 77.6 26.7
N_WBC_FLFS_Area 0.8176 >9313.28 22 74.2 78 25.8
N_WBC_FLSS_Area 0.8046 >11909.12 20.1 70.3 79.9 29.7
N_WBC_SS_P 0.7925 >1137.061 27.6 70.8 72.4 29.2
N_WBC_SS_W 0.7297 >1328 24.4 66.9 75.6 33.1
表20-1 双参数用于鉴别细菌感染和病毒感染的效力
Figure PCTCN2022143965-appb-000058
Figure PCTCN2022143965-appb-000059
表20-2，使用现有技术的PCT(降钙素原)，以及DIFF通道的参数用于鉴别细菌感染和病毒感染的效力
感染标志参数 ROC_AUC 判断阈值 假阳率 真阳率 真阴率 假阴率
PCT 0.851 0.554 7.9％ 67.3％ 92.1％ 32.7％
D_Neu_SS_W 0.733 259.275 24.4％ 60.2％ 75.6％ 39.8％
D_Neu_FL_W 0.836 206.183 20.1％ 75.0％ 79.9％ 25.0％
D_Neu_FS_W 0.601 611.240 34.6％ 56.4％ 65.4％ 43.6％
由表20-2，与表19、20-1比较可知，WNB通道参数在细菌感染鉴别上，具有和PCT 有类似，甚至是更优的诊疗效力，另外，相比DIFF通道参数在用于细菌感染鉴别诊断上也有更优的诊断性能。
表20-3，以三个参数为例说明本实施例使用的统计方法和检验方法
Figure PCTCN2022143965-appb-000060
由表20-3可知，该参数通过Welch检验分析，两组间存在显著统计学差异(p＜0.0001)
由表19和表20-1、20-2可知，本申请提出的感染标志参数能够用于较有效地对细菌感染和病毒感染进行鉴别。基于和实施例1相同的原因，本申请通过深入研究意外发现，WNB通道可以找到比DIFF通道更好用的特征来鉴别细菌感染和病毒感染。
实施例8感染性炎症和非感染性炎症的鉴别
使用深圳迈瑞生物医疗电子股份有限公司生产的BC-6800Plus血液细胞分析仪按照本申请实施例1的步骤，对515例血液样本进行检测，基于散点图采用前述的方法进行感染性炎症鉴别。其中，感染性炎症样本、即阳性样本399例，非感染性炎症样本、即阴性样本116例。
这些病例的纳入标准：存在或疑似急性炎症的成年ICU患者。排除标准：妊娠人群、化疗骨髓抑制者、免疫抑制剂治疗者、血液系统疾病患者。
所述感染性炎症样本：有细菌和/或病毒感染证据；且存在炎症(符合以下任一项即可)
1.局部炎症表现或全身炎症反应表现
2.组织损伤：由物理或化学因素导致的损伤如高温、低温、放射性物质及紫外线等
3.机械损伤：化学物质如强酸、强碱等损伤
4.组织坏死：缺血或缺氧等原因引起组织坏死损伤
5.变态反应：机体免疫反应状态异常如自身免疫性疾病
所述非感染性炎症样本：由物理、化学等因素引起的炎症反应，同时满足①和②：
①无细菌感染证据
②存在炎症(符合以下任一项即可)
1.局部炎症表现或全身炎症反应表现
2.组织损伤：由物理或化学因素导致的损伤如高温、低温、放射性物质及紫外线等
3.机械损伤：化学物质如强酸、强碱等损伤
4.组织坏死：缺血或缺氧等原因引起组织坏死损伤
5.变态反应：机体免疫反应状态异常如自身免疫性疾病
表21示出了本实施例中采用单个白细胞特征参数作为感染标志参数用于判断感染性炎症的效力，表22-1示出了本实施例中采用参数组合作为感染标志参数用于判断感染性炎症的效力,其中，基于表22-1中的参数组合通过函数Y＝A×X1+B×X2+C计算感染标志参数，其中，Y表示感染标志参数，X1表示第一白细胞参数，X2表示第二白细胞参数，A、B、C为常数。
表21 单参数用于鉴别感染性炎症和非感染性炎症的效力
Figure PCTCN2022143965-appb-000061
Figure PCTCN2022143965-appb-000062
表22-1 双参数用于鉴别感染性炎症和非感染性炎症的效力
Figure PCTCN2022143965-appb-000063
Figure PCTCN2022143965-appb-000064
表22-2，使用现有技术的PCT(降钙素原)，以及DIFF通道的参数用于鉴别感染性炎症和非感染性炎症的效力
感染标志参数 ROC_AUC 判断阈值 假阳率 真阳率 真阴率 假阴率
PCT 0.855 0.44 32.1％ 89.6％ 67.9％ 10.4％
D_Neu_SS_W 0.744 290.101 7.8％ 45.7％ 92.2％ 54.3％
D_Neu_FL_W 0.836 220.534 14.7％ 67.3％ 85.3％ 32.7％
D_Neu_FS_W 0.557 563.910 37.9％ 51.3％ 62.1％ 48.7％
由表22-2与表21、22-1比较可知，WNB通道参数在鉴别感染性炎症和非感染性炎症上，具有和PCT类似，甚至更优的诊疗效力；另外，较DIFF通道参数在用于感染性炎症和非感染性炎症的鉴别上有更优的诊断性能。
表22-3，以三个参数为例说明本实施例使用的统计方法和检验方法
Figure PCTCN2022143965-appb-000065
由表22-3可知，该参数通过Welch检验分析，两组间存在显著统计学差异(p＜0.0001)
由表21和22-1、22-2可知，本申请提出的感染标志参数能够用于较有效地鉴别感染性炎症和非感染性炎症。基于和实施例1相同的原因，本申请通过深入研究意外发现，WNB通道可以找到比DIFF通道更好用的特征来鉴别感染性炎症和非感染性炎症。
实施例9脓毒症疗效的评估
使用深圳迈瑞生物医疗电子股份有限公司生产的BC-6800Plus血液细胞分析仪，按实施例1的步骤，对接受脓毒症治疗的28例患者的血液样本进行血常规检测，基于散点图采用前述的方法进行脓毒症疗效的评估。具体地，对诊断为脓毒症的28例患者使用抗生素治疗，5天后对患者血液样本进行血常规检测，获得下表的参数。根据5天治疗效果分为有效组和无效组，临床上症状明显改善为有效组，否则为无效组。其中，11例患者属于无效组，17例患者属于有效组。
表23示出了本实施例中采用单个白细胞特征参数作为感染标志参数用于判断对脓毒症的疗效。其中，N_FL_PULWID_MEAN是指，WNB通道散点图白细胞团中粒子的侧向荧光信号的脉冲宽度的平均值；N_FS_PULWID_MEAN是指，WNB通道散点图白细胞团中粒子的前向散射光信号的脉冲宽度的平均值；N_SS_PULWID_MEAN是指，WNB通道散点图白细胞团中粒子的侧向散射光信号的脉冲宽度的平均值；N_WBC_FL_R是指，WNB通道散点图白细胞团侧向荧光强度分布右边界值(如图6所示)。
表23 单参数用于判断对脓毒症的疗效
Figure PCTCN2022143965-appb-000066
Figure PCTCN2022143965-appb-000067
图24A－图24D直观地显示了使用N_WBC_FL_P作为单参数对脓毒症疗效的检测结果。
图25A－图25D直观地显示了使用N_FL_PULWID_MEAN作为单参数对脓毒症疗效的检测结果。
图26A－图26D直观地显示了使用N_FS_PULWID_MEAN作为单参数对脓毒症疗效的检测结果。
表24示出了本实施例中采用双参数“N_WBC_FL_P”和“N_WBC_FS_W”组合作为感染标志参数用于判断对脓毒症的疗效。该双参数组合的物理意义是将第一检测通道WBC粒子内部核酸含量的重心位置与第一检测通道WBC粒子体积的分布宽度进行组合。
该双参数组合通过函数
Y＝0.0040875×N_WBC_FL_P+0.00905881×N_WBC_FS_W-16.60028217计算感染标志参数，其中，Y表示感染标志参数。
表24
Figure PCTCN2022143965-appb-000068
图27A－图27D直观地显示了使用双参数“N_WBC_FL_P”和“N_WBC_FS_W”组合作为感染标志参数对脓毒症疗效的检测结果。
表25示出了本实施例中采用双参数“N_WBC_FL_W”和“N_WBC_FS_P”组合作为感染标志参数用于判断对脓毒症的疗效。该双参数组合的物理意义是将第一检测通道WBC粒子内部核酸含量的分布宽度与第一检测通道WBC粒子体积的重心位置进行组合。
该双参数组合通过函数
Y＝0.00609253×N_WBC_FL_W+0.00587667×N_WBC_FS_P-20.07103538获得感染标志参数，其中，Y表示感染标志参数。
表25
Figure PCTCN2022143965-appb-000069
图28A－图28D直观地显示了使用双参数“N_WBC_FL_W”和“N_WBC_FS_P”组合作为感染标志参数对脓毒症疗效的检测结果。
表26示出了本实施例中采用双参数“N_WBC_FL_P”和“N_WBC_FS_CV”组合作为感染标志参数用于判断对脓毒症的疗效。该双参数组合的物理意义是将第一检测通道WBC粒子内部核酸含量的中心位置和第一检测通道WBC粒子体积的离散程度进行组合。
该双参数组合通过函数
Y＝0.00462573×N_WBC_FL_P+12.43796108×N_WBC_FS_CV-18.03119401获得感染标志参数，其中，Y表示感染标志参数。
表26
Figure PCTCN2022143965-appb-000070
图29A－图29D直观地显示了使用双参数“N_WBC_FL_P”和“N_WBC_FS_CV”组合作为感染标志参数对脓毒症疗效的检测结果。
表27示出了本实施例中采用DIFF+WNB双通道参数“N_WBC_FL_W”和“D_Neu_FL_W”组合作为感染标志参数用于判断对脓毒症的疗效。该双参数组合的物理意义是将第一检测通道WBC粒子内部核酸含量的分布宽度和第二检测通道中性粒细胞内部核酸含量的分布宽度进行组合。
该双参数组合通过函数
Y＝0.00623272×N_WBC_FL_W+0.01806527×D_Neu_FL_W-16.84312131获得感染标志参数，其中，Y表示感染标志参数。
表27
Figure PCTCN2022143965-appb-000071
图30A－图30D直观地显示了使用双参数“N_WBC_FL_W”和“D_Neu_FL_W”组合作为感染标志参数对脓毒症疗效的检测结果。
表28示出了本实施例中采用DIFF+WNB双通道参数“N_WBC_FL_W”和“D_Neu_FL_CV”组合作为感染标志参数用于判断对脓毒症的疗效。该双参数组合的物理意义是将第一检测通道WBC粒子内部核酸含量的分布宽度和第二检测通道中性粒细胞内部核酸含量的离散程度进行组合。
该双参数组合通过函数
Y＝0.00688519×N_WBC_FL_W+11.27099282×D_Neu_FL_CV-19.2998686获得感染标志参数，其中，Y表示感染标志参数。
表28
Figure PCTCN2022143965-appb-000072
图31A－图31D直观地显示了使用双参数“N_WBC_FL_W”和“D_Neu_FL_CV”组合作为感染标志参数对脓毒症疗效的检测结果。
实施例10计数值结合参数用于脓毒症诊断
使用深圳迈瑞生物医疗电子股份有限公司生产的BC-6800Plus血液细胞分析仪按照本申请实施例3类似的步骤，对1748例血液样本进行血常规检测，基于散点图采用前述的方法进行脓毒症诊断。其中，脓毒症样本、即阳性样本506例，非脓毒症样本、即阴性样本1242例。
这1748例的纳入标准：存在或疑似急性感染的成年ICU患者。排除标准：妊娠人群、化疗骨髓抑制者、免疫抑制剂治疗者、血液系统疾病患者。
表29示出所使用的感染标志参数及其相应的诊断效力，图33示出对应表29中的感染标志参数的ROC曲线。在表29中：
组合参数1＝-0.61535116*Mon#+0.00766353*N_WBC_FL_W-15.04738706；
组合参数2＝-0.03077968*HGB+0.08933918*N_WBC_FL_W-5.72270269；
组合参数3＝-0.00395999*PLT+0.00606333*N_WBC_FL_W-11.55000862。
表29 不同感染标志参数用于诊断脓毒症的效力
Figure PCTCN2022143965-appb-000073
由表14-6与表29相比，血常规的单核细胞计数值，或血红蛋白值，或血小板计数值，结合WNB通道的参数的组合参数，比PCT或单独DIFF通道，在脓毒症诊断上有更优的诊断性能。说明，血常规的白细胞系和血小板的计数值，以及红细胞的血红蛋白浓度，可以作为第一白细胞参数，与WNB通道参数组合计算感染特征参数，用于脓毒症诊断。
表30，以三个参数为例说明本实施例使用的统计方法和检验方法
Figure PCTCN2022143965-appb-000074
由表30可知，该参数通过Welch检验分析，两组间存在显著统计学差异(p＜0.0001)。
以上在说明书、说明书附图以及权利要求书中提及的特征或者特征组合，只要在本申请的范围内是有意义的并且不会相互矛盾，均可以任意相互组合使用或者单独使用。参考本申请实施例提供的血液细胞分析仪所说明的优点和特征以相应的方式适用于本申请实施例提供的血细胞分析方法和感染标志参数的用途，反之亦然。
以上所述仅为本申请的优选实施例，并非因此限制本申请的专利范围，凡是在本申请的发明构思下，利用本申请说明书及附图内容所作的等效变换方案，或直接/间接运用在其他相关的技术领域均包括在本申请的专利保护范围内。

Claims (45)

  1. 一种血液细胞分析仪，包括：
    吸样装置，用于吸取受试者的待测血液样本；
    制样装置，用于制备含有所述待测血液样本的一部分、溶血剂和用于识别有核红细胞的染色剂的测定试样；
    光学检测装置，包括流动室、光源和光检测器，所述流动室用于供所述测定试样通过，所述光源用于用光照射通过所述流动室的测定试样，所述光检测器用于检测所述测定试样在通过所述流动室时被光照射后所产生的光学信息；
    处理器，被配置为：
    从所述光学信息计算所述测定试样中的至少一个目标粒子团的至少一个白细胞特征参数；
    基于所述至少一个白细胞特征参数获得用于评估所述受试者的感染状态的感染标志参数；以及
    输出所述感染标志参数。
  2. 根据权利要求1所述的血液细胞分析仪，其中，所述处理器被进一步配置为根据所述光学信息识别所述测定试样中的有核红细胞以获得有核红细胞计数。
  3. 根据权利要求1或2所述的血液细胞分析仪，其中，所述至少一个目标粒子团选自白细胞团、中性粒细胞团、淋巴细胞团中的一个或多个；优选所述至少一个目标粒子团包括白细胞团和/或中性粒细胞团。
  4. 根据权利要求1-3任一项所述的血液细胞分析仪，其中，所述处理器从所述光学信息计算所述测定试样中的至少一个目标粒子团的至少一个白细胞特征参数，并基于所述至少一个白细胞特征参数获得用于评估所述受试者的感染状态的感染标志参数，包括：
    所述处理器从所述光学信息获得基于以下白细胞特征参数中的一个或多个并据此获得所述感染标志参数：
    白细胞团的前向散射光强度分布重心、侧向散射光强度分布重心、侧向荧光强度分布重心、前向散射光强度分布宽度、侧向散射光强度分布宽度、侧向荧光强度分布宽度、前向散射光强度分布变异系数、侧向散射光强度分布变异系数、侧向荧光强度分布变异系数；
    白细胞团在基于前向散射光强度、侧向散射光强度和侧向荧光强度中的两种光强度生成的二维散点图中的分布区域的面积、白细胞团在由前向散射光强度、侧向散射光强度和荧光强度生成的三维散点图中的分布区域的体积；
    中性粒细胞团的前向散射光强度分布重心、侧向散射光强度分布重心、侧向荧光强度分布重心、前向散射光强度分布宽度、侧向散射光强度分布宽度、侧向荧光强度分布宽度、前向散射光强度分布变异系数、侧向散射光强度分布变异系数、侧向荧光强度分布变异系数；
    中性粒细胞团在基于前向散射光强度、侧向散射光强度和侧向荧光强度中的两种光强度生成的二维散点图中的分布区域的面积、中性粒细胞团在由前向散射光强度、侧向散射光强度和荧光强度生成的三维散点图中的分布区域的体积；
    淋巴细胞团的前向散射光强度分布重心、侧向散射光强度分布重心、侧向荧光强度分布重心、前向散射光强度分布宽度、侧向散射光强度分布宽度、侧向荧光强度分布宽度、前向散射光强度分布变异系数、侧向散射光强度分布变异系数、侧向荧光强 度分布变异系数；以及
    淋巴细胞团在基于前向散射光强度、侧向散射光强度和侧向荧光强度中的两种光强度生成的二维散点图中的分布区域的面积、淋巴细胞团在由前向散射光强度、侧向散射光强度和荧光强度生成的三维散点图中的分布区域的体积。
  5. 根据权利要求4所述的血液细胞分析仪，其中，所述处理器从所述光学信息计算所述测定试样中的至少一个目标粒子团的至少一个白细胞特征参数，并基于所述至少一个白细胞特征参数获得用于评估所述受试者的感染状态的感染标志参数，包括：
    所述处理器从所述光学信息计算以下白细胞特征参数中的一个或多个并据此获得所述感染标志参数：
    白细胞团的前向散射光强度分布重心、侧向散射光强度分布重心、侧向荧光强度分布重心、前向散射光强度分布宽度、侧向散射光强度分布宽度、侧向荧光强度分布宽度、前向散射光强度分布变异系数、侧向散射光强度分布变异系数、侧向荧光强度分布变异系数，以及白细胞团在基于前向散射光强度、侧向散射光强度和侧向荧光强度中的两种光强度生成的二维散点图中的分布区域的面积、白细胞团在由前向散射光强度、侧向散射光强度和荧光强度生成的三维散点图中的分布区域的体积；
    优选的，所述处理器从所述光学信息计算白细胞团的侧向荧光强度分布重心、前向散射光强度分布宽度以及侧向荧光强度分布宽度中的一个或多个，并据此获得所述感染标志参数。
  6. 根据权利要求1至5任一项所述的血液细胞分析仪，其中，所述处理器被进一步配置为：
    当所述感染标志参数的值处于预设范围之外时，输出指示所述感染标志参数异常的提示信息。
  7. 根据权利要求1至6任一项所述的血液细胞分析仪，其中，所述处理器被进一步配置为，基于所述感染标志参数输出指示所述受试者的感染状态的提示信息。
  8. 根据权利要求1至7任一项所述的血液细胞分析仪，其中，所述感染标志参数用于对所述受试者进行脓毒症的早期预测；
    优选的，所述处理器从所述光学信息获得白细胞团的前向散射光强度分布宽度或白细胞团的侧向荧光强度分布宽度，并将其确定为所述感染标志参数；或者
    优选的，所述处理器从所述光学信息获得白细胞团的侧向荧光强度分布重心和白细胞团的前向散射光强度分布宽度，并据此组合计算获得所述感染标志参数。
  9. 根据权利要求8所述的血液细胞分析仪，其中，所述处理器被进一步配置为：当所述感染标志参数满足第一预设条件时，输出指示所述受试者在在被采集所述待测血液样本之后的一定时间段内可能进展为脓毒症的提示信息；优选的，其中，所述一定时间段不大于48小时、优选在24小时之内。
  10. 根据权利要求1至7任一项所述的血液细胞分析仪，其中，所述感染标志参数用于对所述受试者进行脓毒症的诊断；
    优选的，所述处理器从所述光学信息获得白细胞团的侧向荧光强度分布宽度或中性粒细胞团的侧向荧光强度分布宽度，并将其确定为所述感染标志参数；或者
    优选的，所述处理器从所述光学信息获得白细胞团的侧向荧光强度分布重心和白细胞团的前向散射光强度分布宽度，并据此组合计算获得所述感染标志参数。
  11. 根据权利要求10所述的血液细胞分析仪，其中，所述处理器被进一步配置为：当所述感染标志参数满足第二预设条件时，输出指示所述受试者患有脓毒症的提示信息。
  12. 根据权利要求1至7任一项所述的血液细胞分析仪，其中，所述感染标志参数用于对所述受试者进行普通感染与重症感染的鉴别；
    优选的，所述处理器从所述光学信息获得白细胞团的侧向荧光强度分布宽度或中性粒细胞团在基于侧向散射光强度和侧向荧光强度生成的二维散点图中的分布区域的面积，并将其确定为所述感染标志参数；或者
    优选的，所述处理器从所述光学信息获得白细胞团的侧向荧光强度分布重心和白细胞团的前向散射光强度分布宽度，并据此组合计算所述感染标志参数。
  13. 根据权利要求12的血液细胞分析仪，其中，所述处理器被进一步配置为：当所述感染标志参数满足第三预设条件时，输出指示所述受试者患有重症感染的提示信息。
  14. 根据权利要求1至7任一项所述的血液细胞分析仪，其中，所述受试者为感染患者、任选的是患有重症感染或患有脓毒症的患者，所述感染标志参数用于对所述受试者进行感染病情监控；
    优选的，所述处理器将白细胞团的侧向荧光强度分布宽度确定为所述感染标志参数；或者
    优选的，所述处理器基于白细胞团的侧向荧光强度分布重心和白细胞团的前向散射光强度分布宽度组合计算所述感染标志参数。
  15. 根据权利要求14所述的血液细胞分析仪，其特征在于，所述处理器被进一步配置为根据所述感染标志参数监控所述受试者的感染病情发展；
    优选的，所述处理器被进一步配置为：
    获取通过多次检测、尤其是至少三次检测在不同时间点来自受试者的血液样本而获得的所述感染标志参数的值；并且
    根据通过所述多次的检测而获得的所述感染标志参数的值的变化趋势来判断所述受试者病情是否好转，优选当通过所述多次的检测而获得的所述感染标志参数的值逐渐趋势性减低时，输出指示所述受试者病情好转的提示信息。
  16. 根据权利要求1至7任一项所述的血液细胞分析仪，其中，所述受试者为接受了治疗的脓毒症患者，所述感染标志参数对所述受试者进行脓毒症预后分析，
    优选地，所述处理器被进一步配置为：当所述感染标志参数满足第四预设条件时，输出指示所述受试者脓毒症预后良好的提示信息。
  17. 根据权利要求1至7任一项所述的血液细胞分析仪，其中，所述感染标志参数对所述受试者进行细菌感染和病毒感染的鉴别，
    优选地，所述处理器被进一步配置为基于所述感染标志参数判断所述受试者是细菌感染还是病毒感染。
  18. 根据权利要求1至7任一项所述的血液细胞分析仪，其中，所述感染标志参数对所述受试者进行非感染性炎症和感染性炎症的鉴别，
    优选地，所述处理器被进一步配置为：当所述感染标志参数满足第五预设条件时，输出指示所述受试者患感染性炎症的提示信息。
  19. 根据权利要求1至7任一项所述的血液细胞分析仪，其中，所述受试者为正在接受用药治疗的脓毒症患者，所述感染标志参数用于对所述受试者进行脓毒症的疗效评估。
  20. 根据权利要求1至19中任一项所述的血液细胞分析仪，其特征在于，所述处理器被进一步配置为，在从所述光学信息获得所述测定试样中的至少一个目标粒子团的至少一个白细胞特征参数之前，基于所述光学信息获得所述测定试样的白细胞计数，并且当所述白细胞计数小于预设阈值时输出对所述受试者的血液样本进行重新测定的重测指令，其中，基于所述重测指令的测定的样本测定量大于用于获取所述光学信息的测定的样本测定量；以及
    所述处理器被进一步配置为，从基于所述重测指令测得的光学信息获得至少另一个目标粒子团的至少另一个白细胞特征参数，并且基于所述至少另一个白细胞特征参数获得用于评估所述受试者的感染状态的感染标志参数。
  21. 根据权利要求1至19中任一项所述的血液细胞分析仪，其特征在于，所述处理器被进一步配置为：
    当所述目标粒子团的预设特征参数满足第六预设条件时，不输出所述感染标志参数的值，或者输出所述感染标志参数的值并且同时输出指示该感染标志参数的值不可靠的提示信息。
  22. 根据权利要求21所述的血液细胞分析仪，其中，所述处理器被进一步配置为：
    当所述目标粒子团的粒子总数小于预设阈值时，和/或当所述目标粒子团与其他粒子团存在交叠时，不输出所述感染标志参数的值，或者输出所述感染标志参数的值并且同时输出指示该感染标志参数的值不可靠的提示信息。
  23. 根据权利要求1至22中任一项所述的血液细胞分析仪，其中，所述处理器被进一步配置为：
    当所述受试者患有血液疾病或者所述待测血液样本中存在异常细胞、尤其是原始细胞时，例如当根据所述光学信息判断所述待测血液样本中存在异常细胞、尤其是原始细胞时，不输出所述感染标志参数的值，或者输出所述感染标志参数的值并且同时输出指示该感染标志参数的值不可靠的提示信息。
  24. 根据权利要求1至19中任一项所述的血液细胞分析仪，其中，所述处理器从所述光学信息计算所述测定试样中的至少一个目标粒子团的至少一个白细胞特征参数，并基于所述至少一个白细胞特征参数获得用于评估所述受试者的感染状态的感染标志参数，包括：所述处理器
    从所述光学信息计算所述测定试样中的至少一个目标粒子团的多个参数；
    从所述多个参数中获取用于评估所述受试者的感染状态的多个感染标志参数组；
    为所述多个感染标志参数组中的每个感染标志参数组配置优先级；
    计算所述多个感染标志参数组中的每个感染标志参数组的可信度，根据所述多个感染标志参数组的优先级和可信度从所述多个感染标志参数组中选择至少一个感染标志参数组，用于获取所述感染标志参数；或者按照所述多个感染标志参数组的优先级，依次计算所述多个感染标志参数组的可信度并判断该可信度是否达到相应的可信度阈 值，当当前感染标志参数组的可信度达到相应的可信度阈值时，基于该感染标志参数组获取所述感染标志参数并且停止计算和判断；
    优选的，所述处理器被进一步配置为：
    计算所述多个感染标志参数组中的每个感染标志参数组的可信度，并且判断每个感染标志参数组的可信度是否达到相应的可信度阈值；
    将所述多个感染标志参数组中可信度达到相应的可信度阈值的感染标志参数组作为候选感染标志参数组；
    根据所述候选感染标志参数组的优先级从所述候选感染标志参数组中选择至少一个候选感染标志参数组、优选选择优先级最高的感染标志参数组，用于获取所述感染标志参数。
  25. 根据权利要求1至19中任一项所述的血液细胞分析仪，其中，所述处理器从所述光学信息计算所述测定试样中的至少一个目标粒子团的至少一个白细胞特征参数，并基于所述至少一个白细胞特征参数获得用于评估所述受试者的感染状态的感染标志参数，包括：所述处理器
    从所述光学信息计算所述测定试样中的至少一个目标粒子团的多个参数，
    从所述多个参数中获取用于评估所述受试者的感染状态的多个感染标志参数组，
    计算所述多个感染标志参数组中的每个感染标志参数组的可信度，根据所述多个感染标志参数组的可信度从所述多个感染标志参数组中选择至少一个感染标志参数组，用于获取所述感染标志参数。
  26. 根据权利要求1至19中任一项所述的血液细胞分析仪，其中，所述处理器从所述光学信息获得所述测定试样中的至少一个目标粒子团的至少一个白细胞特征参数，并基于所述至少一个白细胞特征参数获得用于评估所述受试者的感染状态的感染标志参数，包括：所述处理器
    根据所述光学信息判断所述待测血液样本是否存在影响感染状态评估的异常；
    当判断所述待测血液样本存在影响感染状态评估的异常时，从所述光学信息获取与所述异常匹配的至少一个目标粒子团的至少一个白细胞特征参数，用于获得所述感染标志参数。
  27. 根据权利要求1至26中任一项所述的血液细胞分析仪，其中，所述处理器被进一步配置为如此选择所述至少一个白细胞特征参数并基于所述至少一个白细胞特征参数获得所述感染标志参数，使得所述感染标志参数的诊断效力大于0.5，优选大于0.6，特别优选大于0.8。
  28. 一种血液细胞分析仪，包括：
    测定装置，用于将受试者的待测血液样本、溶血剂和染色剂混合以制备测定试样并且对该测定试样进行光学测定，以获取所述测定试样的光学信息；以及
    控制器，被配置为：
    接收模式设定指令，
    当模式设定指令表明选择了血常规检测模式时，控制所述测定装置对第一测定量的测定试样进行光学测定，以获取所述测定试样的光学信息，以及基于该光学信息获取并输出所述测定试样的血常规参数，
    当模式设定指令表明选择了脓毒症检测模式时，控制所述测定装置对大于第一测定量的第二测定量的测定试样进行光学测定，以获取所述测定试样的光学信息，从所 述光学信息获得所述测定试样中的至少一个目标粒子团的至少一个白细胞特征参数，基于所述至少一个白细胞特征参数获得用于评估所述受试者的感染状态的感染标志参数，以及输出所述感染标志参数。
  29. 一种用于提示受试者的感染状态的方法，所述方法包括以下步骤：
    获取所述受试者的待测血液样本；
    制备含有所述待测血液样本的一部分、溶血剂和用于识别有核红细胞的染色剂的测定试样；
    使所述测定试样中的粒子逐个通过被光照射的光学检测区，以获得所述测定试样中的粒子在被光照射后所产生光学信息；
    从所述光学信息计算所述测定试样中的至少一个目标粒子团的至少一个白细胞特征参数；
    基于所述至少一个白细胞特征参数获得感染标志参数；并且
    根据所述感染标志参数提示所述受试者的感染状态。
  30. 根据权利要求29所述的方法，其中，所述至少一个目标粒子团选自白细胞团、中性粒细胞团、淋巴细胞团中的一个或多个；优选所述至少一个目标粒子团包括白细胞团和/或中性粒细胞团。
  31. 根据权利要求29所述的方法，其中，从所述光学信息计算获得所述测定试样中的至少一个目标粒子团的至少一个白细胞特征参数，包括：
    计算目标粒子团在散射光或荧光信号强度上的分布重心；
    计算目标粒子团在散射光或荧光信号强度上的分布宽度；
    计算目标粒子团在散射光或荧光信号强度上的分布变异系数
    计算目标粒子团在散射光或荧光信号脉冲宽度的平均值；
    计算目标粒子团的两种光强度生成的二维散点图中的分布区域的面积；
    计算目标粒子团的三种光强度生成的三维散点图中的分布区域的体积；或者
    计算目标粒子团在散射光或荧光信号强度分布的边界值。
  32. 根据权利要求29至31中任一项所述的方法，其中，所述感染标志参数选自以下细胞特征参数中的一个或者由以下细胞特征参数中的多个细胞特征参数组合得到、尤其是通过线性函数组合得到：
    白细胞团的前向散射光强度分布重心、侧向散射光强度分布重心、侧向荧光强度分布重心、前向散射光强度分布宽度、侧向散射光强度分布宽度、侧向荧光强度分布宽度、前向散射光强度分布变异系数、侧向散射光强度分布变异系数、侧向荧光强度分布变异系数、侧向荧光信号的脉冲宽度的平均值、前向散射光信号的脉冲宽度的平均值、侧向散射光信号的脉冲宽度的平均值、侧向荧光强度分布右边界值；
    白细胞团在基于前向散射光强度、侧向散射光强度和侧向荧光强度中的两种光强度生成的二维散点图中的分布区域的面积、白细胞团在由前向散射光强度、侧向散射光强度和荧光强度生成的三维散点图中的分布区域的体积；
    中性粒细胞团的前向散射光强度分布重心、侧向散射光强度分布重心、侧向荧光强度分布重心、前向散射光强度分布宽度、侧向散射光强度分布宽度、侧向荧光强度分布宽度、前向散射光强度分布变异系数、侧向散射光强度分布变异系数、侧向荧光强度分布变异系数；
    中性粒细胞团在基于前向散射光强度、侧向散射光强度和侧向荧光强度中的两种光强度生成的二维散点图中的分布区域的面积、中性粒细胞团在由前向散射光强度、 侧向散射光强度和荧光强度生成的三维散点图中的分布区域的体积；
    淋巴细胞团的前向散射光强度分布重心、侧向散射光强度分布重心、侧向荧光强度分布重心、前向散射光强度分布宽度、侧向散射光强度分布宽度、侧向荧光强度分布宽度、前向散射光强度分布变异系数、侧向散射光强度分布变异系数、侧向荧光强度分布变异系数；以及
    淋巴细胞团在基于前向散射光强度、侧向散射光强度和侧向荧光强度中的两种光强度生成的二维散点图中的分布区域的面积、淋巴细胞团在由前向散射光强度、侧向散射光强度和荧光强度生成的三维散点图中的分布区域的体积。
  33. 根据权利要求29所述的方法，其中，从所述光学信息计算所述测定试样中的至少一个目标粒子团的至少一个白细胞特征参数，并基于所述至少一个白细胞特征参数计算感染标志参数，包括：
    从所述光学信息获得以下白细胞特征参数中的一个或多个并据此计算所述感染标志参数：
    白细胞团的前向散射光强度分布重心、侧向散射光强度分布重心、侧向荧光强度分布重心、前向散射光强度分布宽度、侧向散射光强度分布宽度、侧向荧光强度分布宽度、前向散射光强度分布变异系数、侧向散射光强度分布变异系数、侧向荧光强度分布变异系数，以及白细胞团在基于前向散射光强度、侧向散射光强度和侧向荧光强度中的两种光强度生成的二维散点图中的分布区域的面积、白细胞团在由前向散射光强度、侧向散射光强度和荧光强度生成的三维散点图中的分布区域的体积。
  34. 根据权利要求29所述的方法，其中，从所述光学信息计算所述测定试样中的至少一个目标粒子团的至少一个白细胞特征参数，并基于所述至少一个白细胞特征参数计算感染标志参数，包括：
    从所述光学信息获得白细胞团的侧向荧光强度分布重心、前向散射光强度分布宽度以及侧向荧光强度分布宽度中的一个或多个，并据此计算所述感染标志参数。
  35. 根据权利要求29至34中任一项所述的方法，其中，根据所述感染标志参数提示所述受试者的感染状态包括：基于所述感染标志参数对所述受试者进行脓毒症的早期预测、脓毒症的诊断、普通感染与重症感染的鉴别感染病情监控、脓毒症的预后分析、细菌感染和病毒感染的鉴别、脓毒症的疗效评估、或非感染性炎症和感染性炎症的鉴别。
  36. 根据权利要求35所述的方法，其中，根据所述感染标志参数评估所述受试者的感染状态包括：
    当所述感染标志参数满足第一预设条件时，输出指示所述受试者在采血检测后的一定时间段内可能进展为脓毒症的提示信息；优选地，所述一定时间段不大于48小时、优选在24小时之内；
    优选的，从所述光学信息获得白细胞团的侧向荧光强度分布宽度或中性粒细胞团的侧向荧光强度分布宽度，并将其确定为所述感染标志参数；或者
    优选的，从所述光学信息获得白细胞团的侧向荧光强度分布重心和白细胞团的前向散射光强度分布宽度，并据此组合计算所述感染标志参数。
  37. 根据权利要求35所述的方法，其中，根据所述感染标志参数提示所述受试者的感染状态包括：
    当所述感染标志参数满足第二预设条件时，输出指示所述受试者患有脓毒症的提 示信息
    优选的，从所述光学信息获得白细胞团的侧向荧光强度分布宽度或中性粒细胞团的侧向荧光强度分布宽度，并将其确定为所述感染标志参数；或者
    优选地，从所述光学信息获得白细胞团的侧向荧光强度分布重心和白细胞团的前向散射光强度分布宽度，并据此组合计算所述感染标志参数。
  38. 根据权利要求35所述的方法，其中，根据所述感染标志参数提示所述受试者的感染状态包括：
    当所述感染标志参数满足第三预设条件时，输出指示所述受试者患有重症感染的提示信息；
    优选的，从所述光学信息获得白细胞团的侧向荧光强度分布宽度或中性粒细胞团在基于侧向散射光强度和侧向荧光强度生成的二维散点图中的分布区域的面积，并将其确定为所述感染标志参数；或者
    优选的，从所述光学信息获得白细胞团的侧向荧光强度分布重心和白细胞团的前向散射光强度分布宽度，并据此组合计算所述感染标志参数。
  39. 根据权利要求35所述的方法，其特征在于，所述受试者为感染患者、尤其是患有重症感染或患有脓毒症的患者；并且
    根据所述感染标志参数提示所述受试者的感染状态包括：根据所述感染标志参数监控所述受试者的感染病情发展。
  40. 根据权利要求39所述的方法，其特征在于，根据所述感染标志参数监控所述受试者的感染病情发展包括：
    获取通过多次检测在不同时间点来自受试者的血液样本而获得的所述感染标志参数的值；
    根据通过所述多次的检测而获得的所述感染标志参数的值的变化趋势来判断所述受试者病情是否好转，优选当通过所述多次的检测而获得的所述感染标志参数的值逐渐趋势性减低时，输出指示所述受试者病情好转的提示信息。
  41. 根据权利要求39或40任一项所述的方法，其中，从所述光学信息计算所述测定试样中的至少一个目标粒子团的至少一个白细胞特征参数，并基于所述至少一个白细胞特征参数获得感染标志参数，包括：
    从所述光学信息获得白细胞团的侧向荧光强度分布宽度，并将其确定为所述感染标志参数；或者
    从所述光学信息获得白细胞团的侧向荧光强度分布重心和白细胞团的前向散射光强度分布宽度，并据此组合计算所述感染标志参数。
  42. 根据权利要求35所述的方法，其中，根据所述感染标志参数提示所述受试者的感染状态包括：
    当所述感染标志参数满足第四预设条件时，输出指示所述受试者脓毒症预后良好的提示信息。
  43. 根据权利要求35所述的方法，其中，根据所述感染标志参数提示所述受试者的感染状态并输出指示所述受试者的感染状态的提示信息，包括：
    基于所述感染标志参数判断所述受试者是细菌感染还是病毒感染。
  44. 根据权利要求35的方法，其中，根据所述感染标志参数提示所述受试者的感染状态并输出指示所述受试者的感染状态的提示信息，包括：
    当所述感染标志参数满足第五预设条件时，输出指示所述受试者患感染性炎症的提示信息。
  45. 根据权利要求35的方法，其中，根据所述感染标志参数提示所述受试者的感染状态并输出指示所述受试者的感染状态的提示信息，包括：
    所述受试者为正在接受用药治疗的脓毒症患者，基于所述感染标志参数对所述受试者进行脓毒症的疗效评估。
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