CN116953261A - Combined detection kit for detecting sepsis and application thereof - Google Patents

Combined detection kit for detecting sepsis and application thereof Download PDF

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Publication number
CN116953261A
CN116953261A CN202311002647.3A CN202311002647A CN116953261A CN 116953261 A CN116953261 A CN 116953261A CN 202311002647 A CN202311002647 A CN 202311002647A CN 116953261 A CN116953261 A CN 116953261A
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sepsis
ifn
detecting
application
concentration
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张皓洋
徐爱华
吕莹
高鹏
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Zhejiang Gewuzhizhi Biotechnology Co ltd
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Zhejiang Gewuzhizhi Biotechnology Co ltd
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Abstract

The application relates to the technical field of biological medicines and discloses a joint inspection kit for detecting sepsis and application thereof, wherein the joint inspection kit discloses application of at least two markers in CD14, CD95, HBP, HMGB-1, IFN-alpha, IFN-gamma, IL-1 beta, IL-8, MIP-1 beta, MIF, PCT, TNF-alpha, sTREM-1, TLR-4 and TSP-1 in the detection of sepsis, and a model is established by detecting blood, urine, cerebrospinal fluid, feces, sputum or tissue fluid and other related samples and combining algorithms, so that early diagnosis and screening of sepsis are realized, and bacterial infection, viral infection and sepsis can be distinguished. The kit has good diagnosis results in the stage of sepsis systemic inflammatory reaction and compensatory anti-inflammatory reaction; under the condition of specificity of 98%, the diagnosis sensitivity can reach 98%, which is far higher than that of similar products in the market, and fills the international blank.

Description

Combined detection kit for detecting sepsis and application thereof
Technical Field
The application relates to the technical field of biological medicines, in particular to a joint inspection kit for detecting sepsis and application thereof.
Background
Sepsis is a disease that severely threatens human health, according to related study statistics: about 3150 tens of thousands of people are affected with sepsis worldwide each year, of which about 1940 tens of thousands are severely sepsis patients, and finally 530 tens of thousands die. Many sepsis studies have been largely incorporated into relevant clinical data in developed countries, while in practice the incidence and mortality of sepsis in less developed or developing countries and regions is higher. In 2020, the ICU research report of 44 hospitals in China shows that the incidence rate of sepsis reaches 20.6%, the death rate of illness reaches 35.5%, and the death rate of severe sepsis reaches 50%. The concept and diagnostic criteria for sepsis have evolved since 1991, 30 years later, and the latest definition for sepsis 3.0, which is proposed in 2016, refers to a clinical syndrome in which a host has a runaway response to infection and has a life-threatening organ dysfunction, using the diagnostic criteria of "infection+SOFA.gtoreq.2". The SOFA score covers human body 6 large (organ) systems such as a respiratory system, a blood system, a circulatory system, a nervous system liver, a kidney and the like, has a plurality of detection projects, is mainly used for severe medical departments, and has larger limitation in actual outpatient service and emergency treatment; for infection of patients, blood culture is used for detecting pathogens, however, the culture period is long and needs 24-48 hours, and after the patients are treated by antibiotics, false negative is very easy to occur.
Even if the existing diagnosis standard quantifies the sepsis related organ injury standard, how to rapidly and accurately identify and diagnose sepsis is a problem that needs to be explored in the current medical community. Along with the vigorous development of technologies such as genomics, proteomics and the like, the use of biomarkers for assisting diagnosis of diseases has become increasingly common, and the acute group expert consensus for early prevention and blocking of sepsis in China in 2020 proposes the application of reactive protein (CRP), procalcitonin (PCT), interleukin (IL-6) and amyloid protein (SAA) to sepsis prevention and diagnosis of infection; although the content of the marker in the human body has certain reference significance for evaluating the infection state and prognosis of a patient with sepsis, the marker still can not accurately provide the source of the reaction disorder of the patient because of the complicated immune reaction state and pathological condition of the sepsis, and whether the sepsis infection occurs or not still needs to be confirmed by means of blood culture, other physiological changes and the like; secondly, the marker detection is usually carried out by using instruments such as a biochemical analyzer or a flow cytometer, and the equipment is expensive, has higher technical requirements on personnel and is difficult to be applied to primary hospitals with underdeveloped economy and infrastructure. Therefore, the application of the proper biomarker combination has wide application prospect for early diagnosis and rapid identification of sepsis, and provides an effective scheme for disease course evaluation and personalized diagnosis and treatment of sepsis patients.
Disclosure of Invention
The application provides a joint inspection kit for detecting sepsis and application thereof, which have higher sensitivity and specificity for the diagnosis of sepsis, can distinguish bacterial infection, viral infection and sepsis, can greatly shorten detection time and reduce detection cost.
In order to achieve the above object, the present application provides the following technical solutions:
use of at least two markers selected from the group consisting of CD14, CD95, HBP, HMGB-1, IFN- α, IFN- γ, IL-1β, IL-8, MIP-1β, MIF, PCT, TNF- α, sTREM-1, TLR-4, TSP-1 in combination for detecting sepsis.
The application of early stage screening of sepsis is realized by testing related samples in immune dimension, and bacterial infection, viral infection and sepsis are distinguished.
In a second aspect of the application, there is provided a method of detecting sepsis, comprising the steps of:
(1) Detecting the concentration of at least two markers in CD14, CD95, HBP, HMGB-1, IFN-alpha, IFN-gamma, IL-1β, IL-8, MIP-1β, MIF, PCT, TNF-alpha, sTREM-1, TLR-4, TSP-1 of the sample;
(2) And carrying out logistic regression or Support Vector Machine (SVM) analysis algorithm on the concentration of the marker in the measured sample, and establishing a calculation model.
Further, the logistic regression equation is:
wherein ,logistic regression model results as sepsis markers,/->Natural constant for regression, ++>Coefficients of each marker obtained for regression analysis, +.>For the concentration of each marker, +.>Is an integer greater than or equal to 2.
Substituting the detected biomarker concentration of each sample into a regression equation, calculating the probability of sepsis of each sample, determining a cut-off value (cut-off) of the probability through the about sign index of a point nearest to the upper left corner of the ROC curve, and reminding a doctor or a patient of further checking and diagnosing when the risk of sepsis exists when the Logit (P) is larger than the cut-off value.
As a preferred embodiment, the sample in step (1) is human blood, urine, cerebrospinal fluid, stool, sputum, or interstitial fluid.
As a preferred embodiment, the concentration detection method of the marker in the step (1) is as follows: at least one of a radiation method, an immunization method, a fluorescence method, a flow fluorescence method, a latex turbidimetry method, a biochemical method, an enzymatic method, a hybridization method, a gas chromatography, a liquid chromatography, a nucleic acid mass spectrometry, a chromatography, a chemiluminescence method, a magneto-electric method or a photoelectric conversion method.
In a third aspect of the application, there is provided a joint test kit for detecting sepsis comprising a capture antibody and a detection antibody 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, TSP-1.
Adding samples such as blood, urine, cerebrospinal fluid, faeces, sputum or tissue fluid of a patient into a test paper card sample adding area, inserting a reagent card into a dry type fluorescence immunoassay analyzer for reading in a certain time, and displaying the numerical value of the combined content of the at least two biomarkers on the instrument; collocation algorithm calculates sepsis illness probability based on substituting the read biomarker content into logistic regression equation.
The beneficial effects of the application are as follows:
1. according to the application, related samples are detected through clinical experiments, at least two biomarkers of CD14, CD95, HBP, HMGB-1, IFN-alpha, IFN-gamma, IL-1 beta, IL-8, MIP-1 beta, MIF, PCT, TNF-alpha, sTREM-1, TLR-4 and TSP-1 are combined in application of sepsis diagnosis, and a model is established through detecting related samples such as blood, urine, cerebrospinal fluid, excrement, sputum or tissue fluid in an immune dimension and combining an algorithm, so that early diagnosis and screening of sepsis are realized, and bacterial infection, viral infection and sepsis can be distinguished. The kit has good diagnosis results in the stage of sepsis systemic inflammatory reaction and compensatory anti-inflammatory reaction; under the condition of specificity of 98%, the diagnosis sensitivity can reach 98%, which is far higher than that of similar products in the market, and fills the international blank.
2. After the patient samples are collected and sampled, the application can predict the patient course by rapid algorithm, the time is only 15-30min, and the application can rapidly assist doctors in guiding the intervention of the patient, and improves the cure rate of the patient.
3. The rapid detection of the marker concentration can realize quantitative detection only by a kit and a dry fluorescent immunoassay analyzer, and is combined with rapid algorithm calculation, so that the rapid detection kit is applied to primary screening of outpatient service and emergency treatment, and low-cost sepsis screening is realized.
Detailed Description
In order that those skilled in the art will better understand the present application, a technical solution of embodiments of the present application will be clearly and completely described in the following description with reference to examples, and it is apparent that the described embodiments are only some embodiments of the present application, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present application without making any inventive effort, shall fall within the scope of the present application.
The application discloses application of at least two markers in CD14, CD95, HBP, HMGB-1, IFN-alpha, IFN-gamma, IL-1 beta, IL-8, MIP-1 beta, MIF, PCT, TNF-alpha, sTREM-1, TLR-4 and TSP-1 in combination in detecting sepsis. The application of early stage screening of sepsis in immune dimension can be realized by detecting Guan Yangben such as blood, urine, cerebrospinal fluid, feces, sputum or interstitial fluid, and the like, and common bacterial infection, virus infection and sepsis can be distinguished.
The application discloses a method for detecting sepsis, which is characterized by comprising the following steps:
(1) Detecting the concentration of at least two markers in CD14, CD95, HBP, HMGB-1, IFN-alpha, IFN-gamma, IL-1β, IL-8, MIP-1β, MIF, PCT, TNF-alpha, sTREM-1, TLR-4, TSP-1 of the sample;
(2) And carrying out logistic regression or Support Vector Machine (SVM) analysis algorithm on the concentration of the marker in the measured sample, and establishing a calculation model.
Wherein, the logistic regression equation is:
wherein ,logistic regression model results as sepsis markers,/->Natural constant for regression, ++>Coefficients of each marker obtained for regression analysis, +.>For the concentration of each marker, +.>Is an integer greater than or equal to 2.
Logistic regression is a statistical method used to build classification models that can be used to predict the outcome of a classification problem. The basic idea is to map the input variable into a binary output variable by combining the input variable with a logic function. This logic function, called the sigmoid function, converts the input variable to a value between 0 and 1, representing the probability that the output variable is 1.
Substituting the biomarker concentration of each sample into a logistic regression equation, calculating the probability of sepsis of each sample, determining a cut-off value (cut-off) of the probability through the about sign index of a point nearest to the upper left corner of the ROC curve, and reminding a doctor or a patient of further checking and diagnosing when the Logit (P) is greater than the cut-off value.
The support vector machine (Support Vector Machine, SVM) is a two-class model whose basic idea is to find an optimal hyperplane in the feature space, so that the hyperplane can separate samples of different classes and so that the closest sample points to the hyperplane are at their greatest distance from the hyperplane, these closest sample points being called support vectors. The SVM has better generalization capability and robustness in classification problems, and meanwhile, the problem of linear inseparable can be converted into the problem of linear inseparable through a kernel function.
The sample used in the present application is human blood, urine, cerebrospinal fluid, feces, sputum, or interstitial fluid.
The concentration detection method for the marker in the detection sample comprises the following steps: at least one of a radiation method, an immunization method, a fluorescence method, a flow fluorescence method, a latex turbidimetry method, a biochemical method, an enzymatic method, a hybridization method, a gas chromatography, a liquid chromatography, a nucleic acid mass spectrometry, a chromatography, a chemiluminescence method, a magneto-electric method or a photoelectric conversion method.
The application also discloses a joint detection kit for detecting sepsis, which comprises an immunochromatographic test strip, wherein the immunochromatographic test strip comprises a bottom plate, a sample pad, a nitrocellulose membrane and absorbent paper, the nitrocellulose membrane is adhered to the bottom plate, one end of the nitrocellulose membrane is adhered with an integrated pad for capturing a biomarker combination antibody, the marker detection antibody is coupled with a time-resolved fluorescent microsphere, the other end of the immunonitrocellulose membrane is connected with the absorbent paper, and the immunonitrocellulose membrane is provided with a detection line for coating the capture antibody corresponding to the biomarker combination and a goat anti-mouse antibody quality control line. The biomarker combination is at least two of CD14, CD95, HBP, HMGB-1, IFN-alpha, IFN-gamma, IL-1 beta, IL-8, MIP-1 beta, MIF, PCT, TNF-alpha, sTREM-1, TLR-4 and TSP-1.
Adding blood, urine, cerebrospinal fluid, faeces, sputum or tissue fluid of a patient into a test paper card sample adding area, inserting a reagent card into a dry type fluorescence immunoassay analyzer for reading in a certain time, and displaying the numerical value of the combined content of the at least two biomarkers on the instrument; when an algorithm is matched, the sepsis illness probability is calculated based on the read biomarker content substituted into a logistic regression equation.
The components of the detection kit are slightly different according to different methodologies, and the detection kit is prepared according to national industry standards or enterprise standards.
Examples
The detection samples are serum of healthy people and sepsis diagnosis patients, the selected ages (between 18 and 90 years) and sexes of the samples are equivalent, the concentration of the 15 biomarkers in the samples is measured by using a flow fluorescence technology, after the concentration is read, logistic regression analysis is performed by using Medcalc software/R language/Python programming to obtain correlation coefficients and equations, and a subject operation curve (ROC) is drawn to obtain the area under the subject operation curve (AUC), sensitivity and specificity.
Specific applications of this embodiment for Logistic regression analysis are as follows:
example 1: (1) (2) (cd14+cd95) joint inspection combinatorial regression equation: logit (P) = -8.271 +0.0029 x i (CD 14) +0.00576 x i (CD 95);
(1) (2) (cd14+cd95) combined auc=0.87, sensitivity at 93% was 83% higher than the single unconjugated combination, see table 1 for details.
Example 2: (1) (2) (3) (7) (8) (9)) and ⑪ ⑫ ⑬ ⑭ ⑮ joint inspection combined regression equation: logit (P) = -80.8+0.0072 x i ((1)) +0.0015 x i ((2)) +0.013 x i ((3)) +0.009 x i ((7)) +0.039 x i ((8)) -0.64 x i ((9)) +0.0226 x i (ri) +0.017 x i (⑪) -0.818 x i (⑫) +0.153 x i (⑬) +0.078 x i (⑭) +0.207 x i (⑮);
(1) (2) (3) (7) (8) (9) the combination of the (⑪ ⑫ ⑬ ⑭ ⑮) assays showed auc=0.99, and a sensitivity of 98% at a specificity of 98% which is higher than the combination of the individual assays, as detailed in table 1.
Note that: AUC is the area under the subject operating curve (ROC), with a closer AUC to 1 indicating better or more accurate diagnostic performance of the product.
TABLE 1
Example 2
The detection samples are the plasma of healthy people and sepsis patients, the selected ages (between 18 and 90 years) and the sexes of the samples are equivalent, the concentration of the 15 biomarkers in the samples is measured by using a flow fluorescence technology, and after the concentration is read, logistic regression analysis is performed by using Medcalc software/R language/Python programming to obtain the correlation coefficient, equation and AUC, sensitivity and specificity obtained by analysis. The results are shown in Table 2:
the specific application of this embodiment to logistic regression analysis is as follows:
example 1: (1) (2) (3) (6) (cd14+cd95+hbp+ifn- γ) joint inspection combined regression equation: logit (P) = -11.732 +0.00048 x i (CD 14) +0.0026 x i (CD 95) -0.0113 x i (HBP) +0.25 x i (IFN-. Gamma.);
(1) (2) (3) (6) (cd14+cd95+hbp+ifn- γ) combined auc=0.88, sensitivity was 85% at a specificity of 93% over the single unconjugated combination, see table 2 for details.
Example 2: (1) (2) (3) (7) (8) (9)) and ⑪ ⑫ ⑬ ⑭ ⑮ joint inspection combined regression equation: logit (P) = -68.7+0.0092 ((1)) +0.0032 (2)) -0.011 ((3)) +0.015 ((7)) +0.034 ((8)) -0.59 ((9)) +0.0563 () +0.021 (⑪) -0.772 (⑫) +0.321 (⑬) +0.13 (⑭) +0.067 (⑮);
(1) (2) (3) (7) (8) (9) the combination of the (⑪ ⑫ ⑬ ⑭ ⑮) assays showed auc=0.98, a sensitivity of 98% at 97% specificity, which is higher than the combination of the single assays, as detailed in table 2.
Note that: AUC is the area under the subject operating curve (ROC), with a closer AUC to 1 indicating better or more accurate diagnostic performance of the product.
TABLE 2
Example 3
The detection samples are serum of bacterial infection groups and sepsis diagnosis patients, the selected ages (between 18 and 90 years) of the samples are equivalent to the sexes, the concentration of the 15 biomarkers in the samples is measured by using a flow fluorescence technology, and logic regression analysis is performed by using Medcalc software/R language/Python programming after the concentration is read, so that correlation coefficients, equations and AUC, sensitivity and specificity obtained by analysis are obtained. The results are shown in Table 3:
example 1: (cd14+cd95+hbp+ifn- γ) joint inspection combined regression equation: logit (P) = 2.304 +0.0129×i (CD 14) +0.0826×i (CD 95) +0.1587×i (HBP) -0.0521×i (IFN-. Gamma.);
(1) (2) (3) (6) (cd14+cd95+hbp+ifn- γ) combined auc=0.80, sensitivity at 83% specificity was 75% and sepsis and bacterial infection could be distinguished as detailed in table 3.
Example 2: (1) (2) (3) (7) (8) (9)) and ⑪ ⑫ ⑬ ⑭ ⑮ joint inspection combined regression equation: logic (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 (note) +0.021 i (⑪) -0.636 i (⑫) +0.515 i (⑬) +0.342 i (⑭) -0.127 i (⑮);
(1) (2) (3) (7) (8) (9) the combined auc=0.92 for the ⑪ ⑫ ⑬ ⑭ ⑮ screening combination gave a sensitivity of 88% at 93% specificity, which better distinguished sepsis from bacterial infection as detailed in table 3.
Note that: AUC is the area under the subject operating curve (ROC), with a closer AUC to 1 indicating better or more accurate diagnostic performance of the product.
TABLE 3 Table 3
Example 4
The detection samples are serum of virus infected people and sepsis diagnosed patients, the selected ages (between 18 and 90 years) of the samples are equivalent to the sexes, the concentration of the 15 biomarkers in the samples is measured by using a flow fluorescence technology, and logic regression analysis is performed by using Medcalc software/R language/Python programming after the concentration is read, so that correlation coefficients, equations and AUC, sensitivity and specificity obtained by analysis are obtained. The results are shown in Table 4:
the specific application of this embodiment to logistic regression analysis is as follows:
example 1: (1) (2) (3) (6) (cd14+cd95+hbp+ifn- γ) joint inspection combined regression equation: logit (P) = 12.304 +0.0211×i (CD 14) -0.134×i (CD 95) +0.2218×i (HBP) -0.505×i (IFN-. Gamma.);
(1) (2) (3) (6) (cd14+cd95) combined auc=0.79, sensitivity at 83% specificity was 75% and sepsis and viral infection could be distinguished as detailed in table 4.
Example 2: (1) (2) (3) (7) (8) (9)) and ⑪ ⑫ ⑬ ⑭ ⑮ joint inspection combined regression equation: logit (P) =31.4+0.042 ((1)) +0.0457 ((2)) -0.0045 ((3)) +0.027 ((7)) +0.211 ((8)) -0.312 ((9)) +0.342 (panel) +0.067 (⑪) -0.481 (⑫) +0.562 (⑬) +0.067 (⑭) +0.198 (⑮);
(1) (2) (3) (7) (8) (9) the combined auc=0.93 for the ⑪ ⑫ ⑬ ⑭ ⑮ screening combination, with a sensitivity of 89% at 93% specificity, can better distinguish sepsis from viral infection, as detailed in table 4.
Note that: AUC is the area under the subject operating curve (ROC), with a closer AUC to 1 indicating better or more accurate diagnostic performance of the product.
TABLE 4 Table 4
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The foregoing description is only of the preferred embodiments of the present application and is not intended to limit the scope of the application, and all equivalent structures or equivalent processes using the descriptions and drawings of the present application or directly or indirectly applied to other related technical fields are included in the scope of the application.

Claims (6)

  1. Use 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, TSP-1 in combination for detecting sepsis.
  2. 2. A method of detecting sepsis, comprising the steps of:
    (1) Detecting the concentration of at least two markers in CD14, CD95, HBP, HMGB-1, IFN-alpha, IFN-gamma, IL-1β, IL-8, MIP-1β, MIF, PCT, TNF-alpha, sTREM-1, TLR-4, TSP-1 of the sample;
    (2) And carrying out logistic regression or Support Vector Machine (SVM) analysis algorithm on the concentration of the marker in the measured sample, and establishing a calculation model.
  3. 3. A method of detecting sepsis according to claim 2, wherein the logistic regression equation is:
    ；
    wherein ,logistic regression model results as sepsis markers,/->Natural constant for regression, ++>Coefficients of each marker obtained for regression analysis, +.>For the concentration of each marker, +.>Is an integer greater than or equal to 2.
  4. 4. A method according to claim 2, wherein the sample of step (1) is human blood, urine, cerebrospinal fluid, stool, sputum, or interstitial fluid.
  5. 5. A method according to claim 2, wherein the concentration of the marker in step (1) is detected by: at least one of a radiation method, an immunization method, a fluorescence method, a flow fluorescence method, a latex turbidimetry method, a biochemical method, an enzymatic method, a hybridization method, a gas chromatography, a liquid chromatography, a nucleic acid mass spectrometry, a chromatography, a chemiluminescence method, a magneto-electric method or a photoelectric conversion method.
  6. 6. A joint inspection kit for detecting sepsis, comprising a capture antibody and a detection antibody 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, TSP-1.
CN202311002647.3A 2023-08-10 2023-08-10 Combined detection kit for detecting sepsis and application thereof Pending CN116953261A (en)

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Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20060019256A1 (en) * 2003-06-09 2006-01-26 The Regents Of The University Of Michigan Compositions and methods for treating and diagnosing cancer
JP2012159356A (en) * 2011-01-31 2012-08-23 Mochida Pharmaceut Co Ltd Combined diagnostic marker for sepsis
US20190154704A1 (en) * 2016-03-24 2019-05-23 Mologic Limited Detecting sepsis
WO2022229444A2 (en) * 2021-04-30 2022-11-03 Roche Diagnostics Gmbh Pct marker panels for early detection of sepsis

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20060019256A1 (en) * 2003-06-09 2006-01-26 The Regents Of The University Of Michigan Compositions and methods for treating and diagnosing cancer
JP2012159356A (en) * 2011-01-31 2012-08-23 Mochida Pharmaceut Co Ltd Combined diagnostic marker for sepsis
US20190154704A1 (en) * 2016-03-24 2019-05-23 Mologic Limited Detecting sepsis
WO2022229444A2 (en) * 2021-04-30 2022-11-03 Roche Diagnostics Gmbh Pct marker panels for early detection of sepsis

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