WO2023125940A1 - Hematology analyzer, method, and use of infection marker parameter - Google Patents

Abstract

A hematology analyzer (100), a method, and the use of an infection marker parameter. The hematology analyzer (100) comprises a sample suction apparatus (110) used for sucking a blood sample to be tested of a subject, a sample preparation apparatus (120) used for preparing a measurement specimen, an optical testing apparatus (130) used for testing the measurement specimen to obtain optical information, and a processor (140). The processor (140) is configured to: obtain from the optical information at least one leukocyte parameter of at least one leukocyte granule mass in the measurement specimen; on the basis of the at least one leukocyte parameter, obtain an infection marker parameter; and output the infection marker parameter, the infection marker parameter being used for determining whether the subject suffers from a severe infection. The accurate and effective infection marker parameter is quickly provided for diagnosing a severe infection.

Description

Hematology analyzer, method and use of infection marker parameters technical field

The present application relates to the field of in vitro diagnosis, in particular to a blood cell analyzer, a method for identifying whether a subject has a severe infection, and the use of infection marker parameters in identifying whether a subject has a severe infection.

Background technique

Infectious diseases are clinically common diseases, among which sepsis (Sepsis) is a serious infectious disease. The incidence of sepsis is high. There are more than 18 million cases of severe sepsis every year in the world, and the condition of sepsis is dangerous and the mortality rate is high. About 14,000 people worldwide die of its complications every day. According to foreign epidemiological surveys, the fatality rate of sepsis has surpassed that of myocardial infarction, and it has become the main cause of death of non-heart disease patients in the intensive care unit. In recent years, despite advances in anti-infection therapy and organ function support technology, the mortality rate of sepsis is still as high as 30% to 70%. The cost of sepsis treatment is high, and the consumption of medical resources is large, which seriously affects the quality of life of human beings and poses a huge threat to human health.

For this reason, clinicians need to diagnose whether patients are infected in a timely manner and search for pathogens in order to formulate effective treatment plans. Therefore, how to quickly and early screen and diagnose infectious diseases has become an urgent problem for clinical laboratories.

For the rapid differential diagnosis of infectious diseases, the existing solutions and their shortcomings in the industry are as follows:

1. Microbial culture: Microbial culture is considered the most reliable gold standard, which can directly culture and detect bacteria in clinical specimens such as body fluids or blood, thereby interpreting the type and drug resistance of bacteria, which can directly guide clinical medication. However, the microbial culture method has a long reporting period, the samples are easily contaminated, and the false negative rate is high, which cannot well meet the requirements of rapid and accurate clinical results.

2. Detection of inflammatory markers such as C-reactive protein (CRP), procalcitonin (PCT) and serum amyloid A (SAA): due to inflammatory factors such as CRP, PCT and SAA It has good sensitivity and is widely used in the auxiliary diagnosis of infectious diseases. However, the detection specificity of these inflammatory markers is weak, and additional examination fees are required, which increases the financial burden on patients. In addition, CRP and PCT are interfered by specific diseases and cannot correctly reflect the patient's infection status. For example, CRP is produced in the liver, and infected patients with liver damage have normal CRP levels, leading to false negatives.

3. Serum antigen and antibody detection: 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. Additional inspection fees need to be charged, which increases the financial burden of patients.

4. Routine blood test: Routine blood test can prompt the occurrence of infection and identify the type of infection to a certain extent. However, the blood routine WBC\Neu% currently used in clinical practice is affected by many aspects, such as being easily affected by other non-infectious inflammatory reactions and normal physiological fluctuations of the body, and cannot accurately and timely reflect the patient's condition. bad.

Contents of the invention

In order to at least partly solve the above technical problems, the task of the present application is to provide a blood cell analyzer, a method for identifying whether a subject has a severe infection, and infection marker parameters in identifying whether a subject has a severe infection The use of the invention, which can obtain the infection marker parameters with high diagnostic efficacy from the original signal of the blood routine detection process, so that it can accurately and quickly judge whether the subject has a severe infection based on the infection marker parameters.

In order to achieve the above tasks of the present application, 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 leukocyte classification;

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:

calculating at least one leukocyte parameter of at least one leukocyte particle cluster in said assay sample from said optical information,

obtaining an infection marker parameter based on said at least one white blood cell parameter, and

The infection flag parameter is output, and the infection flag parameter is used to judge whether the subject suffers from severe infection.

In order to achieve the above tasks of the present application, the second aspect of the present application also provides a method for identifying whether a subject has a severe infection, including:

Obtaining a blood sample to be tested from the subject;

preparing a measurement sample containing a part of the blood sample to be tested, a hemolyzing agent, and a staining agent for leukocyte classification;

making the particles in the measurement sample pass through the optical detection zone irradiated by light one by one, so as to obtain the optical information generated by the particles in the measurement sample after being irradiated by light;

calculating at least one leukocyte parameter of at least one leukocyte particle cluster in said assay sample from said optical information;

obtaining an infection marker parameter based on said at least one leukocyte parameter; and

Whether the subject suffers from severe infection is judged according to the infection marker parameters.

In order to achieve the above tasks of the present application, the third aspect of the present application also provides the use of infection marker parameters in identifying whether a subject has severe infection, wherein the infection marker parameters are obtained by the following method:

obtaining at least one leukocyte parameter of at least one leukocyte particle cluster detected by flow cytometry on an assay sample comprising a portion of a test blood sample from a subject, a hemolyzing agent, and a stain for leukocyte classification; and

An infection marker parameter is obtained based on the at least one white blood cell parameter.

In the technical solutions provided in various aspects of the present application, the infection marker parameter is calculated based on at least one leukocyte parameter obtained from the detection channel used for leukocyte classification, and based on the infection marker parameter, it can be effectively judged whether the subject has a severe infection, Therefore, it is possible to quickly, accurately and efficiently assist doctors in judging whether a subject has a severe infection.

Description of drawings

The present application will be explained more clearly below in conjunction with the embodiments and the accompanying drawings. Through the detailed description of the embodiments of the present application, the above advantages and other advantages will become apparent to those skilled in the art. The drawings are only for illustrating preferred embodiments and are not to be considered as limiting the application. Throughout the drawings, the same or similar reference numerals denote the same components. In the attached picture:

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 scatter diagram of SS-FL 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 SS-FS-FL of a measurement sample according to some embodiments of the present application.

Fig. 6 shows the determination of cell characteristic parameters of neutrophil populations in a sample according to some embodiments of the present application.

Fig. 7 is a scatter diagram of abnormalities in the measurement samples according to some embodiments of the present application.

Fig. 8 is a schematic flowchart of a method for identifying whether a subject has a severe infection according to some embodiments of the present application.

9-10 are ROC curves of infection marker parameters used to identify whether a subject has severe infection or not according to some embodiments of the present application.

Fig. 11 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.

Detailed ways

The embodiments of the present application will be clearly and completely described below in conjunction with the accompanying drawings. Apparently, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in this application, all other embodiments obtained by persons of ordinary skill in the art without creative efforts fall within the protection scope of this application.

It should be noted that the term "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.

Those skilled in the art can understand that, unless otherwise defined, all terms (including technical terms and scientific terms) used herein have the same meanings as commonly understood by those of ordinary skill in the art to which this application belongs.

1) Scatter diagram: It 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-coordinate axis, Y-coordinate axis and The Z coordinate axis represents a characteristic of each particle. For example, in a scatter diagram, the X coordinate axis represents the forward scattered light intensity, the Y coordinate axis represents the fluorescence intensity, and the Z axis represents the side scattered light intensity. The term "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.

2) Particle group/cell group: distributed in a certain area of the scatter diagram, a particle group formed by multiple particles with the same cell characteristics, such as white blood cell (including all types of white blood cell) groups, and white blood cell subpopulations, such as medium granulocytes, lymphocytes, monocytes, eosinophils, or basophils.

3) Blood shadow: fragment particles obtained by dissolving red blood cells and platelets in the blood with a hemolytic agent.

4) ROC curve (receiver operator characteristic curve): Receiver operating characteristic curve, which is based on a series of different binary classification methods (demarcation threshold), with the true positive rate on the vertical axis and the false positive rate on the horizontal axis. ROC_AUC (area under the curve) represents the area enclosed by the ROC curve and the horizontal axis.

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.

Since 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 closer the ROC curve is to the upper left corner, the higher the sensitivity of the test, the lower the false positive rate, and the better the performance of the diagnostic method. It can be seen that 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 (diagnostic threshold).

Currently, blood cell analyzers generally count and classify white blood cells through DIFF channels and/or WNB channels. Among them, 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 identifies nucleated red blood cells through the WNB channel, and can simultaneously obtain the number of nucleated red blood cells, white blood cells and basophils. Combining the DIFF channel with the WNB channel can result in five classifications of white blood cells, including lymphocytes (Lym), monocytes (Mon), neutrophils (Neu), eosinophils (Eos), basophils cells (Baso) five types of white blood cells.

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. Here, 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. When the laser beam irradiates the particles, the characteristics of the particles themselves (such as volume, Staining degree, cell content size and content, cell nucleus density, etc.) can block or change the direction of the laser beam, thereby generating scattered light at various angles corresponding to its characteristics, and the scattered light can be obtained after the signal detector receives the particle structure and information about the composition. Among them, 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), and 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 not-shown fluid circuit system, which is used to communicate with the sample suction device 110, the sample preparation device 120 and the optical detection device 130, so as to carry out liquid delivery among these devices.

The sample aspirating device 110 is used to aspirate the subject's blood sample to be tested.

In some embodiments, the sample aspirating device 110 has a sampling needle (not shown) for aspirating a blood sample to be tested. In addition, for example, 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 at least to prepare a measurement sample containing a part of a blood sample to be measured, a hemolyzing agent, and a staining agent for leukocyte classification.

In the embodiment of the present application, 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.

In some embodiments, the hemolytic agent may be any one or a combination of cationic surfactants, nonionic surfactants, anionic surfactants, and amphiphilic surfactants. In other embodiments, the hemolytic agent may include at least one of alkyl glycosides, triterpene saponins, and steroidal saponins.

In the embodiment of the present application, the staining agent is a fluorescent dye used to classify white blood cells, for example, it can be a fluorescent dye that can classify white blood cells in a blood sample into at least three white blood cell subgroups (monocytes, lymphocytes, and neutrophils). ) fluorescent dyes.

In some embodiments, the staining agent may include membrane-specific dyes or mitochondria-specific dyes, more details of which can be referred to PCT patent application WO2019/206300A1 filed by the applicant on April 26, 2019, the entire disclosure of which is incorporated by reference merged here.

In other embodiments, the dyeing agent may include a cationic cyanine compound. For more details, please refer to the Chinese patent application CN101750274A submitted by the applicant on September 28, 2019, the entire disclosure of which is incorporated herein by reference.

In some embodiments, 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.

For example, the at least one reaction cell may include a first reaction cell and a second reaction cell, and 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.

The optical detection device 130 includes a flow chamber for allowing the measurement sample to pass through, a light source for irradiating the measurement sample passing through the flow chamber with light, and a light detector for detecting The detector is used to detect the optical information generated by the measurement sample when it is irradiated with light when it passes through the flow cell.

For example, 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, and 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.

It can be understood here that the first detection channel (also referred to as DIFF channel) used for leukocyte classification refers to the detection of the first measurement sample prepared by the sample preparation device 120 by the optical detection device 130, and is used to identify the The second detection channel of red blood cells (also referred to as 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 .

Herein, a flow cell refers to a chamber of focused liquid flow suitable for detection of light scattering and fluorescence signals. When a particle, such as a blood cell, passes through the detection aperture of the flow cell, the particle scatters in all directions an incident light beam from the light source directed to the detection aperture. Light detectors may 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. In particular, 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. In other embodiments, 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. In some embodiments, the side light scatter signal may be detected from an angle of about 65° to about 115° from the incident light beam. Typically, the fluorescent signal from blood cells stained with a fluorochrome is also typically detected at about 90° to the incident beam.

In some embodiments, the photodetector may include a forward scattered light detector for detecting forward scattered light signal (or forward scattered light intensity), a side scattered light signal for detecting side scattered light signal (or side scattered light intensity ) side scatter light detector and a fluorescence detector for detecting fluorescence signal (or fluorescence intensity). Accordingly, 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. On one side of the flow chamber 103, 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 . In the embodiment of the present application, the processor 140 is configured to implement the method steps described in detail below.

In the application embodiment, 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), a digital A device such as a signal processor (DSP) used to interpret computer instructions and process data in computer software. For example, 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.

In addition, 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 , and 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.

As mentioned in the background technology, 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.

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.

Based on this background, the inventors have found that at least one white blood cell parameter obtained from the DIFF channel, especially the white blood cell characteristic parameter, can be used to achieve high-efficiency severe infection diagnosis . For example, neutrophils and monocytes are the body's first barrier against infection, and are valuable in reflecting the degree of infection; the inventors have found through research that the characteristic parameters of neutrophils can be used for the diagnosis of severe infections, and further , the characteristic parameters of neutrophils combined with the characteristic parameters of monocytes can realize a more efficient diagnosis of severe infection.

The white blood cell parameters of the DIFF channel, especially the white blood cell characteristic parameters, can realize high-efficiency severe infection diagnosis through linear discriminant analysis (linear discriminant analysis, LDA), for example. 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 types of events (for example, whether a donor will or will not be present within a certain period of time in the future). will progress to sepsis, with or without sepsis, bacterial or viral infection, common or severe infection, infectious or non-infectious inflammation, effective or ineffective treatment of sepsis) A linear combination of features, a multi-dimensional data is linearly combined to obtain one-dimensional data, so that the two types of events can be characterized or distinguished. 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.

Therefore, the embodiment of the present application first proposes a blood cell analyzer, including:

A sample aspirating device 110, used to aspirate a blood sample to be tested from a subject (such as an infected patient or a suspected infected patient);

A sample preparation device 120 for preparing a measurement sample containing a part of the blood sample to be tested, a hemolyzing agent and a staining agent for leukocyte classification;

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 a device for detecting optical information generated by the measurement sample when it is irradiated with light while passing through the flow chamber; and

Processor 140 is configured to:

calculating at least one leukocyte parameter of at least one leukocyte particle cluster in said assay sample from said optical information,

obtaining an infection marker parameter based on said at least one white blood cell parameter, and

The infection flag parameter is output, and the infection flag parameter is used to judge whether the subject suffers from severe infection.

Preferably, the at least one white blood cell parameter includes a cell characteristic parameter, that is, the at least one white blood cell parameter includes a cell characteristic parameter of the at least one white blood cell particle cluster. In this way, infection marker parameters with further improved diagnostic efficacy can be provided.

It should be understood here that the cell characteristic parameters of the leukocyte particle cluster do not include the cell count or classification parameters of the leukocyte particle cluster, but include cell characteristics such as the volume, internal granularity, and internal nucleic acid content of the cells in the leukocyte particle cluster. The characteristic parameters of .

In some embodiments, the cell characteristic parameters of the white blood cell cluster can be obtained by analyzing all particle information of the white blood cell cluster, or can be obtained by analyzing part of the particle information of the white blood cell cluster. For example, the cell characteristic parameters of the leukocyte particle cluster can be obtained by distinguishing the part of the sample to be tested that does not overlap with the part of the leukocyte particle cluster in the normal human blood sample that may carry infection characteristic information.

Further, in some embodiments, the leukocytes in the measurement sample can be classified into at least monocyte population, neutrophil population, and lymphocyte population based on the optical information, and in particular can be classified into monocyte population, neutrophil population, and neutrophil population. Granulocyte population, lymphocyte population and eosinophil population.

In a specific example, as shown in Figures 3 to 5, based on the forward scattered light signal (or forward scattered light intensity) FS, side scattered light signal (or side scattered light intensity) SS and The fluorescence signal (or fluorescence intensity) FL can classify the leukocytes in the measurement sample into monocyte population Mon, neutrophil population Neu, lymphocyte population Lym, and eosinophil population Eos. Among them, Fig. 3 is a two-dimensional scatter diagram generated based on the side scattered light signal SS and the fluorescence signal FL in the optical information, and Fig. 4 is a two-dimensional scatter diagram generated based on the forward scattered light signal FS and the side scattered light signal SS in the optical information Figure 5 is a three-dimensional scattergram generated based on the forward scattered light signal FS, side scattered light signal SS and fluorescence signal FL in the optical information.

Correspondingly, in some embodiments, the at least one leukocyte particle cluster may comprise at least one of monocyte population Mon, neutrophil population Neu, lymphocyte population Lym and eosinophil population Eos in the assay sample. A cell population, that is, the at least one white blood cell parameter may include determining one or more of the cell characteristic parameters of the monocyte population Mon, the neutrophil population Neu, the lymphocyte population Lym and the eosinophil population Eos in the sample. multiple parameters.

Preferably, the at least one white blood cell parameter includes one or more of the cell characteristic parameters of monocyte population Mon, neutrophil population Neu and lymphocyte population Lym in the measurement sample.

More preferably, the at least one white blood cell particle cluster may include at least one cell population in the monocyte population Mon and the neutrophil population Neu in the assay sample, that is, the at least one white blood cell parameter may include the assay sample One or more parameters, such as one or two or more than two parameters, among the cell characteristic parameters of the monocyte population Mon and the neutrophil population Neu.

In some other embodiments, the at least one white blood cell particle cluster may also include a white blood cell population (including all types of white blood cells) Wbc, that is, the at least one white blood cell parameter may include measuring the cell characteristic parameters of the white blood cell population Wbc in the sample.

In some embodiments, the at least one white blood cell parameter includes one or more of the following parameters: the width of the forward scattered light intensity distribution of the white blood cell particle cluster, the center of gravity of the forward scattered light intensity distribution, the forward scattered light intensity distribution Coefficient of variation, width of side scattered light intensity distribution, center of gravity of side scattered light intensity distribution, coefficient of variation of side scattered light intensity distribution, width of fluorescence intensity distribution, center of gravity of fluorescence intensity distribution, coefficient of variation of fluorescence intensity distribution, and the white blood cell particle group in The area of the distribution area in the two-dimensional scatter diagram generated by two kinds of light intensities in forward scattered light intensity, side scattered light intensity and fluorescence intensity The volume of the distribution area in a 3D scatterplot generated by scattered light intensity and fluorescence intensity.

In some specific examples, the at least one white blood cell parameter may include one or more of the following parameters, such as one or two parameters: the distribution width of the forward scattered light intensity of the monocyte population in the measurement sample D_MON_FS_W, center of gravity of forward scattered light intensity distribution D_MON_FS_P, coefficient of variation of forward scattered light intensity distribution D_MON_FS_CV, width of side scattered light intensity distribution D_MON_SS_W, center of gravity of side scattered light intensity distribution D_MON_SS_P, coefficient of variation of side scattered light intensity distribution D_MON_SS_CV, fluorescence Intensity distribution width D_MON_FL_W, fluorescence intensity distribution center of gravity D_MON_FL_P, fluorescence intensity distribution coefficient of variation D_MON_FL_CV and the two-dimensional light intensity generated by the monocyte population in forward scattered light intensity, side scattered light intensity and fluorescence intensity The area of the distribution area in the scatter diagram D_MON_FLFS_Area (the area of the distribution area of the monocyte population in the two-dimensional scatter diagram generated by the forward scattered light intensity and the fluorescence intensity), D_MON_FLSS_Area (the area of the monocyte population in the two-dimensional scatter diagram generated by the The area of the distribution area in the two-dimensional scatter diagram generated by the scattered light intensity and the fluorescence intensity), D_MON_SSFS_Area (the distribution area of the monocyte population in the two-dimensional scatter diagram generated by the forward scattered light intensity and the side scattered light intensity area) and the volume of the distribution area of the monocyte population in the three-dimensional scatter plot generated by the forward scattered light intensity, side scattered intensity and fluorescence intensity; the preceding measurement of the neutrophil population in the sample Towardscattered light intensity distribution width D_NEU_FS_W, forward scattered light intensity distribution center of gravity D_NEU_FS_P, forward scattered light intensity distribution coefficient of variation D_NEU_FS_CV, side scattered light intensity distribution width D_NEU_SS_W, side scattered light intensity distribution center of gravity D_NEU_SS_P, side scattered light intensity Distribution coefficient of variation D_NEU_SS_CV, fluorescence intensity distribution width D_NEU_FL_W, fluorescence intensity distribution center of gravity D_NEU_FL_P, fluorescence intensity distribution coefficient of variation D_NEU_FL_CV and the neutrophil population in the forward scattered light intensity, side scattered light intensity and fluorescence intensity The area D_NEU_FLFS_Area of the distribution area in the two-dimensional scatter diagram generated by the light intensity (the area of the distribution area of the neutrophil population in the two-dimensional scatter diagram generated by the forward scattered light intensity and fluorescence intensity), D_NEU_FLSS_Area ( The area of the distribution area of the neutrophil population in the two-dimensional scatter diagram generated by the side scattered light intensity and the fluorescence intensity), D_NEU_SSFS_Area (the neutrophil population is generated by the forward scattered light intensity and the side scattered light intensity The area of the distribution area in the two-dimensional scatter diagram) and the volume of the distribution area of the neutrophil population in the three-dimensional scatter diagram generated by the forward scattered light intensity, side scattered intensity and fluorescence intensity; and the Measure the forward scattered light intensity distribution width D_LYM_FS_W, the forward scattered light intensity distribution center of gravity D_LYM_FS_P, the forward scattered light intensity distribution coefficient of variation D_LYM_FS_CV, the side scattered light intensity distribution width D_LYM_SS_W, and the side scattered light intensity distribution width D_LYM_SS_W of the lymphocyte population in the sample. Intensity distribution center of gravity D_LYM_SS_P, side scattered light intensity distribution coefficient of variation D_LYM_SS_CV, fluorescence intensity distribution width D_LYM_FL_W, fluorescence intensity distribution center of gravity D_LYM_FL_P, fluorescence intensity distribution coefficient of variation D_LYM_FL_CV and the lymphocyte population in the forward scattered light intensity, side scatter The area of the distribution area in the two-dimensional scatter diagram generated by two light intensities in light intensity and fluorescence intensity D_LYM_FLFS_Area (the distribution area of the lymphocyte population in the two-dimensional scatter diagram generated by forward scattered light intensity and fluorescence intensity area), D_LYM_FLSS_Area (the area of the distribution area of the lymphocyte population in the two-dimensional scatter plot generated by the side-scattered light intensity and the fluorescence intensity), D_LYM_SSFS_Area (the area of the lymphocyte population by the forward-scattered light intensity and the side-scattered light intensity The area of the distribution area in the two-dimensional scatter diagram generated by the intensity) and the volume of the distribution area of the lymphocyte population in the three-dimensional scatter diagram generated by the forward scattered light intensity, the side scatter intensity and the fluorescence intensity; the determination Forward scattered light intensity distribution width D_EOS_FS_W, forward scattered light intensity distribution center of gravity D_EOS_FS_P, forward scattered light intensity distribution coefficient of variation D_EOS_FS_CV, side scattered light intensity distribution width D_EOS_SS_W, side scatter Center of gravity of light intensity distribution D_EOS_SS_P, coefficient of variation of side scattered light intensity distribution D_EOS_SS_CV, width of fluorescence intensity distribution D_EOS_FL_W, center of gravity of fluorescence intensity distribution D_EOS_FL_P, coefficient of variation of fluorescence intensity distribution D_EOS_FL_CV and the eosinophil population in the forward scattered light intensity, The area of the distribution area D_EOS_FLFS_Area in the two-dimensional scatter diagram generated by the two kinds of light intensities in the side scattered light intensity and the fluorescence intensity The area of the distribution area in the figure), D_EOS_FLSS_Area (the area of the distribution area of the eosinophil population in the two-dimensional scatter plot generated by the side scattered light intensity and the fluorescence intensity), D_EOS_SSFS_Area (the eosinophil population in the The area of the distribution area in the two-dimensional scatter plot generated by forward scattered light intensity and side scattered light intensity) and the eosinophil population in the three-dimensional scatter plot generated by forward scattered light intensity, side scattered intensity and fluorescence intensity The area of the distribution area in the figure; and the forward scattered light intensity distribution width D_WBC_FS_W, the forward scattered light intensity distribution center of gravity D_WBC_FS_P, the forward scattered light intensity distribution coefficient of variation D_WBC_FS_CV, the side scatter Light intensity distribution width D_WBC_SS_W, side scattered light intensity distribution center of gravity D_WBC_SS_P, side scattered light intensity distribution coefficient of variation D_WBC_SS_CV, fluorescence intensity distribution width D_WBC_FL_W, fluorescence intensity distribution center of gravity D_WBC_FL_P, fluorescence intensity distribution coefficient of variation D_WBC_FL_CV and the white blood cell population in The area D_WBC_FLFS_Area of the distribution area in the two-dimensional scatter diagram generated by the forward scattered light intensity, the side scattered light intensity and the fluorescence intensity dimensional scatter diagram), D_WBC_FLSS_Area (the area of the distribution area of the white blood cell population in the two-dimensional scatter diagram generated by the side scattered light intensity and the fluorescence intensity), D_WBC_SSFS_Area (the white blood cell population is generated by the forward scatter The area of the distribution area in the two-dimensional scatter diagram generated by light intensity and side scatter intensity) and the distribution area of the leukocyte population in the three-dimensional scatter diagram generated by forward scattered light intensity, side scatter intensity and fluorescence intensity volume.

Preferably, the at least one white blood cell parameter may include one or more of the following parameters, such as one or two parameters: the forward scattered light intensity distribution width D_MON_FS_W of the mononuclear cell population in the measurement sample, the forward Scattered light intensity distribution center of gravity D_MON_FS_P, forward scattered light intensity distribution coefficient of variation D_MON_FS_CV, side scattered light intensity distribution width D_MON_SS_W, side scattered light intensity distribution center of gravity D_MON_SS_P, side scattered light intensity distribution coefficient of variation D_MON_SS_CV, fluorescence intensity distribution width D_MON_FL_W , the center of gravity of the fluorescence intensity distribution D_MON_FL_P, the coefficient of variation of the fluorescence intensity distribution D_MON_FL_CV, and the monocyte population in the two-dimensional scatter diagram generated by two light intensities in the forward scattered light intensity, side scattered light intensity and fluorescence intensity The area of the distribution area D_MON_FLFS_Area, D_MON_FLSS_Area, D_MON_SSFS_Area and the volume of the distribution area of the mononuclear cell population in the three-dimensional scatter plot generated by the forward scattered light intensity, side scattered light intensity and fluorescence intensity; and the determination Forward scattered light intensity distribution width D_NEU_FS_W, forward scattered light intensity distribution center of gravity D_NEU_FS_P, forward scattered light intensity distribution coefficient of variation D_NEU_FS_CV, side scattered light intensity distribution width D_NEU_SS_W, side scattered light intensity distribution width D_NEU_SS_W, The center of gravity of light intensity distribution D_NEU_SS_P, the coefficient of variation of side scattered light intensity distribution D_NEU_SS_CV, the width of fluorescence intensity distribution D_NEU_FL_W, the center of gravity of fluorescence intensity distribution D_NEU_FL_P, the coefficient of variation of fluorescence intensity distribution D_NEU_FL_CV and the neutrophil population in the forward scattered light intensity, The area of the distribution area D_NEU_FLFS_Area, D_NEU_FLSS_Area, D_NEU_SSFS_Area in the two-dimensional scatter diagram generated by the side scattered light intensity and the fluorescence intensity The volume of the distribution area in a 3D scatterplot generated by scattered light intensity and fluorescence intensity.

In other embodiments, the at least one white blood cell parameter may also include determining the classification parameter Mon% or counting parameter Mon# of the monocyte population Mon in the sample or the classification parameter Neu% or counting of the neutrophil population Neu The parameter Neu# or the classification parameter Lym% of the lymphocyte population Lym or the counting parameter Mon#.

Here, the meanings of the distribution width, the distribution center of gravity, the coefficient of variation, and the area or volume of the distribution region are explained with the help of FIG. 6 , wherein FIG. 6 shows a cell for measuring the neutrophil population in a sample according to some embodiments of the present application. Characteristic Parameters.

As shown in Figure 6, D_NEU_FL_W represents the width of the fluorescence intensity distribution of the neutrophil population in the measurement sample, wherein D_NEU_FL_W is equal to the upper limit S1 of the fluorescence intensity distribution of the neutrophil population and the lower limit of the fluorescence intensity distribution of the neutrophil population Difference of S2. D_NEU_FL_P represents the center of gravity of the fluorescence intensity distribution of the neutrophil population in the test sample, that is, the average position of the neutrophils in the FL direction, where D_NEU_FL_P is calculated by the following formula:

Among them, FL(i) is the fluorescence intensity of the i-th neutrophil. D_NEU_FL_CV represents the coefficient of variation of the fluorescence intensity distribution of the neutrophil population in the measurement sample, wherein D_NEU_FL_CV is equal to dividing D_NEU_FL_W by D_NEU_FL_P.

In addition, D_NEU_FLSS_Area represents the area of the distribution area of the neutrophil population in the measurement sample in the scattergram generated from the side scattered light intensity and the fluorescence intensity.

In some embodiments, as shown in FIG. 6 , C1 represents the contour distribution curve of the neutrophil population, for example, the total number of positions within the contour distribution curve C1 can be recorded as the area D_NEU_FLSS_Area of the neutrophil population.

In other embodiments, the D_NEU_FLSS_Area can also be implemented through the following algorithm steps (Figure 11):

Randomly select a particle P1 from the neutrophil (NEU) particle cluster, and find a particle P2 farthest from P1;

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);

Then take P1 as the starting point of the vector, find another particle P4 in the neutrophil (NEU) particle group, and construct the vector V3 (P1-P4), so that the vector V3 (P1-P4) and the vector V1 (P1-P2 ) into the largest angle;

By analogy, the outermost group of particles P1, P2, P3, P4, ... Pn of the neutrophil (NEU) particle group are respectively obtained;

Use an ellipse to fit particle points P1, P2, P3, P4,...Pn, and obtain the major axis a and minor axis b of the ellipse;

The D_NEU_FLSS_Area is the product of the major axis a and the minor axis b.

Similarly, 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. It can be understood here that the definition of other white blood cell parameters can refer to the embodiments shown in FIG. 6 and FIG. 11 in a corresponding manner.

In some embodiments, the infection marker parameter may consist of a single white blood cell parameter, such as one of the cell characteristic parameters listed above. Alternatively, the infection marker parameter may be a linear or non-linear function of individual leukocyte parameters.

In some other embodiments, the infection marker parameter can also be calculated by combining at least two white blood cell parameters, that is, the infection marker parameter is a function of at least two white blood cell parameters, such as a linear function. 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 of the present invention can improve the prediction, diagnosis and/or guiding treatment efficacy of the present invention.

In some other embodiments, 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).

To this end, in some embodiments, the processor 140 may be further configured to:

calculating at least one first leukocyte parameter of a first leukocyte cluster in said assay sample and at least one second leukocyte parameter of a second leukocyte cluster in said assay sample from said optical information; and

The infection marker parameter is calculated based on the at least one first leukocyte parameter and the at least one second leukocyte parameter.

Here, the first leukocyte cluster and the second leukocyte cluster are different from each other, and can be selected from monocyte population Mon, neutrophil population Neu, lymphocyte population Lym, and eosinophil population Eos in the measurement sample. composed of groups.

Preferably, the first leukocyte population is a monocyte population and the second leukocyte population is a neutrophil population. Accordingly, the at least one first white blood cell parameter preferably includes at least one cell characteristic parameter of the monocyte population, and the at least one second white blood cell parameter preferably includes at least one cell characteristic parameter of the neutrophil population.

Further preferably, the processor 140 may be further configured to combine the at least one first white blood cell parameter and the at least one second white blood cell parameter into an infection marker parameter through a linear function, that is, calculate the infection marker parameter through the following formula :

Y＝A*X1+B*X2+C

Wherein, Y represents an infection marker parameter, X1 represents a first white blood cell parameter, X2 represents a second white blood cell parameter, and A, B, and C are constants.

Of course, in other embodiments, the at least one first white blood cell parameter and the at least one 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.

In some other embodiments, the processor 140 may also be further configured to:

calculating at least two leukocyte parameters of a leukocyte cluster in said assay sample from said optical information; and

Said infection marker parameter is calculated based on said at least two leukocyte parameters, in particular via a linear function.

In some examples, for example, the parameter combinations shown in Table 1 can be used to calculate the infection marker parameters for identifying severe infections.

Table 1 Combinations of parameters used to identify severe infections

first leukocyte parameter	Second white blood cell parameter	first leukocyte parameter	Second white blood cell parameters
D_Mon_SS_W	D_Neu_FLFS_Area	D_Neu_FL_P	D_Neu_FS_P
D_Mon_SS_W	D_Neu_FLSS_Area	D_Mon_FL_P	D_Mon_FL_W
D_Mon_SS_W	D_Mon_FS_P	D_Neu_FS_P	D_Neu_FLFS_Area
D_Neu_FL_W	D_Mon_SS_W	D_Mon_FL_W	D_Mon_FS_P
D_Mon_SS_W	D_Mon_FL_W	D_Neu_SS_CV	D_Neu_FLFS_Area
D_Neu_SS_CV	D_Mon_SS_W	D_Neu_FS_CV	D_Mon_FL_W
D_Neu_FS_W	D_Mon_SS_W	D_Neu_FS_W	D_Mon_FL_W
D_Neu_FL_CV	D_Mon_SS_W	D_Neu_FS_P	D_Mon_FL_W
D_Mon_SS_W	D_Mon_FL_P	D_Neu_FL_CV	D_Mon_SS_P
D_Neu_FL_P	D_Mon_SS_W	D_Neu_FS_CV	D_Neu_FLFS_Area
D_Neu_FS_P	D_Mon_SS_W	D_Neu_FS_W	D_Neu_FLFS_Area
D_Mon_SS_P	D_Mon_SS_W	D_Neu_SS_W	D_Mon_SS_P
D_Neu_FS_CV	D_Mon_SS_W	D_Neu_SS_CV	D_Neu_FL_P

D_Mon_SS_W	D_Mon_FS_W	D_Neu_FS_P	D_Mon_SS_P
D_Neu_SS_W	D_Mon_SS_W	D_Mon_SS_P	D_Mon_FS_P
D_Neu_SS_P	D_Mon_SS_W	D_Mon_SS_P	D_Mon_FS_W
D_Mon_SS_P	D_Neu_FLSS_Area	D_Neu_FL_P	D_Neu_FS_CV
D_Mon_FL_W	D_Neu_FLSS_Area	D_Neu_SS_W	D_Neu_FL_P
D_Mon_FL_W	D_Neu_FLFS_Area	D_Neu_SS_P	D_Mon_SS_P
D_Mon_FS_W	D_Neu_FLSS_Area	D_Neu_FL_P	D_Mon_FL_P
D_Neu_FL_W	D_Neu_FLFS_Area	D_Neu_FS_CV	D_Mon_SS_P
D_Mon_SS_P	D_Neu_FLFS_Area	D_Neu_FL_P	D_Mon_FS_P
D_Neu_FL_P	D_Neu_FLFS_Area	D_Mon_SS_P	D_Mon_FL_P
D_Neu_FL_P	D_Neu_FLSS_Area	D_Neu_FS_W	D_Mon_SS_P
D_Neu_FL_W	D_Neu_FLSS_Area	D_Neu_SS_P	D_Neu_FL_P
D_Neu_FL_W	D_Mon_FS_W	D_Neu_FL_P	D_Neu_FS_W
D_Neu_FL_W	D_Mon_FL_W	D_Neu_FL_CV	D_Mon_FS_W
D_Neu_FL_P	D_Mon_FL_W	D_Neu_SS_W	D_Mon_FS_W
D_Neu_FL_W	D_Mon_SS_P	D_Neu_SS_P	D_Neu_FL_CV
D_Mon_SS_P	D_Mon_FL_W	D_Neu_SS_P	D_Mon_FS_W
D_Mon_FL_W	D_Mon_FS_W	D_Neu_SS_W	D_Neu_FL_CV
D_Mon_FL_P	D_Neu_FLSS_Area	D_Neu_SS_CV	D_Mon_FS_W
D_Mon_FS_W	D_Neu_FLFS_Area	D_Mon_FL_P	D_Mon_FS_W
D_Mon_FS_P	D_Neu_FLSS_Area	D_Neu_FL_CV	D_Mon_FS_P
D_Neu_FL_CV	D_Neu_FLSS_Area	D_Neu_SS_CV	D_Neu_FL_CV
D_Neu_FS_P	D_Neu_FLSS_Area	D_Neu_SS_W	D_Mon_FS_P
D_Neu_SS_CV	D_Neu_FLSS_Area	D_Neu_FS_W	D_Mon_FS_W
D_Neu_SS_W	D_Neu_FLSS_Area	D_Neu_SS_W	D_Neu_FS_P
D_Neu_FL_P	D_Mon_FS_W	D_Neu_FS_CV	D_Mon_FS_W
D_Neu_FS_W	D_Neu_FLSS_Area	D_Neu_SS_P	D_Neu_FS_P
D_Neu_FS_CV	D_Neu_FLSS_Area	D_Neu_SS_W	D_Mon_FL_P
D_Neu_SS_P	D_Neu_FLSS_Area	D_Neu_FS_P	D_Mon_FS_W
D_Neu_FLSS_Area	D_Neu_FLFS_Area	D_Mon_FS_P	D_Mon_FS_W
D_Neu_FL_W	D_Neu_FS_W	D_Neu_SS_CV	D_Mon_FS_P
D_Neu_FL_CV	D_Mon_FL_W	D_Neu_FL_CV	D_Neu_FS_CV
D_Neu_FL_W	D_Mon_FL_P	D_Neu_SS_P	D_Mon_FS_P
D_Neu_FL_W	D_Neu_FS_P	D_Neu_SS_W	D_Neu_FS_CV
D_Neu_SS_W	D_Neu_FLFS_Area	D_Neu_SS_P	D_Neu_SS_CV
D_Neu_SS_W	D_Mon_FL_W	D_Neu_SS_W	D_Neu_FS_W
D_Neu_SS_CV	D_Neu_FL_W	D_Neu_SS_P	D_Neu_SS_W
D_Neu_FL_W	D_Mon_FS_P	D_Neu_SS_W	D_Neu_SS_CV
D_Mon_FL_P	D_Neu_FLFS_Area	D_Neu_FL_CV	D_Neu_FS_W
D_Neu_SS_P	D_Mon_FL_W	D_Neu_SS_P	D_Mon_FL_P
D_Neu_FL_CV	D_Neu_FLFS_Area	D_Neu_FL_CV	D_Mon_FL_P
D_Neu_SS_W	D_Neu_FL_W	D_Neu_SS_P	D_Neu_FS_CV
D_Neu_FL_P	D_Mon_SS_P	D_Neu_FL_CV	D_Neu_FS_P
D_Neu_FL_W	D_Neu_FL_CV	D_Neu_SS_P	D_Neu_FS_W
D_Neu_SS_P	D_Neu_FLFS_Area	D_Neu_SS_CV	D_Mon_FL_P

D_Neu_FL_P	D_Neu_FL_CV	D_Neu_SS_CV	D_Neu_FS_W
D_Neu_SS_CV	D_Mon_FL_W	D_Neu_SS_CV	D_Neu_FS_CV
D_Neu_SS_CV	D_Mon_SS_P	D_Neu_SS_CV	D_Neu_FS_P
D_Neu_FL_W	D_Neu_FS_CV	D_Neu_FS_P	D_Mon_FS_P
D_Neu_SS_P	D_Neu_FL_W	D_Neu_FS_CV	D_Mon_FS_P
D_Mon_FS_P	D_Neu_FLFS_Area	D_Mon_FL_P	D_Mon_FS_P
D_Neu_FL_P	D_Neu_FL_W	D_Neu_FS_W	D_Mon_FS_P

In some embodiments, 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.

Optionally, the processor 140 may also be configured to output the preset range.

Next, some embodiments for further ensuring reliable diagnosis based on infection marker parameters are described, but it should be understood that the embodiments of the present application are not limited thereto.

In order to prevent the white blood cell parameters used to calculate the infection marker parameters from interfering with the diagnostic reliability, in some embodiments, the processor 140 may be further configured to, when the preset characteristic parameters of the at least one white blood cell particle cluster meet the preset condition, do not output the value of the infection flag parameter (that is, mask the value of the infection flag parameter), or output the value of the infection flag parameter and simultaneously output prompt information indicating that the value of the infection flag parameter is unreliable.

In a specific example, 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 value and at the same time output a prompt message indicating that the value of the infection flag parameter is unreliable.

That is to say, when the total number of particles in the white blood cell cluster is less than the preset threshold, that is, there are fewer particles in the white blood cell cluster, and the amount of information represented by the particles is limited, at this time the calculation result of the infection marker parameter may be unreliable. For example, as shown in FIG. 7( a ), the total number of particles of the leukocyte population in the measurement sample is too low, which may lead to unreliable infection marker parameters calculated from the leukocyte parameters of the leukocyte population.

Here, for example, it may be determined by measuring the optical information of the sample whether the preset characteristic parameters of the leukocyte cluster are abnormal, for example, whether the total number of particles of the leukocyte cluster is lower than a preset threshold.

In some other examples, 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 when the at least one leukocyte particle cluster overlaps with other particle clusters And at the same time output prompt information indicating that the value of the infection flag parameter is unreliable.

For example, as shown in Figure 7(b), there is an overlap between the monocyte population and the lymphocyte population in the assay sample, which may lead to the calculation of infection marker parameters from the white blood cell parameters of the monocyte population or the lymphocyte population Unreliable.

Here, for example, by measuring the optical information of the sample, it can be determined whether the leukocyte particle cluster used overlaps with other particle clusters.

In addition, the disease status of the subject and the abnormal cells in the blood of the subject may also affect the diagnostic efficacy of the infection marker parameters. To this end, the processor 140 can be further configured to: according to whether the subject suffers from a specific disease and/or whether there are preset types of abnormal cells (such as blast cells, abnormal lymphocytes, immature granulocytes, etc.) in the blood sample to be tested ) to determine whether the infection flag parameters are reliable.

In some specific examples, the processor 140 may be configured to not output the infection marker parameter when the subject suffers from a blood disease or there are abnormal cells, especially primitive cells, in the blood sample to be tested value, 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. Understandably, subjects with hematologic disorders have abnormal hematograms, rendering a diagnosis based on this infection marker parameter unreliable.

For example, the processor 140 may acquire whether the subject suffers from a blood disease according to the identity information of the subject.

In some embodiments, the processor 140 may be configured to determine whether there are abnormal cells, especially primitive cells, in the blood sample to be tested according to the optical information.

In some embodiments, the processor 140 can also be configured to perform data processing on the used white blood cell parameters before calculating the infection marker parameters, such as removing noise (impurity particles) interference (as shown in FIG. 7(c)), so that More accurate calculation of infection marker parameters, such as avoiding signal changes caused by different instruments and different reagents.

In some embodiments, the processor 140 may be configured to output prompt information indicating that the subject has a severe infection when it is determined according to the infection flag parameters that the subject has a severe infection. For example, when the value of the infection marker parameter is greater than a preset threshold, it is determined that the subject suffers from severe infection. The preset threshold can be determined according to specific parameters or parameter combinations and the blood cell analyzer.

Further, 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 . For example, the processor 140 may output the prompt information to the display device on the user (doctor) side through the hospital information management system.

Similarly, the processor 140 may be further configured to, when the preset characteristic parameter of the at least one white blood cell cluster satisfies a preset condition, for example, when the total number of particles of the at least one white blood cell cluster is less than a preset threshold and/or or when the at least one leukocyte particle cluster overlaps with other particle clusters, do not output prompt information indicating that the subject suffers from a severe infection, or output the prompt information and output additional information that the prompt information is unreliable .

Alternatively or additionally, the processor 140 may be further configured to: 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, according to the optical When the information determines that there are abnormal cells in the blood sample to be tested, no prompt information indicating that the subject suffers from severe infection is not output, or the prompt information is output and additional information that the prompt information is unreliable is output.

The manner in which the processor 140 configures a priority for each infection flag parameter group will be described below in conjunction with some of the following embodiments. In some embodiments, 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.

Here, preferably, 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.

In some embodiments, the infection marker parameter set of the present application can be used for the assessment of various infection states, for example, for the identification of severe infection. Correspondingly, the diagnostic efficacy of infection includes the diagnostic efficacy for differentiating common infection from severe infection. For example, 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.

As some implementation manners, 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. Among them, 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, and the ROC_AUC of each infection marker parameter group can reflect the infection diagnostic efficacy of the infection marker parameter group.

In some embodiments, the parameter stability includes at least one of numerical repeatability, aging stability, temperature stability, and machine-to-machine consistency. Among them, the 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; the 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. When the instrument detects the same blood sample to be tested, the stability of the value of the infection marker parameter group used; the consistency between machines 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.

In some examples, if 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, the higher the consistency of the values of the infection marker parameter sets used, that is, the numerical repeatability The higher the value, the higher the priority of the infection flag parameter group.

Alternatively or additionally, if the same instrument is used in the same environment to detect the same blood sample to be tested at different time points, the higher the stability of the value of the infection marker parameter set used (that is, the smaller the fluctuation of the value) ), that is, the higher the aging stability, the higher the priority of the infection flag parameter group.

Alternatively or additionally, if the same instrument is used to detect the same blood sample to be tested under different temperature environments, the higher the stability of the value of the infection marker parameter set used (that is, the smaller the fluctuation of the value), That is, the higher the temperature stability, the higher the priority of the infection flag parameter group.

Alternatively or additionally, when the same blood sample to be tested is tested on different instruments in the same environment, the higher the consistency of the values of the infection marker parameter sets used, that is, the higher the consistency between machines, the higher the The infection flag parameter group has higher priority.

In some embodiments, 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.

In some embodiments, 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. Here, the processor 140 may configure a priority for each infection flag parameter group according to the preset. For example, 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.

Next, the manner in which the processor 140 calculates the reliability of the infection flag parameter set is further described in conjunction with the following embodiments.

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.

In some embodiments, 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.

In some embodiments, 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). For example, 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. For example, if the total number of particles in the target particle cluster, that is, the count value, is less than the preset threshold, that is, the target particle cluster has fewer particles, and the amount of information represented by the particles is limited, at this time, the infection flag parameter set obtained through the relevant parameters of the target particle cluster Possibly unreliable, so the confidence in this set of infection flags parameters is low.

Next, the manner in which the processor 140 screens the parameter set of infection flags is further described in conjunction with some embodiments.

In this embodiment of the present application, 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.

In some other embodiments, the processor 140 may be configured to perform the following steps to screen the infection flag parameter set and output its parameter value:

obtaining a plurality of parameters for measuring at least one target particle cluster in the sample from the optical information;

Deriving a plurality of parameter sets of infection markers for assessing the subject's infection status from the plurality of parameters;

According to the priorities of the plurality of infection flag parameter groups, sequentially calculate the credibility of the multiple infection flag parameter groups and determine whether the credibility reaches a corresponding credibility threshold;

When the credibility of the current infection flag parameter set reaches the corresponding credibility threshold, the parameter value of the infection flag parameter set is output and the calculation and judgment are stopped.

In some embodiments, 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.

Here, for example, 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.

In some other embodiments, 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,

obtaining a plurality of sets of infection marker parameters for assessing the subject's infection status from the plurality of parameters,

calculating the credibility of each infection marker parameter set in the plurality of infection marker parameter sets, and selecting at least one infection marker from the plurality of infection marker parameter sets according to the credibility of the plurality of infection marker parameter sets parameter group and output its parameter values.

In some embodiments, 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.

Further, the processor is further configured to:

When the parameter value of the selected infection flag parameter group is greater than the infection positive threshold, an alarm prompt is output.

In some other embodiments, 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.

In one example, if it is judged that there is an abnormal classification result affecting the assessment of the infection status in the blood sample to be tested, for example, when there is overlap between monocyte clusters and neutrophil clusters in the blood sample to be tested, 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.

In another example, if it is judged that there are abnormal cells affecting the evaluation of the infection status in the blood sample to be tested, such as blast cells, 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.

Next, the manner in which the processor 140 controls retesting is further described in conjunction with some embodiments.

In some embodiments, the processor may be further configured to:

Prior to obtaining at least one leukocyte parameter of at least one leukocyte particle cluster in the assay sample from the optical information, obtaining a leukocyte count of the subject, and outputting a response to all leukocyte counts when the leukocyte count is less than a predetermined threshold A test-retest order for re-measurement of a blood sample from the subject, wherein the assay based on the retest order has a greater sample volume than the assay used to obtain the optical information; and

At least another leukocyte parameter of at least one other leukocyte particle mass is obtained from the optical information measured based on the retest instruction, and an infection marker parameter for identification of severe infection is obtained based on the at least one other leukocyte parameter.

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;

The controller is configured to: receive a mode setting instruction, and 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 measurement The optical information of the sample, and based on the optical information, acquire and output the blood routine parameters of the measured sample; when the mode setting instruction indicates that the sepsis detection mode is selected, control the measuring device to a value greater than the first measured amount The measurement sample of the second measurement amount is optically measured to obtain optical information of the measurement sample, and at least one leukocyte parameter of at least one leukocyte particle cluster in the measurement sample is calculated from the optical information, based on the The at least one white blood cell parameter is used to obtain an infection marker parameter, and output the infection marker parameter, and the infection marker parameter is used for identification of severe infection.

For this reason, when the white blood cell count in the sample is less than the preset threshold and the test parameter results are unreliable, the sample analyzer can be controlled to perform a retest action, so as to obtain more accurate infection marker parameters for differential diagnosis of severe infection.

The embodiment of the present application also proposes a method 200 for identifying whether a subject has a severe infection. As shown in FIG. 8, the method 200 includes the following steps:

S210, collecting the subject's blood sample to be tested;

S220, preparing a measurement sample containing a part of the blood sample to be tested, a hemolytic agent and a staining agent for leukocyte classification;

S230, making the particles in the measurement sample pass through the optical detection area irradiated with light one by one, so as to obtain the optical information generated by the particles in the measurement sample after being irradiated with light;

S240. Obtain at least one white blood cell parameter of at least one white blood cell particle cluster in the measurement sample from the optical information;

S250, obtaining an infection marker parameter based on the at least one white blood cell parameter; and

S260. Determine whether the subject suffers from severe infection according to the infection marker parameters.

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.

Further, in the method 200, the at least one white blood cell parameter may include one or more of the cell characteristic parameters of monocyte population, neutrophil population and lymphocyte population in the measurement sample. Preferably, the at least one white blood cell parameter may include one or more of the cell characteristic parameters of the monocyte population and the neutrophil population in the measurement sample.

Further, in the method 200, the at least one white blood cell parameter may include one or more of the following parameters: the width of the forward scattered light intensity distribution of the white blood cell particle cluster, the center of gravity of the forward scattered light intensity distribution, the forward Coefficient of variation of intensity distribution of scattered light, width of intensity distribution of side scattered light, center of gravity of intensity distribution of side scattered light, coefficient of variation of intensity distribution of side scattered light, width of distribution of fluorescence intensity, center of gravity of distribution of fluorescence intensity, coefficient of variation of fluorescence intensity distribution and all The area of the distribution area of the leukocyte particle group in the two-dimensional scatter plot generated by two kinds of light intensities in the forward scattered light intensity, side scattered light intensity and fluorescence intensity and the distribution area of the leukocyte particle group in the forward scattered light intensity The volume of the distribution area in the 3D scatterplot generated by light intensity, side-scattered light intensity, and fluorescence intensity.

Preferably, the at least one white blood cell parameter may include one or more of the following parameters: the width of the forward scattered light intensity distribution, the center of gravity of the forward scattered light intensity distribution, the forward direction 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, fluorescence intensity distribution width, fluorescence intensity distribution center of gravity, fluorescence intensity distribution coefficient of variation and the The area or volume of the distribution area of the monocyte population in the two-dimensional scatter plot generated by the two light intensities of the forward scattered light intensity, the side scattered light intensity and the fluorescence intensity and the distribution area of the monocyte population by The volume of the distribution area in the three-dimensional scatter diagram generated by the forward scattered light intensity, the side scattered light intensity and the fluorescence intensity; and the forward scattered light intensity distribution width, front Gravity center of forward scattered light intensity distribution, coefficient of variation of forward scattered light intensity distribution, width of side scattered light intensity distribution, center of gravity of side scattered light intensity distribution, coefficient of variation of side scattered light intensity distribution, width of fluorescence intensity distribution, center of gravity of fluorescence intensity distribution , the coefficient of variation of fluorescence intensity distribution, and the sum of the area of the distribution area of the neutrophil population in the two-dimensional scatter diagram generated by the two light intensities of forward scattered light intensity, side scattered light intensity and fluorescence intensity The volume of the distribution area of the neutrophil population in the three-dimensional scatter plot generated by the forward scattered light intensity, side scattered light intensity and fluorescence intensity.

Further, step S240 and step S250 may include:

At least one first leukocyte parameter of a first leukocyte cluster in said assay sample and at least one second leukocyte parameter of a second leukocyte cluster in said assay sample are obtained from said optical information, preferably said first leukocyte parameter a population of leukocytes is a population of monocytes and said second population of leukocytes is a population of neutrophils; and

Said infection marker parameter is calculated based on said at least one first leukocyte parameter and said at least one second leukocyte parameter, in particular via a linear function.

Alternatively, step S240 and step S250 may include:

obtaining at least two leukocyte parameters of a leukocyte cluster in said assay sample from said optical information; and

Said infection marker parameter is calculated based on said at least two leukocyte parameters, in particular via a linear function.

Further, the method 200 may further include: when the value of the infection flag parameter is outside a preset range, outputting prompt information indicating that the infection flag parameter is abnormal.

Further, the method 200 may also include:

When the preset characteristic parameter of the at least one white blood cell cluster meets the preset condition, for example, when the total number of particles in the at least one white blood cell cluster is less than a preset threshold and/or when the at least one white blood cell cluster is mixed with other particles When clusters overlap, 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 prompt information indicating that the value of the infection flag parameter is unreliable.

Alternatively or additionally, the method 200 may further include: when the subject suffers from a blood disease or abnormal cells, especially primitive cells, exist in the blood sample to be tested, for example, according to the optical information When it is judged that there are abnormal cells, especially primitive cells, in the blood sample to be tested, 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.

Optionally, the method 200 further includes: when judging whether the subject has a severe infection according to the infection marker parameters, outputting prompt information indicating that the subject has a severe infection.

For more embodiments and advantages of the method 200 proposed in the embodiment of the present application, reference may be made to the above description of the blood cell analyzer 100 proposed in the embodiment of the present application, especially the description of the method steps implemented by the processor 140, which will not be repeated here. repeat.

The embodiment of the present application also proposes the use of infection marker parameters in identifying whether a subject has a severe infection, wherein the infection marker parameters are obtained by the following method:

obtaining at least one leukocyte parameter of at least one leukocyte particle cluster detected by flow cytometry on an assay sample comprising a portion of a test blood sample from a subject, a hemolyzing agent, and a stain for leukocyte classification; and

An infection marker parameter is obtained based on the at least one white blood cell parameter.

For more examples and advantages of the use of the infection marker parameters proposed in the embodiments of the present application in evaluating the infection status of subjects, please refer to the above description of the blood cell analyzer 100 proposed in the embodiments of the present application, especially the processor 140 The description of the implemented method steps will not be repeated here.

Next, the present application and its advantages are further described through specific embodiments.

The 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:

True positive rate%=TP/(TP+FN)×100%;

True negative rate%=TN/(FP+TN)×100%;

False positive rate%=1-true negative rate%;

False negative rate%=1-true positive rate%;

Among them, TP is the number of true positive individuals, FP is the number of false positive individuals, TN is the number of true negative individuals, and FN is the number of false negative individuals.

The BC-6800Plus blood cell analyzer produced by Shenzhen Mindray Biomedical Electronics Co., Ltd. was used to detect blood samples from 1528 donors according to the method proposed in the embodiment of this application, so as to identify severe infections. Among them, there were 756 cases of severe infection samples, that is, positive samples, and 792 cases of non-severe infection samples, that is, negative samples.

Inclusion criteria: 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 severe infection samples mentioned above: have suspicious or definite infection sites, positive laboratory culture results, and organ dysfunction, which meet any one or more of the following:

① Evidence of systemic, widespread, and body cavity disseminated infection

② There are life-threatening special site infections

③Infection caused at least one organ function index abnormality caused by infection

The others were non-severely infected samples.

Table 2 shows the use of a single leukocyte parameter as an infection marker parameter and its corresponding diagnostic efficacy, and Table 3 shows the use of two parameter combinations (the first leukocyte parameter and the second leukocyte parameter combination) as an infection marker parameter and its corresponding diagnostic efficacy , and Table 4 shows the respective coefficients of the function Y=A*X1+B*X2+C for calculating the infection marker parameters based on the first white blood cell parameter and the second white blood cell parameter in Table 3, wherein Y represents the infection marker parameter , X1 represents the first white blood cell parameter, X2 represents the second white blood cell parameter, and A, B, and C are constants. Figure 9 shows a ROC curve using a single leukocyte parameter as an infection marker parameter, and Figure 10 shows a ROC curve using a combination of two leukocyte parameters as an infection marker parameter.

Among them, 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; true negative is It means that the prompt result obtained in this embodiment matches the clinical condition of the patient and they are all patients with common infection; false negative means that the prompt result obtained in this embodiment is common infection, but the actual condition of the patient is severe infection.

Table 2 Efficiency of a single parameter in the diagnosis of severe infection

Table 3 The effectiveness of dual parameters in the diagnosis of severe infection

Table 4 Coefficients used to calculate infection marker parameters

first leukocyte parameter	Second white blood cell parameter	A	B	C
D_Mon_SS_W	D_Neu_FLFS_Area	0.070301	0.002663	-9.43013
D_Mon_SS_W	D_Neu_FLSS_Area	0.062337	0.003069	-8.38946
D_Mon_SS_W	D_Mon_FS_P	0.091834	-0.00586	-0.57654
D_Neu_FL_W	D_Mon_SS_W	0.009491	0.065123	-7.86304
D_Mon_SS_W	D_Mon_FL_W	0.064609	0.009496	-9.81162
D_Neu_SS_CV	D_Mon_SS_W	4.789869	0.076408	-10.2364
D_Neu_FS_W	D_Mon_SS_W	-0.00393	0.083145	-5.13267
D_Neu_FL_CV	D_Mon_SS_W	3.531762	0.074525	-8.24038
D_Mon_SS_W	D_Mon_FL_P	0.081386	-0.00113	-6.11002
D_Neu_FL_P	D_Mon_SS_W	0.007154	0.067606	-9.36314
D_Neu_FS_P	D_Mon_SS_W	-0.00168	0.080976	-4.21378
D_Mon_SS_P	D_Mon_SS_W	-0.00404	0.082551	-6.42678
D_Neu_FS_CV	D_Mon_SS_W	-1.91024	0.079902	-6.45037
D_Mon_SS_W	D_Mon_FS_W	0.078448	0.000315	-7.05588

D_Neu_SS_W	D_Mon_SS_W	0.004196	0.074491	-7.72417
D_Neu_SS_P	D_Mon_SS_W	0.002635	0.07609	-7.70882
D_Mon_SS_P	D_Neu_FLSS_Area	0.040385	0.003706	-12.2402
D_Mon_FL_W	D_Neu_FLSS_Area	0.01108	0.003447	-8.03873
D_Mon_FL_W	D_Neu_FLFS_Area	0.012747	0.002781	-8.88668
D_Mon_FS_W	D_Neu_FLSS_Area	0.008568	0.004008	-6.93636
D_Neu_FL_W	D_Neu_FLFS_Area	0.016814	0.002876	-7.21989
D_Mon_SS_P	D_Neu_FLFS_Area	0.046609	0.003087	-13.844
D_Neu_FL_P	D_Neu_FLFS_Area	0.013022	0.003111	-9.92741
D_Neu_FL_P	D_Neu_FLSS_Area	0.009593	0.003475	-7.81004
D_Neu_FL_W	D_Neu_FLSS_Area	0.011788	0.003284	-5.71331
D_Neu_FL_W	D_Mon_FS_W	0.017958	0.008011	-6.95146
D_Neu_FL_W	D_Mon_FL_W	0.015177	0.012019	-8.57224
D_Neu_FL_P	D_Mon_FL_W	0.010335	0.012627	-10.3629
D_Neu_FL_W	D_Mon_SS_P	0.014726	0.034953	-10.8566
D_Mon_SS_P	D_Mon_FL_W	0.038278	0.01213	-13.5517
D_Mon_FL_W	D_Mon_FS_W	0.013932	0.008157	-9.04112
D_Mon_FL_P	D_Neu_FLSS_Area	0.003163	0.004156	-6.96995
D_Mon_FS_W	D_Neu_FLFS_Area	0.008852	0.003167	-7.11278
D_Mon_FS_P	D_Neu_FLSS_Area	0.005118	0.00402	-10.3894
D_Neu_FL_CV	D_Neu_FLSS_Area	3.448061	0.003835	-5.22826
D_Neu_FS_P	D_Neu_FLSS_Area	-0.00101	0.004105	-2.09609
D_Neu_SS_CV	D_Neu_FLSS_Area	0.520527	0.004093	-4.23801
D_Neu_SS_W	D_Neu_FLSS_Area	0.001034	0.004039	-4.08879
D_Neu_FL_P	D_Mon_FS_W	0.012851	0.009334	-9.53898
D_Neu_FS_W	D_Neu_FLSS_Area	0.00013	0.00413	-3.9683
D_Neu_FS_CV	D_Neu_FLSS_Area	1.336107	0.004116	-4.3147
D_Neu_SS_P	D_Neu_FLSS_Area	0.000728	0.004004	-4.04252
D_Neu_FLSS_Area	D_Neu_FLFS_Area	0.003751	0.000124	-3.673
D_Neu_FL_W	D_Neu_FS_W	0.021411	-0.00362	-2.73062
D_Neu_FL_CV	D_Mon_FL_W	7.450739	0.013805	-9.46385
D_Neu_FL_W	D_Mon_FL_P	0.022732	-0.00274	-2.42596
D_Neu_FL_W	D_Neu_FS_P	0.019285	-0.00094	-2.66839
D_Neu_SS_W	D_Neu_FLFS_Area	0.010274	0.002997	-6.41678
D_Neu_SS_W	D_Mon_FL_W	0.009221	0.013791	-8.47423

It can be seen from Tables 2 to 3 and Figures 9 to 10 that the infection marker parameters proposed in this application can be used to more effectively diagnose whether a subject has severe infection.

The features or combinations of features mentioned above in the description, drawings and claims can be used in any combination or alone as long as they are meaningful within the scope of the application and do not contradict each other.

The above is only a preferred embodiment of the application, and does not limit the patent scope of the application. Under the inventive concept of the application, the equivalent transformation made by using the description of the application and the contents of the accompanying drawings, or directly/indirectly used in Other relevant technical fields are included in the scope of patent protection of this application.

Claims (19)

1. A blood cell analyzer, comprising:

   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 leukocyte classification;

   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 illuminate 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:

   calculating at least one leukocyte parameter of at least one leukocyte particle cluster in said assay sample from said optical information,

   obtaining an infection marker parameter based on said at least one leukocyte parameter, and

   The infection flag parameter is output, and the infection flag parameter is used to judge whether the subject suffers from severe infection.
2. The blood cell analyzer according to claim 1, wherein the at least one white blood cell parameter includes one of the cell characteristic parameters of the monocyte population, the neutrophil population and the lymphocyte population in the measurement sample. one or more;

   Preferably, the at least one white blood cell parameter includes one or more of the cell characteristic parameters of the monocyte population and the neutrophil population in the measurement sample.
3. The blood cell analyzer according to claim 1 or 2, wherein the at least one white blood cell parameter includes one or more of the following parameters: the width of the forward scattered light intensity distribution of the white blood cell particle cluster, the forward direction Center of gravity of scattered light intensity distribution, coefficient of variation of forward scattered light intensity distribution, width of side scattered light intensity distribution, center of gravity of side scattered light intensity distribution, coefficient of variation of side scattered light intensity distribution, width of fluorescence intensity distribution, center of gravity of fluorescence intensity distribution, Fluorescence intensity distribution coefficient of variation and the area of the distribution area of the leukocyte particle group in the two-dimensional scatter diagram generated by the two light intensities of forward scattered light intensity, side scattered light intensity and fluorescence intensity and the leukocyte The volume of the distribution area of the particle cluster in the three-dimensional scatter diagram generated by the forward scattered light intensity, side scattered light intensity and fluorescence intensity;

   Preferably, the at least one white blood cell parameter includes one or more of the following parameters: the width of the forward scattered light intensity distribution, the center of gravity of the forward scattered light intensity distribution, and the forward scattered light intensity of the mononuclear cell population in the measurement sample. Distribution coefficient of variation, width of side scattered light intensity distribution, center of gravity of side scattered light intensity distribution, coefficient of variation of side scattered light intensity distribution, width of fluorescence intensity distribution, center of gravity of fluorescence intensity distribution, coefficient of variation of fluorescence intensity distribution and the monocyte The area of the distribution area of the population in the two-dimensional scatter plot generated by two light intensities of forward scattered light intensity, side scattered light intensity and fluorescence intensity and the distribution area of the monocyte population in , the volume of the distribution area in the three-dimensional scatter diagram generated by side scattered light intensity and fluorescence intensity; and the forward scattered light intensity distribution width and forward scattered light intensity distribution of the neutrophil population in the measurement sample Center of gravity, coefficient of variation of forward scattered light intensity distribution, width of side scattered light intensity distribution, center of gravity of side scattered light intensity distribution, coefficient of variation of side scattered light intensity distribution, width of fluorescence intensity distribution, center of gravity of fluorescence intensity distribution, variation of fluorescence intensity distribution The coefficient and the area of the distribution area of the neutrophil population in the two-dimensional scatter plot generated by two light intensities of forward scattered light intensity, side scattered light intensity and fluorescence intensity and the neutrophil population The volume of the distribution area of a cell population in a 3D scatterplot generated from forward-scattered light intensity, side-scattered light intensity, and fluorescence intensity.
4. The blood cell analyzer according to any one of claims 1 to 3, wherein the processor is further configured to:

   At least one first leukocyte parameter of a first leukocyte cluster in said assay sample and at least one second leukocyte parameter of a second leukocyte cluster in said assay sample are calculated from said optical information, preferably said first leukocyte parameter a population of leukocytes is a population of monocytes and said second population of leukocytes is a population of neutrophils; and

   Said infection marker parameter is calculated based on said at least one first leukocyte parameter and said at least one second leukocyte parameter, in particular via a linear function.
5. The blood cell analyzer according to any one of claims 1 to 3, wherein the processor is further configured to:

   calculating at least two leukocyte parameters of a leukocyte cluster in said assay sample from said optical information; and

   Said infection marker parameter is calculated based on said at least two leukocyte parameters, in particular via a linear function.
6. The blood cell analyzer according to any one of claims 1 to 5, wherein the processor is further configured to:

   When the value of the infection flag parameter is outside the preset range, output prompt information indicating that the infection flag parameter is abnormal.
7. The blood cell analyzer according to any one of claims 1 to 6, wherein the processor is further configured to:

   When the preset characteristic parameter of the at least one white blood cell cluster satisfies the preset condition, the value of the infection flag parameter is not output, or the value of the infection flag parameter is output and at the same time, the value indicating that the infection flag parameter is unreliable is output. prompt information.
8. The blood cell analyzer according to claim 7, wherein the processor is further configured to:

   When the total number of particles of the at least one white blood cell cluster is less than a preset threshold and/or the at least one white blood cell cluster overlaps with other clusters, the value of the infection flag parameter is not output, or the infection flag parameter is output. and at the same time output a prompt message indicating that the value of the infection flag parameter is unreliable.
9. The blood cell analyzer according to any one of claims 1 to 8, wherein the processor is further configured to:

   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 it is judged based on the optical information that there are abnormal cells, especially primitive cells, in the blood sample to be tested When a cell is detected, the value of the infection flag parameter is not output, or the value of the infection flag parameter is output and a prompt message indicating that the value of the infection flag parameter is unreliable is output at the same time.
10. The blood cell analyzer according to any one of claims 1 to 9, wherein the processor is further configured to:

    When it is judged that the subject suffers from a severe infection according to the infection marker parameters, a prompt message indicating that the subject suffers from a severe infection is output.
11. The blood cell analyzer according to any one of claims 1 to 7, wherein the processor is further configured to calculate at least one leukocyte particle cluster in the measurement sample from the optical information Before at least one white blood cell parameter of the subject, the white blood cell count of the subject is obtained, and when the white blood cell count is less than a preset threshold value, a retest instruction for retesting the blood sample of the subject is output, wherein, based on the a sample measurement volume of the assay of said retest order is greater than the sample assay volume of the assay used to obtain said optical information; and

    The processor is further configured to calculate at least another leukocyte parameter of at least another leukocyte particle cluster from the optical information measured based on the retest instruction, and obtain a judgment based on the at least another leukocyte parameter that the subject Infection marker parameters of whether the patient suffers from severe infection.
12. The blood cell analyzer according to any one of claims 1 to 7, wherein the processor calculates at least one leukocyte parameter of at least one leukocyte particle cluster in the measurement sample from the optical information, and based on The at least one white blood cell parameter obtains an infection flag parameter, including: the processor

    calculating a plurality of parameters of at least one leukocyte cluster in said assay sample from said optical information;

    Obtaining a plurality of infection marker parameter sets for judging whether the subject suffers from severe infection from the plurality of parameters;

    configuring a priority for each of the plurality of taint flag parameter sets;

    calculating the credibility of each infection flag parameter group in the plurality of infection flag parameter groups, and selecting at least An infection flag parameter group, used to obtain the infection flag parameters; or according to the priorities of the multiple infection flag parameter groups, sequentially calculate the credibility of the multiple infection flag parameter groups and determine whether the credibility When the corresponding credibility threshold is reached, when the credibility of the current infection flag parameter set reaches the corresponding credibility threshold, the infection flag parameters are acquired based on the infection flag parameter set and the calculation and judgment are stopped.
13. The blood cell analyzer according to claim 12, wherein the processor is further configured to:

    calculating the credibility of each infection flag parameter set among the plurality of infection flag parameter sets, and judging whether the credibility of each infection flag parameter set reaches a corresponding credibility threshold;

    Taking an infection flag parameter group whose credibility reaches a corresponding confidence threshold among the plurality of infection flag parameter groups as a candidate infection flag parameter group;

    Selecting at least one candidate infection flag parameter group from the candidate infection flag parameter groups according to the priority of the candidate infection flag parameter group, preferably selecting the infection flag parameter group with the highest priority, for obtaining the infection flag parameter.
14. The blood cell analyzer according to any one of claims 1 to 7, wherein the processor calculates at least one leukocyte parameter of at least one leukocyte particle cluster in the measurement sample from the optical information, and based on The at least one white blood cell parameter obtains an infection flag parameter, including: the processor

    calculating a plurality of parameters of at least one leukocyte cluster in said assay sample from said optical information,

    Obtaining a plurality of infection marker parameter sets for judging whether the subject suffers from severe infection from the plurality of parameters,

    calculating the credibility of each infection marker parameter set in the plurality of infection marker parameter sets, and selecting at least one infection marker from the plurality of infection marker parameter sets according to the credibility of the plurality of infection marker parameter sets A parameter group, used to obtain the infection flag parameters.
15. The blood cell analyzer according to any one of claims 1 to 7, wherein the processor calculates at least one leukocyte parameter of at least one leukocyte particle cluster in the measurement sample from the optical information, and based on The at least one white blood cell parameter obtains an infection flag parameter, including: the processor

    judging whether there is an abnormality in the blood sample to be tested according to the optical information;

    When it is determined that there is an abnormality in the blood sample to be tested that affects the judgment of severe infection, at least one white blood cell parameter of at least one white blood cell particle cluster matching the abnormality is obtained from the optical information to obtain the infection marker parameter.
16. The blood cell analyzer according to any one of claims 1 to 15, characterized in that the diagnostic efficacy of the infection marker parameters is greater than 0.5, preferably greater than 0.6, more preferably greater than 0.8.
17. A blood cell analyzer, comprising:

    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 performing optical measurement on the measurement sample to acquire optical information of the measurement sample; and

    Controller, configured as:

    Receive mode setting command,

    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,

    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, At least one leukocyte parameter of at least one leukocyte particle cluster in the assay sample is calculated from the optical information, an infection signature parameter is obtained based on the at least one leukocyte parameter, and the infection signature parameter is output, the infection signature parameter is used It is used to judge whether the subject suffers from severe infection.
18. A method for identifying whether a subject has a severe infection, comprising:

    Obtaining a blood sample to be tested from the subject;

    preparing a measurement sample containing a part of the blood sample to be tested, a hemolyzing agent, and a staining agent for leukocyte classification;

    making the particles in the measurement sample pass through the optical detection zone irradiated by light one by one, so as to obtain the optical information generated by the particles in the measurement sample after being irradiated by light;

    calculating at least one leukocyte parameter of at least one leukocyte particle cluster in said assay sample from said optical information;

    obtaining an infection marker parameter based on said at least one leukocyte parameter; and

    Whether the subject suffers from severe infection is judged according to the infection marker parameters.
19. Use of infection marker parameters in identifying whether a subject suffers from severe infection, wherein the infection marker parameters are obtained by the following method:

    obtaining at least one leukocyte parameter of at least one leukocyte particle cluster detected by flow cytometry on an assay sample comprising a portion of a test blood sample from a subject, a hemolyzing agent, and a stain for leukocyte classification; and

    An infection marker parameter is obtained based on the at least one white blood cell parameter.

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