Biomarker for death risk of myocardial infarction patient

Abstract

The invention discloses a biomarker for death risk of a myocardial infarction patient. The biomarker disclosed by the invention is a combination of NT-proBNP, ALT and BUN. Experiments prove that the BAB index constructed by the combination of NT-proBNP, ALT and BUN can effectively predict the death risk of a myocardial infarction patient, and has a wide clinical application prospect.

Description

Technical Field

[0001] The present invention belongs to the field of biomedicine and relates to biomarkers for the risk of death in patients with myocardial infarction. Background Art

[0002] ST-segment elevation myocardial infarction is a life-threatening cardiovascular disease with very high incidence and prevalence globally. Even after optimized medical treatment and successful revascularization, the disability rate and mortality of ST-segment elevation myocardial infarction remain prominent. Individual risk assessment and timely intervention may be the key to improving clinical prognosis. The use of biomarkers seems to be a simple, non-invasive and cost-effective tool.

[0003] The 2023 European Society of Cardiology guidelines recommend the GARCE risk score, which was initially developed as a risk assessment tool for in-hospital death, but it is mainly used for the selection of invasive strategies and reperfusion therapy in patients with non-ST-segment elevation myocardial infarction (NSTEMI). The CAMI-STEMI risk score is an effective tool for predicting mortality in the Chinese population of patients with ST-segment elevation myocardial infarction, but it is mainly based on vital signs and endpoint events.

[0004] The heart, liver, and kidneys are important organs of the human body, and their normal functions are fundamental to maintaining the stable operation of the body. The huge impact brought by ST-segment elevation myocardial infarction not only directly affects the cardiac structure and function, but also the secondary peripheral hypoperfusion caused by it will lead to impaired liver and kidney functions, threatening life.

[0005] Therefore, finding a method for quickly and non-invasively predicting the risk of death in patients with acute ST-segment elevation myocardial infarction is of great significance for early intervention and improving prognosis. Summary of the Invention

[0006] The present invention provides a biomarker combination for predicting the risk of death in patients with myocardial infarction, and the biomarker combination includes NT-proBNP, ALT, and BUN.

[0007] The present invention also provides the use of a reagent for detecting the content of the aforementioned biomarker combination in a sample in the preparation of a product for predicting the risk of death in patients with myocardial infarction.

[0008] Furthermore, the patients with myocardial infarction are patients with acute ST-segment elevation myocardial infarction.

[0009] The present invention also provides a product for predicting the risk of death in myocardial infarction, and the product includes a reagent for detecting the content of the aforementioned biomarker combination.

[0010] The present invention also provides a prediction model for the risk of myocardial infarction death. The prediction model is the BAB index, and the calculation formula of the BAB index is as follows:

[0011] BAB Index = Log 10 (NT-proBNP * ALT * BUN).

[0012] The measurement unit of NT-proBNP is ng / L, the measurement unit of ALT is U / L, and the measurement unit of BUN is mmol / L.

[0013] The present invention also provides a prediction device or a prediction system for the risk of myocardial infarction death. The device or system includes:

[0014] Data acquisition module: used to acquire the content data of the aforementioned biomarker combination in the sample of the subject to be tested;

[0015] Prediction module: used to provide the content data of the biomarker combination obtained by the data acquisition module as input data to a trained prediction model, and the prediction model is trained to predict the death risk of the subject based on the content data of the biomarker combination of the subject;

[0016] Prediction result output module: used to acquire the output result of the prediction model in the prediction module to obtain the death risk prediction result of the subject.

[0017] Furthermore, the prediction model is a Cox regression model;

[0018] Furthermore, the Cox regression model is a LASSO Cox regression model;

[0019] Even further, the prediction model is as described above.

[0020] The present invention also provides a computer device. The computer device includes a memory and a processor. The memory stores a program, and when the processor executes the program, the following method is implemented:

[0021] Acquire the content data of the aforementioned biomarker combination in the sample of the subject to be tested;

[0022] Provide the content data of the biomarker combination as input data to a trained prediction model;

[0023] Output the death risk prediction result of the subject to be tested;

[0024] Preferably, the prediction model is a Cox regression model;

[0025] More preferably, the Cox regression model is a LASSO Cox regression model;

[0026] Most preferably, the prediction model is as described above.

[0027] The present invention also provides a computer-readable storage medium, which includes a stored computer program;

[0028] Wherein, when the computer program runs, it controls the computer-readable storage medium to implement the method described above.

[0029] The present invention also provides any one of the following applications, which is characterized in that the application includes:

[0030] 1) The application of the biomarker combination described above in establishing a prediction model for the risk of death in patients with myocardial infarction;

[0031] 2) The application of the prediction model described above in predicting the risk of death in patients with myocardial infarction;

[0032] Preferably, the patients with myocardial infarction are patients with acute ST-segment elevation myocardial infarction.

[0033] The computer device of the present invention includes (but is not limited to) any personal computer, server and other terminals that can perform human-computer interaction with users through means such as keyboards, touchpads or voice control devices. The computing devices herein may also include mobile terminals, which include (but are not limited to) any electronic device that can perform human-computer interaction with users through means such as keyboards, touchpads or voice control devices, for example, tablet computers, smart phones, personal digital assistants (Personal Digital Assistant, PDA), smart wearable devices and other terminals. The network where the computing device is located includes (but is not limited to) the Internet, wide area network, metropolitan area network, local area network, virtual private network (Virtual Private Network, VPN).

[0034] Furthermore, the memory of the present invention includes non-volatile and / or volatile memory. The non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. The code of the operating system is stored thereon. For example, code or instructions are also stored on the memory, and by running these code or instructions, the risk scoring model for colorectal cancer prognosis prediction provided by the embodiments disclosed herein can be implemented. The volatile memory may include random access memory (RAM) or external cache memory.

[0035] Furthermore, the computer device of the present invention may include a processor, a memory, an external interface, a display, and an input device connected via a system bus. Among them, the processor is used to provide computing and control capabilities. The display of the computer device may be a liquid crystal display screen or an electronic ink display screen. The input device may be a touch layer covering the display screen, or may be, for example, a button, a trackball, or a touchpad provided on the casing of the computing device, or may also be an external keyboard, touchpad, or mouse, etc.

[0036] The processor may include one or more microprocessors and digital processors. The processor can call the program code stored in the memory to execute related functions. The processor is also called a central processing unit (CPU, Central Processing Unit), and may be a very large-scale integrated circuit, which is an operation core (Core) and a control core (Control Unit).

[0037] Methods for detecting the content or activity of NT-proBNP in the prior art include immunoassay, electrochemiluminescence immunoassay (ECLIA), enzyme-linked immunosorbent assay (ELISA), radioimmunoassay (RIA), immunofluorescence method, chemiluminescence immunoassay (CLIA), colloidal gold immunochromatography, and mass spectrometry.

[0038] Methods for detecting the content of ALT in the prior art include enzyme-linked immunosorbent assay (ELISA), mass spectrometry, immunoturbidimetry, immunofluorescence method, chemiluminescence immunoassay (CLIA), and radioimmunoassay (RIA).

[0039] Methods for detecting the activity of ALT in the prior art include colorimetry, Reitman-Frankel method (traditional method, measuring the activity of ALT through a colorimetric reaction), rate method (kinetic method): measuring the activity of ALT by monitoring the reaction rate, with high automation and accurate results; ultraviolet-visible spectrophotometry: measuring the activity of ALT by detecting the change in absorbance of the reaction product, with high sensitivity and accuracy; fluorescence method: using a fluorescently labeled substrate or product and measuring the activity of ALT by detecting the change in fluorescence intensity, with high sensitivity; chemiluminescence method: detecting the activity of ALT through a chemiluminescence reaction, with high sensitivity and suitable for micro-detection; electrochemical method: detecting the activity of ALT through an electrochemical sensor, fast and highly sensitive, suitable for point-of-care testing; mass spectrometry: with high sensitivity and specificity, but expensive equipment and complex operation, mostly used for research.

[0040] Methods for detecting BUN content in the prior art include enzymatic methods (urease-glutamate dehydrogenase method; diacetyl monoxime method (DAM method); o-phthalaldehyde method (OPA method); urease-indophenol method; colorimetric method; ultraviolet spectrophotometry; chemiluminescence method; electrochemistry method; isotope labeling method; mass spectrometry analysis method, etc.).

[0041] Compared with the prior art, the advantages and beneficial effects of the present invention are as follows:

[0042] The present invention provides a novel prediction model for the risk of death in patients with myocardial infarction, and the stable effectiveness of the model is confirmed by the validation dataset of patients with myocardial infarction. The model provides a reliable biomarker for the prognosis evaluation of patients with myocardial infarction, improves the ability to predict the prognosis of patients with myocardial infarction, can effectively identify patients with myocardial infarction with high-risk prognosis, and can be monitored early and intervened effectively in clinical practice, so as to reduce the incidence rate and mortality of poor prognosis of myocardial infarction, improve the prognosis of patients with myocardial infarction, and the model has broad application prospects in clinical practice. Brief Description of the Drawings

[0043] Figure 1 Graph showing the results of univariate and multivariate Cox regression analysis;

[0044] Figure 2 Graph showing the training and validation results of the BAB index and the 30-day all-cause death risk; wherein, A: ROC curve of the training set; B: ROC curve of the internal validation set; C: ROC curve of the external validation set; D: Kaplan-Meier analysis of the training set; E: Kaplan-Meier analysis of the internal validation set; F: Kaplan-Meier analysis of the external validation set; G: Comparison of the BAB index and the prediction ability of CAMI-STEMI; H: Comparison of the BAB index and the prediction ability of a single index;

[0045] Figure 3 Graph showing the validation results of the BAB index and the 1-year all-cause death risk; wherein, A: ROC curve of the training set; B: ROC curve of the internal validation set; C: ROC curve of the external validation set; D: Kaplan-Meier analysis of the training set; E: Kaplan-Meier analysis of the internal validation set; F: Kaplan-Meier analysis of the external validation set;

[0046] Figure 4 Graph showing the results of Cox regression analysis of the BAB index tertiles and the all-cause death risk in STEMI patients;

[0047] Figure 5Show the result graphs of survival analysis and restricted cubic splines for patients grouped by tertiles. Among them, A: 30-day survival analysis; B: 1-year survival analysis; C: Restricted cubic spline of BAB index and 1-month all-cause death; D: Restricted cubic spline of BAB index and 1-year all-cause death;

[0048] Figure 6 Show the result graphs of subgroup analysis and interaction analysis; among them, A: Predictive ability of BAB index in subgroups of age, gender, hypertension, diabetes, hyperlipidemia, liver insufficiency, kidney insufficiency, and heart insufficiency; B: Predictive ability of BAB index in different culprit vessel groups. Detailed implementation manners

[0049] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments. The following embodiments are only used to illustrate the present invention and are not used to limit the scope of the present invention. For experimental methods without specific conditions noted in the embodiments, they are usually carried out under conventional conditions, such as the conditions described in Sambrook et al., Molecular Cloning: A Laboratory Manual (New York: Cold Spring Harbor Laboratory Press, 1989), or the conditions recommended by the manufacturer.

[0050] Embodiment

[0051] 1. Data source

[0052] The data is sourced from the Coronary Heart Disease Special Disease Database of the Tianjin Health Medical Big Data Super Platform (hereinafter referred to as the "Platform"). The data provider, Tianjin Health Medical Big Data Co., Ltd., is authorized to be responsible for data collection, governance, and application of the Platform. The Platform has collected and aggregated clinical diagnosis and treatment data and public health system data from 43 tertiary hospitals and 39 secondary hospitals in Tianjin. After normalization and desensitization governance by the Platform, a scientific research application database that is interconnected and available is formed. The population included in the Coronary Heart Disease Special Disease Database is all the diagnosis and treatment information of patients who had at least one hospitalization during the period from January 1, 2010, to March 31, 2024, and the discharge diagnosis of this hospitalization included coronary heart disease. The information collected includes demographic characteristics, disease diagnosis information, medication and non-medication order information, examination and test information, surgical information, cost information, community medication and physical examination information, public health death information, etc.

[0053] The modeling set and the internal validation set belong to the data from the Coronary Heart Disease Special Disease Database from 2010 to 2021, and are randomly divided into a modeling set (5601 people) and an internal validation set (2401 people) at a ratio of 7:3.

[0054] The external validation set selects acute myocardial infarction patients (320 people) who visited the Second Hospital of Tianjin Medical University from 2022 to 2023.

[0055] 2. Experimental methods

[0056] 1) Data collection

[0057] General demographic data: including gender, age, ethnicity, clinical comorbidities (diabetes, hypertension, hyperlipidemia, etc.), KILLIP classification, etc.;

[0058] Laboratory indicators: blood routine, liver function, kidney function, blood lipids, NT-proBNP, etc.

[0059] Clinical medications: angiotensin-converting enzyme inhibitors; angiotensin receptor inhibitors; angiotensin receptor neprilysin inhibitors; β-blockers; calcium channel blockers.

[0060] 2) Inclusion and exclusion criteria

[0061] Inclusion criteria: age > 18 years; definite diagnosis of acute ST-segment elevation myocardial infarction; complete baseline data and follow-up data.

[0062] Exclusion criteria: complicated with severe heart, liver, and kidney diseases; complicated with malignant tumors; expected to undergo surgical operations, etc.

[0063] 3) Study endpoints

[0064] Primary study endpoint: all-cause death at 30 days.

[0065] Secondary study endpoint: all-cause death at 1 year.

[0066] 4) Statistical methods

[0067] Univariate Cox regression analysis was performed on each clinical variable, and variables with P < 0.05 were included in the multivariate Cox regression analysis to further determine the predictive value of NT-proBNP, ALT, and BUN for all-cause death at 1 month. Then, the calculation formula of the BAB index was constructed based on the corresponding regression coefficients and hazard ratios in the modeling set. The receiver operating characteristic (ROC) curve was used to evaluate the classification efficacy of the BAB index for the primary endpoint event. The cut-off value was calculated according to the Youden index. We will report the area under the curve (AUC), its corresponding 95% confidence interval (CI), and P value. The ROC curve was used to compare the predictive value of the BAB index with that of CAMI-STEMI and each index of NT-proBNP, ALT, and BUN, and the Delong test was used to compare the differences in AUC between the two. According to whether the BAB index was higher than the cut-off value of the training set, the population in the modeling set was divided into two groups, and the Kaplan-Meier survival curve was used to evaluate the differences in endpoint events between the two groups, and the log-rank test was used for comparison. The BAB index was verified in the internal and external validation cohorts through the ROC curve and Kaplan-Meier analysis.

[0068] Subsequently, the entire population was divided into three groups according to the tertiles of the BAB index. By adjusting different variables into the model, 3 different multivariate Cox models were used to test the association between the BAB index and the endpoint. The restricted cubic spline (RCS) model was used to analyze the non-linear correlation between the BAB index and the 1-month and 1-year death risks. Subgroup analysis and interaction tests were used to further evaluate the predictive role of the BAB index. At the same time, the interaction between the culprit vessel and the BAB index was evaluated. A P < 0.05 was considered statistically significant.

[0069] 3. Experimental Results

[0070] 1) NT-proBNP, ALT, and BUN are independent risk factors for predicting all-cause death at 1 month

[0071] As Figure 1 shown, univariate Cox regression analysis found that age, male, percutaneous coronary intervention (PCI) treatment, hypertension, diabetes, and hyperlipidemia history, ALT, AST, creatinine, BUN, NT-proBNP, triglyceride, hemoglobin, platelet, CCB, ACEI, antiplatelet drugs, and β-blockers were significantly correlated with all-cause mortality at 1 month (P < 0.05). Multivariate regression analysis showed that age, PCI treatment, diabetes history, ALT, BUN, NT-proBNP, use of ACEI, and β-blockers were independent risk factors affecting prognosis (P < 0.05). Among them, Figure 1PCI: Percutaneous Coronary Intervention; ALT: Alanine Aminotransferase; AST: Aspartate Aminotransferase; BUN: Blood Urea Nitrogen; NT-proBNP: N-terminal Pro-Brain Natriuretic Peptide; ACEI: Angiotensin-Converting Enzyme Inhibitor; ARB: Angiotensin Receptor Blocker; ARNI: Angiotensin Receptor Neprilysin Inhibitor.

[0072] Table 1 Univariate Regression Results of NT-proBNP, ALT, and BUN

[0073]

[0074] Note: ALT: Alanine Aminotransferase; AST: Aspartate Aminotransferase; BUN: Blood Urea Nitrogen; HR: Hazard Ratio

[0075] Based on the HR values in Table 1, we constructed the calculation formula for the BAB index:

[0076] BAB index = Log 10 (NT-proBNP * ALT * BUN)

[0077] The measurement unit of NT-proBNP is ng / L, the measurement unit of ALT is U / L, and the measurement unit of BUN is mmol / L.

[0078] 2) Relationship between BAB index and all-cause death at 1 month

[0079] Cox regression analysis using this BAB index showed that in the training set, the BAB index was independently associated with a higher 1-month mortality rate (HR: 2.951, 95% CI 2.645 - 3.294, P < 0.001, Table 2). The AUC of the ROC curve was 0.804, the sensitivity was 0.733, and the specificity was 0.785 ( Figure 2 A). According to the Youden index, the optimal cut-off value of the BAB index for predicting 1-month all-cause death was 5.616. Compared with using NT-proBNP, ALT, or BUN alone, the predictive ability of the BAB index was significantly superior (P = 0.033, P < 0.001, P = 0.001, Figure 2 H). According to the cut-off value, patients were divided into a low-risk group (BAB index ≤ 5.616) and a high-risk group (BAB index > 5.616). Kaplan-Meier analysis found that the 1-month all-cause mortality rate of patients in the high-risk group was higher than that in the non-high-risk group (P < 0.01, Figure 2 D). The same conclusion was also obtained in the internal validation set and the external validation set.

[0080] The predictive ability of the BAB index and the CAMI-STEMI score for the 1-month all-cause mortality of patients in the training set was compared by the ROC curve. The AUC of CAMI-STEMI prediction was 0.794, the sensitivity was 0.881, and the specificity was 0.572. The BAB index was not inferior to CAMI-STEMI (0.804 vs 0.794, P = 0.641)( Figure 2 G). Incorporating the BAB index into CAMI-STEMI improved the AUC (0.829), decreased the sensitivity (0.761), but increased the specificity (0.761)( Figure 2 G).

[0081] Table 2 Univariate Cox analysis of the BAB index and endpoint variables

[0082]

[0083] 3) Relationship between the BAB index and 1-year all-cause mortality

[0084] In the training set, the BAB index was a significant predictor of 1-year all-cause mortality (HR: 2.756, 95% CI: 2.513 - 3.022, P < 0.001, Table 2). The AUC of the BAB index for predicting 1-year mortality was 0.794 (sensitivity: 0.670, specificity: 0.799)( Figure 3 A). The 1-year all-cause mortality was higher in the high-risk group (P < 0.01)( Figure 3 D). The same conclusion was also obtained in the internal validation set and the external validation set.

[0085] 4) Relationship between the BAB index grouped by tertiles and all-cause mortality

[0086] As Figure 4 known, Cox regression analysis showed that the BAB index with high tertiles was associated with a higher risk of 1-month all-cause mortality (HR = 2.839 (95% CI: 2.588 - 3.115)) and 1-year all-cause mortality (HR = 2.696 (95% CI: 2.496 - 2.912)).

[0087] Kaplan-Meier analysis also showed that higher BAB index tertiles were associated with a higher risk of death (Log-Rank test, P < 0.01, Figure 5 A and 5B). Restricted cubic splines found a linear correlation between higher BAB index tertiles and 1-month mortality (nonlinear P = 0.217, Figure 5 C) and 1-year mortality (nonlinear P = 0.478, Figure 5 D).

[0088] 5) Subgroup analysis

[0089] Subgroup analysis was performed according to age, sex, hypertension, diabetes, hyperlipidemia, hepatic insufficiency, renal insufficiency, and cardiac insufficiency ( Figure 6 A). In each subgroup, the high BAB index remained a predictor of high mortality. Interaction analysis suggested that the risk of all-cause death at 1 month was higher in younger and male patients than in older and female patients. The BAB index could predict the 1-month mortality regardless of the presence or absence of hypertension, diabetes, hyperlipidemia, hepatic insufficiency, renal insufficiency, or cardiac insufficiency (interaction P value > 0.05).

[0090] To further determine that the BAB index for predicting the risk of 1-month all-cause death is independent of the culprit vessel, we extracted 892 patients with detailed coronary angiography records from our cohort. Subgroup analysis showed that the BAB index was still associated with an increased risk of 1-month all-cause death, and there was no interaction between the BAB index and the culprit vessel in terms of the 1-month death risk ( Figure 6 B).

[0091] As shown in Table 3, although any combination of three laboratory indicators included in the regression analysis could predict 1-month all-cause death (P < 0.05), their ability to predict 1-month all-cause death (AUC) was less than that of the BAB index. As shown in Table 4, the predictive ability of triglyceride, hemoglobin, and platelet for 1-month all-cause death was very limited (AUC < 0.5), and after combining with indicators with stronger predictive ability such as NT-proBNP and urea nitrogen, the predictive ability was even weaker than that of a single indicator, and the AUC value decreased.

[0092] Table 3 Comparison of combinations of other laboratory-related indicators and the BAB index for 1-month all-cause death

[0093]

[0094]

[0095] Note: ALT: alanine aminotransferase; AST: aspartate aminotransferase; BUN: blood urea nitrogen; NT-proBNP: N-terminal pro-brain natriuretic peptide; TG: triglyceride; HB: hemoglobin; PLT: platelet.

[0096] Table 4 Predictive ability of single indicators for 1-month all-cause death

[0097]

[0098] Note: ALT: alanine aminotransferase; AST: aspartate aminotransferase; BUN: blood urea nitrogen; NT-proBNP: N-terminal pro-brain natriuretic peptide

[0099] In summary, based on a multicenter observational cohort of 8,002 STEMI patients, this study established a new BAB index using the biomarkers NT-proBNP, ALT, and BUN. The results of this study further showed that the BAB index is an independent predictor of the risk of all-cause death at 1 month and 1 year in STEMI patients. A higher BAB index was significantly associated with a higher risk of all-cause death at 1 month and 1 year. The BAB index is a new non-invasive, simple, and rapid indicator that can be used to predict the risk of death in patients with acute ST-segment elevation myocardial infarction.

[0100] Although the specific embodiments of the present invention have been described in detail, those skilled in the art will understand that various modifications and changes can be made to the details based on all the teachings that have been published, and such changes are within the scope of protection of the present invention. The entire scope of the present invention is given by the appended claims and any equivalents thereof.

Claims

1. A biomarker combination for predicting the risk of death in patients with myocardial infarction, characterized in that: The biomarker combination includes NT-proBNP, ALT, and BUN.

2. Use of a reagent for detecting the content of the biomarker combination described in claim 1 in a sample in the preparation of a product for predicting the risk of death in patients with myocardial infarction.

3. The use according to claim 2, characterized in that: The myocardial infarction patient is a patient with acute ST-segment elevation myocardial infarction.

4. A product for predicting the risk of death from myocardial infarction, characterized in that: The product includes a reagent for detecting the content of the biomarker combination according to claim 1.

5. A prediction model for the risk of death from myocardial infarction, characterized in that: The prediction model is the BAB index, and the calculation formula of the BAB index is as follows: BAB Index=Log 10 (NT-proBNP*ALT*BUN)。 6. A prediction device or prediction system for the risk of death from myocardial infarction, characterized in that: The device or system comprises: Data acquisition module: used to obtain the content data of the biomarker combination of claim 1 in the sample of the subject to be tested; Prediction module: used for providing the biomarker combination content data obtained by the data acquisition module as input data to a trained prediction model, wherein the prediction model is trained to predict the death risk of the subject based on the biomarker combination content data of the subject; Prediction result output module: used to obtain the output result of the prediction model in the prediction module to obtain the death risk prediction result of the subject.

7. The device or system according to claim 6, characterized in that: The prediction model is a Cox regression model; Preferably, the Cox regression model is a LASSOCox regression model; More preferably, the prediction model is as described in claim 5.

8. A computer device, characterized in that: The computer device includes a memory and a processor, the memory stores a program, and the processor implements the following method when executing the program: Obtaining data on the biomarker combination content of claim 1 in a sample of a test subject; Providing the biomarker combination content data as input data to a trained prediction model; Output the death risk prediction results of the tested subjects; Preferably, the prediction model is a Cox regression model; More preferably, the Cox regression model is a LASSOCox regression model; Most preferably, the prediction model is as described in claim 5.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored computer program; Wherein, when the computer program is running, it controls the computer-readable storage medium to implement the method described in claim 8.

10. Any of the following applications, characterized in that: The applications include: 1) Use of the biomarker combination of claim 1 in establishing a mortality risk prediction model for patients with myocardial infarction; 2) Use of the prediction model described in claim 5 in predicting the risk of death in patients with myocardial infarction; Preferably, the myocardial infarction patient is a patient with acute ST-segment elevation myocardial infarction.

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