Combined detection kit for detecting sepsis and use thereof

Abstract

A combined detection kit for detecting sepsis and the use thereof. The present invention relates to the use of a combination of at least two markers of CD14, CD95, HBP, HMGB-1, IFN-α, IFN-γ, IL-1β, IL-8, MIP-1β, MIF, PCT, TNF-α, sTREM-1, TLR-4 and TSP-1 in the detection of sepsis. The early diagnosis and screening of sepsis can be achieved by means of detecting relevant samples such as blood, urine, cerebrospinal fluid, feces, sputum or tissue fluid and combining algorithms to build a model, and bacterial infection, viral infection and sepsis can be distinguished. The combined detection kit has good diagnosis results in both the systemic inflammatory response stage and the compensatory anti-inflammatory response stage of sepsis; and under the condition of 98% specificity, the diagnosis sensitivity can reach 98%.

Description

A combined detection kit for detecting sepsis and its application Technical Field

[0001] The present invention relates to the technical field of biomedicine, and in particular to a combined detection kit for detecting sepsis and applications thereof. Background Art

[0002] Sepsis is a serious threat to human health. According to relevant research, approximately 31.5 million people worldwide develop sepsis annually, of which approximately 19.4 million develop severe sepsis, and 5.3 million ultimately die. Many sepsis studies primarily incorporate clinical data from developed countries, whereas the incidence and mortality rates are actually higher in underdeveloped or developing countries. A 2020 study of ICUs in 44 hospitals in my country revealed a sepsis incidence rate of 20.6% and a mortality rate of 35.5%, with the mortality rate for severe sepsis reaching as high as 50%. Since the concept and diagnostic criteria for sepsis were first proposed in 1991, the diagnostic criteria and definitions have evolved over the past 30 years. The latest definition, Sepsis 3.0, was proposed in 2016 and defines it as a clinical syndrome characterized by an uncontrolled host response to infection and life-threatening organ dysfunction, using the diagnostic criteria of "infection + SOFA ≥ 2." The SOFA score covers six major (organ) systems of the human body, including the respiratory system, blood system, circulatory system, nervous system, liver, and kidneys. It has many test items and is mainly used in critical care medicine. It has great limitations in actual outpatient and emergency departments. For patient infections, blood culture is used to detect pathogens. However, the culture cycle is long, requiring 24-48 hours, and false negatives are very likely to occur after the patient is treated with antibiotics.

[0003] Even though existing diagnostic criteria quantify the severity of sepsis-related organ damage, rapid and accurate identification and diagnosis of sepsis remains a pressing challenge for the medical community. With the rapid development of technologies such as genomics and proteomics, the use of biomarkers for auxiliary diagnosis has become increasingly common. The 2020 Chinese Expert Consensus on Early Prevention and Intervention of Sepsis in Emergency Medicine proposed the use of cell-free plasma (CRP), procalcitonin (PCT), interleukin-6 (IL-6), amyloid-beta (SAA), and heparin-binding protein (HBP) for the prevention and diagnosis of sepsis. While the levels of these markers in the human body provide valuable insights into the infection status and prognosis of patients with sepsis, due to the complex immune response and pathological conditions of sepsis, these markers cannot accurately identify the source of the disordered response in the patient. Confirmation of sepsis infection still requires blood culture and other physiological changes. Furthermore, these markers often require instruments such as biochemical analyzers or flow cytometry, which are expensive and require high technical skills, making them difficult to use in primary care hospitals with underdeveloped economies and infrastructure. Therefore, the use of appropriate biomarker combinations for early diagnosis and rapid identification of sepsis has broad application prospects and provides an effective solution for the course assessment and personalized diagnosis and treatment of sepsis patients.

[0004] Summary of the Invention

[0005] The present invention provides a combined detection kit for detecting sepsis and its application, which has high sensitivity and specificity for the diagnosis of sepsis, and can distinguish between bacterial infection, viral infection and sepsis, can significantly shorten the detection time, and reduce the detection cost.

[0006] In order to achieve the above object, the present invention provides the following technical solutions:

[0007] The application of a combination of at least two markers among CD14, CD95, HBP, HMGB-1, IFN-α, IFN-γ, IL-1β, IL-8, MIP-1β, MIF, PCT, TNF-α, sTREM-1, TLR-4, and TSP-1 in the detection of sepsis.

[0008] By testing relevant samples in the immune dimension, the application of early graded screening for sepsis can be achieved, and bacterial infection, viral infection and sepsis can be distinguished.

[0009] A second aspect of the present application provides a method for detecting sepsis, characterized by comprising the following steps:

[0010] (1) Detecting the concentration of at least two markers of CD14, CD95, HBP, HMGB-1, IFN-α, IFN-γ, IL-1β, IL-8, MIP-1β, MIF, PCT, TNF-α, sTREM-1, TLR-4, and TSP-1 in the sample;

[0011] (2) Performing logistic regression or support vector machine (SVM) analysis on the concentration of the markers in the measured samples to establish a calculation model.

[0012] Furthermore, the logistic regression equation is:

[0013] Among them, Logit(P) is the logistic regression model result of sepsis markers, A is the natural constant obtained by regression, β i is the coefficient of each marker obtained by regression analysis, i is the concentration of each marker, and n is an integer greater than or equal to 2.

[0014] The biomarker concentration of each sample tested is substituted into the regression equation to calculate the probability of sepsis for each sample. The Youden index of the point closest to the upper left corner of the ROC curve is used to determine the probability cut-off value. When the Logit (P) is greater than the above cut-off value, there is a risk of sepsis, which can remind doctors or patients that further examination and diagnosis are needed.

[0015] As a preferred embodiment, the sample in step (1) is human blood, urine, cerebrospinal fluid, feces, sputum, or tissue fluid.

[0016] As a preferred embodiment, the method for detecting the concentration of the marker in step (1) is at least one of: a radiometric method, an immunological method, a fluorescence method, a flow cytometry method, a latex turbidimetry method, a biochemical method, an enzymatic method, a hybridization method, a gas chromatography-mass spectrometry method, a liquid chromatography-mass spectrometry method, a nucleic acid mass spectrometry method, a chromatography method, a chemiluminescence method, a magnetoelectric method or a photoelectric conversion method.

[0017] In the third aspect of the present application, a combined detection kit for detecting sepsis is provided, comprising capture antibodies and detection antibodies for at least two markers selected from CD14, CD95, HBP, HMGB-1, IFN-α, IFN-γ, IL-1β, IL-8, MIP-1β, MIF, PCT, TNF-α, sTREM-1, TLR-4, and TSP-1.

[0018] A sample of the patient's blood, urine, cerebrospinal fluid, feces, sputum, or tissue fluid is added to the sample loading area of ​​the test strip card. Within a certain period of time, the reagent card is inserted into a dry fluorescent immunoassay analyzer for reading. The instrument displays the numerical value of the combined content of at least two of the above biomarkers. An algorithm is used to calculate the probability of sepsis based on the readout biomarker content and substitute it into a logistic regression equation.

[0019] The beneficial effects of the present invention are:

[0020] 1. The present invention uses clinical experimental testing of relevant samples to combine at least two biomarkers, including CD14, CD95, HBP, HMGB-1, IFN-α, IFN-γ, IL-1β, IL-8, MIP-1β, MIF, PCT, TNF-α, sTREM-1, TLR-4, and TSP-1, for the diagnosis of sepsis. In the immune dimension, by testing relevant samples such as blood, urine, cerebrospinal fluid, feces, sputum, or tissue fluid, and combining algorithms to establish a model, it achieves early diagnosis and screening of sepsis and can distinguish between bacterial infection, viral infection, and sepsis. It has good diagnostic results in both the systemic inflammatory response and compensatory anti-inflammatory response stages of sepsis; under conditions of 98% specificity, its diagnostic sensitivity can reach 98%, far higher than similar products on the market, filling an international gap. This detection system is the first reported internationally, and its diagnostic technology and performance have reached advanced levels both domestically and internationally.

[0021] 2. After collecting and testing patient samples, the present invention can quickly calculate the patient's disease course through an algorithm, which takes only 15-30 minutes. It can quickly assist and guide doctors to intervene in the patient's condition and improve the patient's cure rate.

[0022] 3. The present invention can quickly detect the concentration of markers by using a kit and a dry fluorescent immunoassay to achieve quantitative detection. Combined with the algorithm for rapid calculation, it can be applied to initial screening in outpatient and emergency departments to achieve low-cost sepsis screening. DETAILED DESCRIPTION

[0023] In order to help those skilled in the art better understand the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to examples. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work should fall within the scope of protection of the present invention.

[0024] The present invention discloses an application of a combination of at least two markers selected from CD14, CD95, HBP, HMGB-1, IFN-α, IFN-γ, IL-1β, IL-8, MIP-1β, MIF, PCT, TNF-α, sTREM-1, TLR-4, and TSP-1 in the detection of sepsis. By testing relevant samples such as blood, urine, cerebrospinal fluid, feces, sputum, or tissue fluid, the application of early graded screening for sepsis is achieved in the immune dimension, and can distinguish between common bacterial infections, viral infections, and sepsis.

[0025] The present invention discloses a method for detecting sepsis, which is characterized by comprising the following steps:

[0026] (1) Detecting the concentration of at least two markers of CD14, CD95, HBP, HMGB-1, IFN-α, IFN-γ, IL-1β, IL-8, MIP-1β, MIF, PCT, TNF-α, sTREM-1, TLR-4, and TSP-1 in the sample;

[0027] (2) Performing logistic regression or support vector machine (SVM) analysis on the concentration of the markers in the measured samples to establish a calculation model.

[0028] Among them, the logistic regression equation is:

[0029] Among them, Logit(P) is the logistic regression model result of sepsis markers, A is the natural constant obtained by regression, β i is the coefficient of each marker obtained by regression analysis, i is the concentration of each marker, and n is an integer greater than or equal to 2.

[0030] Logistic regression is a statistical method used to build classification models that can be used to predict the outcome of a binary classification problem. Its basic idea is to map the input variables to a binary output variable by combining them with a logistic function. This logistic function, called the sigmoid function, converts the input variable to a value between 0 and 1, representing the probability that the output variable will be 1.

[0031] The biomarker concentration of each sample tested is substituted into the logistic regression equation to calculate the probability of sepsis for each sample. The Youden index of the point closest to the upper left corner of the ROC curve is used to determine the probability cut-off value. When the Logit (P) is greater than the above cut-off value, there is a risk of sepsis, reminding the doctor or patient that further examination and diagnosis are needed.

[0032] A support vector machine (SVM) is a binary classification model whose basic idea is to find an optimal hyperplane in feature space that separates samples of different categories and maximizes the distance from the sample points closest to the hyperplane. These closest sample points are called support vectors. SVMs have good generalization and robustness in classification problems and can also transform linearly inseparable problems into linearly separable ones using kernel functions.

[0033] The sample used in the present invention is human blood, urine, cerebrospinal fluid, feces, sputum, or tissue fluid.

[0034] The present invention provides a method for detecting the concentration of the above-mentioned marker in a sample, which comprises at least one of a radiometric method, an immunological method, a fluorescence method, a flow cytometry method, a latex turbidimetry method, a biochemical method, an enzymatic method, a hybridization method, a gas chromatography-mass spectrometry method, a liquid chromatography-mass spectrometry method, a nucleic acid mass spectrometry method, a chromatography method, a chemiluminescence method, a magnetoelectric method, or a photoelectric conversion method.

[0035] The present invention also discloses a joint test kit for detecting sepsis, comprising an immunochromatographic test strip, the immunochromatographic test strip comprising a base plate, a sample pad, a nitrocellulose membrane and absorbent paper, the nitrocellulose membrane being attached to the base plate, one end of the nitrocellulose membrane being attached with an integrated pad of a biomarker combination capture antibody, the marker detection antibody being coupled to a time-resolved fluorescent microsphere, the other end of the immunonitrocellulose membrane being connected to the absorbent paper, and the immunonitrocellulose membrane being provided with a detection line coated with a capture antibody of the corresponding biomarker combination and a sheep anti-mouse antibody quality control line. The above-mentioned biomarker combination is at least two of CD14, CD95, HBP, HMGB-1, IFN-α, IFN-γ, IL-1β, IL-8, MIP-1β, MIF, PCT, TNF-α, sTREM-1, TLR-4, and TSP-1.

[0036] The patient's blood, urine, cerebrospinal fluid, feces, sputum, or tissue fluid is added to the sample addition area of ​​the test strip card. The reagent card is inserted into a dry fluorescent immunoassay analyzer for reading within a certain period of time. The instrument displays the numerical value of the combined content of at least two of the above biomarkers. When combined with an algorithm, the read biomarker content is substituted into the logistic regression equation to calculate the probability of sepsis.

[0037] Depending on the different methodologies, the components of the detection kits are slightly different and are prepared according to national industry standards or enterprise standards.

[0038] Example 1

[0039] The test samples were serum from healthy people and patients diagnosed with sepsis. The samples were selected with similar age (between 18 and 90 years old) and gender. The concentrations of the above 15 biomarkers in the samples were determined using flow cytometry. After reading the concentrations, logistic regression analysis was performed using Medcalc software / R language / Python programming to obtain the correlation coefficient and equation. The receiver operating curve (ROC) was plotted to obtain the area under the receiver operating curve (AUC), sensitivity, and specificity.

[0040] This embodiment is directed to a specific application of logistic regression analysis, and the following examples are given:

[0041] Example 1: ①②(CD14+CD95) combined regression equation: Logit(P)=-8.271+0.0029*i(CD14)+0.00576*i(CD95);

[0042] The AUC of the combined test of ①② (CD14+CD95) was 0.87, with a specificity of 93% and a sensitivity of 83%, which was higher than that of a single combination without combined test. See Table 1 for details.

[0043] Example 2: ①②③⑦⑧⑨⑩ Joint inspection combined regression equation: Logit(P)=-80.8+0.0072*i(①)+0.0015*i(②)-0.013*i(③)+0.009*i(⑦)+0.039*i(⑧)-0.64*i(⑨)+0.0226*i(⑩)+0.017*i -0.818*i +0.153*i +0.078*i +0.207*i

[0044] ①②③⑦⑧⑨⑩ The AUC of the combined test was 0.99, and the sensitivity was 98% at a specificity of 98%, which was higher than that of a single combination without combined test. See Table 1 for details.

[0045] Note: AUC is the area under the receiver operating curve (ROC). The closer the AUC is to 1, the better or more accurate the product's diagnostic performance.

[0046] Table 1

[0047] Example 2

[0048] The test samples were plasma from healthy individuals and patients diagnosed with sepsis. The samples were selected from individuals of comparable age (18-90 years) and gender. Flow cytometry was used to measure the concentrations of the 15 biomarkers mentioned above. After reading the concentrations, logistic regression analysis was performed using Medcalc software / R language / Python programming. Correlation coefficients, equations, and the resulting AUC, sensitivity, and specificity were obtained. The results are shown in Table 2.

[0049] This embodiment is directed to a specific application of logistic regression analysis, and the following examples are given:

[0050] Example 1: ①②③⑥ (CD14+CD95+HBP+IFN-γ) combined test regression equation: Logit(P)=-11.732+0.00048*i(CD14)+0.0026*i(CD95)-0.0113*i(HBP)+0.25*i(IFN-γ);

[0051] The AUC of the combined test of ①②③⑥ (CD14+CD95+HBP+IFN-γ) was 0.88, with a specificity of 93% and a sensitivity of 85%, which was higher than that of a single combination without combined test. See Table 2 for details.

[0052] Example 2: ①②③⑦⑧⑨⑩ Joint inspection combined regression equation: Logit(P)=-68.7+0.0092*i(①)+0.0032*i②)-0.011*i(③)+0.015*i(⑦)+0.034*i(⑧)-0.59*i(⑨)+0.0563*i(⑩)+0.021*i -0.772*i +0.321*i +0.13*i +0.067*i

[0053] ①②③⑦⑧⑨⑩ The AUC of the combined test was 0.98, with a specificity of 97% and a sensitivity of 98%, which was higher than that of a single combination without combined test. See Table 2 for details.

[0054] Note: AUC is the area under the receiver operating curve (ROC). The closer the AUC is to 1, the better or more accurate the product's diagnostic performance.

[0055] Table 2

[0056] Example 3

[0057] The test samples were serum from patients with bacterial infections and confirmed sepsis. The samples were selected from individuals of comparable age (18-90 years) and gender. Flow cytometry was used to measure the concentrations of the 15 biomarkers mentioned above. After reading the concentrations, logistic regression analysis was performed using Medcalc software / R language / Python programming. Correlation coefficients, equations, and the resulting AUC, sensitivity, and specificity were obtained. The results are shown in Table 3.

[0058] Example 1: (CD14+CD95+HBP+IFN-γ) combined test regression equation: Logit(P)=2.304+0.0129*i(CD14)+0.0826*i(CD95)+0.1587*i(HBP)-0.0521*i(IFN-γ);

[0059] The combined detection of ①②③⑥ (CD14+CD95+HBP+IFN-γ) had an AUC of 0.80, a sensitivity of 75% at a specificity of 83%, and was able to distinguish between sepsis and bacterial infection. See Table 3 for details.

[0060] Example 2: ①②③⑦⑧⑨⑩ Joint inspection combined regression equation: Logit(P)=10.4+0.019*i(①)+0.0332*i(②)+0.021*i(③)-0.035*i(⑦)+0.135*i(⑧)-0.802*i(⑨)+0.153*i(⑩)+0.021*i -0.636*i +0.515*i +0.342*i -0.127*i

[0061] ①②③⑦⑧⑨⑩ The combined test had an AUC of 0.92, a sensitivity of 88% at a specificity of 93%, and was able to distinguish sepsis from bacterial infection well. See Table 3 for details.

[0062] Note: AUC is the area under the receiver operating curve (ROC). The closer the AUC is to 1, the better or more accurate the product's diagnostic performance.

[0063] Table 3

[0064] Example 4

[0065] The test samples were serum from people with viral infections and confirmed sepsis. The samples were selected from individuals of comparable age (18-90 years) and gender. Flow cytometry was used to measure the concentrations of the 15 biomarkers mentioned above. After reading the concentrations, logistic regression analysis was performed using Medcalc software / R language / Python programming. Correlation coefficients, equations, and the resulting AUC, sensitivity, and specificity were obtained. The results are shown in Table 4.

[0066] This embodiment is directed to a specific application of logistic regression analysis, and the following examples are given:

[0067] Example 1: ①②③⑥ (CD14+CD95+HBP+IFN-γ) combined test regression equation: Logit(P)=12.304+ 0.0211*i(CD14)-0.134*i(CD95)+0.2218*i(HBP)-0.505*i(IFN-γ);

[0068] The AUC of the combined detection of ①②③⑥ (CD14+CD95) was 0.79, with a specificity of 83% and a sensitivity of 75%, which can distinguish between sepsis and viral infection. See Table 4 for details.

[0069] Example 2: ①②③⑦⑧⑨⑩ Joint inspection combined regression equation: Logit(P)=31.4+0.042*i(①)+0.0457*i(②)-0.0045*i(③)+0.027*i(⑦)+0.211*i(⑧)-0.312*i(⑨)+0.342*i(⑩)+0.067*i -0.481*i +0.562*i +0.067*i +0.198*i

[0070] ①②③⑦⑧⑨⑩ The combined test had an AUC of 0.93, a sensitivity of 89% at a specificity of 93%, and was able to distinguish sepsis from viral infection well. See Table 4 for details.

[0071] Note: AUC is the area under the receiver operating curve (ROC). The closer the AUC is to 1, the better or more accurate the product's diagnostic performance.

[0072] Table 4

[0073] The above description is only a preferred embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made by using the contents of the present invention description and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. The application of at least two markers selected from CD14, CD95, HBP, HMGB-1, IFN-α, IFN-γ, IL-1β, IL-8, MIP-1β, MIF, PCT, TNF-α, sTREM-1, TLR-4, and TSP-1 in the detection of sepsis.

2. A method for detecting sepsis, characterized in that: The following steps are involved: (1) detecting the concentration of at least two markers of CD14, CD95, HBP, HMGB-1, IFN-α, IFN-γ, IL-1β, IL-8, MIP-1β, MIF, PCT, TNF-α, sTREM-1, TLR-4, and TSP-1 in the sample; (2) Performing logistic regression or support vector machine (SVM) analysis on the concentration of the marker in the measured sample to establish a computational model.

3. A method for detecting sepsis according to claim 2, characterized in that: The logistic regression equation is: Among them, Logit(P) is the logistic regression model result of sepsis markers, A is the natural constant obtained by regression, β i is the coefficient of each marker obtained by regression analysis, i is the concentration of each marker, and n is an integer greater than or equal to 2.

4. A method for detecting sepsis according to claim 2, characterized in that: The sample in step (1) is human blood, urine, cerebrospinal fluid, feces, blood, urine, cerebrospinal fluid, feces, sputum, or tissue fluid or tissue fluid.

5. A method for detecting sepsis according to claim 2, characterized in that: The method for detecting the concentration of the marker in step (1) is at least one of: a radiation method, an immunological method, a fluorescence method, a flow cytometry, a latex turbidimetry, a biochemical method, an enzyme method, a hybridization method, a gas chromatography-mass spectrometry method, a liquid chromatography-mass spectrometry method, a nucleic acid mass spectrometry method, a chromatography method, a chemiluminescence method, a magnetoelectric method or a photoelectric conversion method.

6. A combined test kit for detecting sepsis, characterized in that: The joint detection kit comprises capture antibodies and detection antibodies for at least two markers of CD14, CD95, HBP, HMGB-1, IFN-α, IFN-γ, IL-1β, IL-8, MIP-1β, MIF, PCT, TNF-α, sTREM-1, TLR-4, and TSP-1.

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