A marker for predicting the prognosis risk of an acute coronary syndrome patient and a system for predicting the prognosis risk thereof

Abstract

The application belongs to the technical field of disease prognosis, and particularly relates to a marker for predicting the prognosis risk of an acute coronary syndrome patient and a system for predicting the prognosis risk. The application provides a marker for predicting the prognosis risk of an acute coronary syndrome patient, which comprises a biomarker and a chronic kidney disease index; the biomarker comprises NT-proBNP and RBP4; and the chronic kidney disease index is an eGFR index. The application also provides a system for predicting the prognosis risk of an acute coronary syndrome patient, which comprises a protein detection module, a chronic kidney disease index detection module, a prognosis score determination module and a prognosis risk evaluation module. The system for predicting the prognosis risk of the application effectively solves the technical problems that the existing GRACE score prognosis prediction method has low prediction accuracy in the East Asian population and has limited prediction value for long-term cardiovascular adverse event risks of more than 1 year.

Description

Technical Field

[0001] This application belongs to the field of disease prognosis technology, and in particular relates to a biomarker for predicting the prognostic risk of patients with acute coronary syndrome and a system for predicting prognostic risk. Background Technology

[0002] Acute coronary syndrome (ACS) is the leading cause of death worldwide and a major disease burden in many countries. Rapid risk assessment and timely intervention for ACS are crucial secondary prevention measures that can effectively prevent adverse cardiovascular events and reduce the disease burden and lost healthy life years associated with ACS. Therefore, rapid and accurate risk assessment is essential.

[0003] Currently, several prognostic risk assessment scores exist for patients with acute coronary syndrome (ACS), among which only the GRACE score is recommended by guidelines. The GRACE score can efficiently assess the risk of adverse cardiovascular events in ACS patients within 6 months to 1 year. However, the GRACE score is based on a Caucasian population, and its general applicability and performance in other ethnic groups, especially East Asians, are poor, often underestimating the risk of future adverse cardiovascular events. Furthermore, the GRACE score has limited predictive value for long-term adverse cardiovascular event risk beyond 1 year. These limitations of the traditional GRACE score have created challenges for the rapid coordination of medical resources for ACS patients in China and for the development of secondary prevention measures for ACS patients. Therefore, a simplified score suitable for long-term adverse cardiovascular event risk assessment in Chinese ACS patients urgently needs to be developed. Summary of the Invention

[0004] Based on the shortcomings of the traditional GRACE prognostic score, the purpose of this application is to solve the problems of versatility and accuracy of cardiovascular adverse event risk scores for patients with acute coronary syndrome. It aims to provide a simple score applicable to assessing the risk of cardiovascular adverse events in East Asians, especially Chinese patients with acute coronary syndrome, which can achieve rapid risk stratification and coordination of medical resources in different high-risk patient groups.

[0005] The first aspect of this application provides biomarkers for predicting the prognostic risk of patients with acute coronary syndrome, including biomarkers and indicators of chronic kidney disease;

[0006] The biomarkers include NT-proBNP and RBP4;

[0007] The chronic kidney disease indicator is the glomerular filtration rate (eGFR) index.

[0008] Preferably, the prognostic risks include all-cause mortality risk, angina risk, myocardial infarction risk, revascularization risk, heart failure risk, or stroke risk.

[0009] Preferably, it also includes: cardiovascular-related indicators; the cardiovascular-related indicators are the left ventricular ejection fraction (LVEF) index.

[0010] A second aspect of this application provides a system for predicting the prognostic risk of patients with acute coronary syndrome.

[0011] The system includes:

[0012] A protein detection module is used to detect NT-proBNP and RBP4 in protein samples from patients with acute coronary syndrome, and to determine the levels of NT-proBNP and RBP4 in the patients.

[0013] The chronic kidney disease index detection module is used to detect the eGFR index in patients with acute coronary syndrome and determine the eGFR value in the patient.

[0014] The prognostic score determination module is used to determine the prognostic score of patients with acute coronary syndrome based on the NT-proBNP content, the RBP4 content, the eGFR value, and a prognostic detection model; wherein, the prognostic detection model is:

[0015] Prognostic score = Z1(eGFR) + Z2(RBP4) + Z3(NT-proBNP);

[0016] The Z1(eGFR) is: if C eGFR Greater than 90 mL / min / 1.73 m 2 If C, then Z1 is 0. eGFR Less than or equal to 90 mL / min / 1.73 m 2 If Z1 is 1, then Z1 is 1;

[0017] The Z2(RBP4) is: if C RBP4 If the concentration is less than 38.18 μg / mL, then Z2 is 0; if C RBP4 If the concentration is greater than or equal to 38.18 μg / mL, then Z2 is 1;

[0018] The Z3(NT-proBNP) is: if C NT-proBNP If it is less than 450 ng / L, then Z3 is 0; if C NT-proBNP If the concentration is greater than or equal to 450 ng / L, then Z3 is 1;

[0019] Where n represents the number of prognostic biomarkers, Z1 represents the effective score of chronic kidney disease markers, Z2 represents the effective score of RBP4, Z3 represents the effective score of NT-proBNP, and C eGFR C represents the patient's glomerular filtration rate. RBP4The level of RBP4 in a patient, C NT-proBNP The level of NT-proBNP in a patient;

[0020] The prognostic risk assessment module is used to determine the prognostic risk of patients with acute coronary syndrome based on the prognostic score.

[0021] Specifically, the formula for calculating Z1(eGFR) is as follows:

[0022]

[0023] Specifically, the formula for calculating Z2(RBP4) is as follows:

[0024]

[0025] Specifically, the formula for calculating Z3(NT-proBNP) is as follows:

[0026]

[0027] Preferably, the protein detection module includes a kit for detecting NT-proBNP protein content and a kit for detecting RBP4 protein content.

[0028] Preferably, the kits for detecting NT-proBNP protein content and the kits for detecting RBP4 protein content are ELISA kits.

[0029] Preferably, the chronic kidney disease index detection module includes a formula for calculating the eGFR index (MDRD formula).

[0030] Preferably, the method by which the prognostic risk assessment module determines the prognostic risk of patients with acute coronary syndrome includes:

[0031] If the prognostic score is less than 2, the patient with acute coronary syndrome is considered to have a good prognosis.

[0032] A prognostic score greater than or equal to 2 indicates a poor prognosis for patients with acute coronary syndrome.

[0033] More specifically, the method by which the prognostic risk assessment module determines the prognostic risk of patients with acute coronary syndrome includes:

[0034] Patients with acute coronary syndrome (ACS) were classified as low-risk patients with a prognostic score of 0–1, and as high-risk patients with a prognostic score of 2–3. Kaplan-Meier survival analysis was used to compare the cumulative incidence of adverse cardiovascular events over six years between low-risk and high-risk patients. A log-rank test was used to confirm a significant survival difference between the two groups (p<0.05).

[0035] Specifically, adverse cardiovascular events include all-cause mortality, angina, myocardial infarction, revascularization, heart failure, or stroke.

[0036] Preferably, the system further includes:

[0037] The cardiovascular-related index detection module is used to detect the LVEF index in patients with acute coronary syndrome and determine the value of LVEF in the patient.

[0038] The prognostic score determination module is used to determine the prognostic score of patients with acute coronary syndrome based on the NT-proBNP content, the RBP4 content, the eGFR value, the LVEF value, and a prognostic detection model; wherein, the prognostic detection model is:

[0039] Prognostic score = Z1(eGFR) + Z2(RBP4) + Z3(NT-proBNP) + Z4(LVEF);

[0040] The Z1(eGFR) is: if C eGFR Greater than 90 mL / min / 1.73 m 2 If C, then Z1 is 0. eGFR Less than or equal to 90 mL / min / 1.73 m 2 If Z1 is 1, then Z1 is 1;

[0041] The Z2(RBP4) is: if C RBP4 If the concentration is less than 38.18 μg / mL, then Z2 is 0; if C RBP4 If the concentration is greater than or equal to 38.18 μg / mL, then Z2 is 1;

[0042] The Z3(NT-proBNP) is: if C NT-proBNP If it is less than 450 ng / L, then Z3 is 0; if C NT-proBNP If the concentration is greater than or equal to 450 ng / L, then Z3 is 1;

[0043] The Z4(LVEF) is: if C LVEF If it is greater than 55%, then Z4 is 0; if C LVEF If it is less than or equal to 55%, then Z4 is 1;

[0044] Where n represents the number of prognostic biomarkers, Z1 represents the effective score of chronic kidney disease markers, Z2 represents the effective score of RBP4, Z3 represents the effective score of NT-proBNP, Z4 represents the effective score of LVEF, and C eGFR C represents the patient's glomerular filtration rate. RBP4 The level of RBP4 in a patient, C NT-proBNP The level of NT-proBNP in a patient, C LVEF This represents the patient's left ventricular ejection fraction.

[0045] Specifically, the formula for calculating Z4(LVEF) is as follows:

[0046]

[0047] Preferably, the method by which the prognostic risk assessment module determines the prognostic risk of patients with acute coronary syndrome includes:

[0048] If the prognostic score is less than 2, the patient with acute coronary syndrome is considered to have a good prognosis.

[0049] A prognostic score greater than or equal to 2 indicates a poor prognosis for patients with acute coronary syndrome.

[0050] More specifically, the method by which the prognostic risk assessment module determines the prognostic risk of patients with acute coronary syndrome includes:

[0051] Patients with acute coronary syndrome were classified as low-risk if their prognostic score was less than 2, and as high-risk if their prognostic score was greater than or equal to 2. Kaplan-Meier survival analysis was used to compare the cumulative incidence of adverse cardiovascular events over six years between low-risk and high-risk patients. A log-rank test was used to confirm a significant survival difference between the two groups (p < 0.05).

[0052] The current guidelines recommend GRACE as the prognostic score for acute coronary syndrome, but it has many shortcomings, including: 1) GRACE is based on white Europeans, and its predictive accuracy and applicability are poor in black, yellow and especially East Asian populations; 2) It has poor general applicability in some high-risk groups, such as ethnic minorities.

[0053] Based on the shortcomings of the traditional GRACE prognostic score, the technical problems addressed in this application are: 1) to construct a simplified multi-marker score based on effective biomarkers in the Chinese population, which can effectively predict future adverse cardiovascular events in Chinese patients with acute coronary syndrome; 2) the effective biomarkers need to be commonly used indicators in routine blood biochemistry tests; 3) the constructed simplified score can effectively predict the risk of adverse cardiovascular events in various high-risk patients with acute coronary syndrome; 4) the constructed simplified score can also effectively predict the risk of specific adverse cardiovascular events; 5) the constructed simplified score has significantly improved prediction accuracy and versatility compared to the traditional GRACE score.

[0054] The purpose of this application is to address the issues of versatility and accuracy in cardiovascular adverse event risk scoring for acute coronary syndrome (ACS). It aims to provide a simplified scoring system suitable for assessing the risk of cardiovascular adverse events in Chinese patients with ACS, enabling rapid risk stratification and coordination of medical resources across different high-risk patient groups. Experimental results show that the biomarkers and system presented in this application have higher prognostic accuracy and greater versatility than the existing GRACE score in Chinese ACS patients. This system can more effectively assist Chinese clinicians in rapidly assessing the risk of long-term cardiovascular adverse events and quickly stratifying the risk of ACS in various high-risk types of patients. Attached Figure Description

[0055] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below.

[0056] Figure 1 This application demonstrates the principal component analysis results of the risk factor group provided in Example 1 of this application;

[0057] Figure 2 This application provides a ranking chart of the contribution of risk factor groups according to Embodiment 1.

[0058] Figure 3 The results of Example 1 of this application show that NT-proBNP, eGFR and RBP4 are significantly correlated with the cumulative probability of cardiovascular adverse events in acute coronary syndrome.

[0059] Figure 4 The results provided in Example 1 of this application show that NT-proBNP, eGFR, RBP4, and LVEF are significantly correlated with the cumulative probability of adverse cardiovascular events in acute coronary syndrome.

[0060] Figure 5 The restricted cubic spline curve of the first prognostic detection model provided in Embodiment 1 of this application is shown.

[0061] Figure 6 The restricted cubic spline curve of the second prognostic detection model provided in Embodiment 1 of this application is shown.

[0062] Figure 7 The first prognostic detection model provided in Example 1 of this application uses the log-rank test to determine a significant survival difference between the low-risk and high-risk patient groups;

[0063] Figure 8 The second prognostic detection model provided in Example 1 of this application uses the log-rank test to determine a significant survival difference between the low-risk and high-risk patient groups;

[0064] Figure 9 This application demonstrates the general applicability analysis results of the first prognostic detection model provided in Embodiment 2 of this application;

[0065] Figure 10 This application demonstrates the general applicability analysis results of the second prognostic detection model provided in Embodiment 2 of this application;

[0066] Figure 11 This shows the prediction effectiveness analysis results of the first prognostic detection model provided in Embodiment 2 of this application;

[0067] Figure 12 This document presents the prediction effectiveness analysis results of the second prognostic detection model provided in Embodiment 2 of this application;

[0068] Figure 13 This demonstrates the practical application value of the first prognostic detection model provided in Embodiment 2 of this application;

[0069] Figure 14 This demonstrates the practical application value of the second prognostic detection model provided in Embodiment 2 of this application. Detailed Implementation

[0070] This application provides a biomarker for predicting the prognostic risk of patients with acute coronary syndrome and a system for predicting prognostic risk, effectively solving the technical problem that the existing GRACE score prognostic prediction method has low accuracy in East Asian populations and limited predictive value for long-term cardiovascular adverse event risk greater than 1 year.

[0071] The technical solutions in the embodiments of this application will be clearly and completely described below. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0072] Acute coronary syndrome is a group of clinical syndromes based on the pathological basis of coronary atherosclerotic plaque rupture or invasion, followed by complete or incomplete occlusive thrombosis. It includes acute ST-segment elevation myocardial infarction, acute non-ST-segment elevation myocardial infarction, and unstable angina (UA).

[0073] Adverse cardiovascular events include all-cause mortality, angina, myocardial infarction, revascularization, heart failure, or stroke.

[0074] The following examples are based on the NT-proBNP cutoff values ​​recommended by the 2014 Chinese Heart Failure Guidelines and the NT-proBNP cutoff values ​​recommended by the 2016 ESC Guidelines. This application selects 450 ng / L as the cutoff value standard. If NT-proBNP ≥ 450 ng / L, the effective score of NT-proBNP is 1, otherwise it is 0.

[0075] The following examples are based on the normal LVEF cutoff values ​​specified in the 2021 European Society of Cardiology (ESC) / European Association for Thoracic and Cardiovascular Surgery (EACTS) guidelines for the management of valvular heart disease. In this application, 55% is selected as the cutoff value standard. If LVEF ≤ 55%, the effective score of LVEF is 1, and otherwise it is 0.

[0076] The following examples are based on the normal eGFR cutoff values ​​specified in the 2021 UK National Institute for Health and Care Excellence (NICE) guidelines for the assessment and management of chronic kidney disease (CKD). In this application, 90 mL / min / 1.73 m 2 As a threshold standard, if LVEF ≤ 90 mL / min / 1.73 m 2 If the score is 1, then the effective score for eGFR is 1; otherwise, it is 0.

[0077] In the following examples, RBP4 is used as a biomarker. The most widely used definition in this application is: when the RBP4 level is ≥ the median RBP4 level in the same population (38.18 μg / mL), the effective score of RBP4 is 1, and otherwise it is 0.

[0078] Example 1

[0079] This application provides methods for screening biomarkers to predict the prognostic risk of patients with acute coronary syndrome, including:

[0080] 1. A total of 826 patients with acute coronary syndrome were recruited from the emergency departments of tertiary hospitals in Xinjiang. Baseline plasma biochemical information, physiological function information, clinical information, and follow-up prognostic information of all patients were collected (see Table 1). As shown in Table 1, differences existed in LVEF, eGFR, NT-proBNP, and RBP4, suggesting their ability to predict adverse cardiovascular events. Patients who were more likely to experience cardiovascular events had higher baseline age, lower rates of antiplatelet drug use, higher rates of multivessel disease / left aortic disease, lower left ventricular ejection fraction and eGFR, and higher RBP4 and NT-proBNP.

[0081] Table 1:

[0082]

[0083]

[0084] 2. Screening for the importance of markers:

[0085] Patients were grouped according to "adverse outcomes" and "no adverse outcomes." Principal component analysis was performed on the baseline risk factor clusters of the Chinese acute coronary syndrome patient population, revealing significant changes in the baseline risk factor clusters of patients who would later experience adverse cardiovascular events. Characteristic ranking was performed based on biomarkers loadings, and the five biomarkers with the highest contribution were selected. A significance threshold of p < 0.05 was set. See [link to relevant documentation]. Figures 1-2 . Figure 1 Principal component analysis revealed significant differences in baseline risk factor groups among patients with and without adverse outcomes of acute coronary syndrome. Figure 2 The loading values ​​of the risk factors indicate that NT-proBNP, CKMB, eGFR, LVEF, and RBP4 are the five most influential biomarkers that lead to significant differences in risk factor groups.

[0086] 3. Screening for differences in biomarkers:

[0087] Among the five most important biomarkers selected above (NT-proBNP, CKMB, eGFR, LVEF, and RBP4), linear models were used to test the differences between groups, with a significance threshold of p < 0.05 (see Table 2). Biomarker combination 1 (NT-proBNP, eGFR, and RBP4), after adjusting for other traditional cardiovascular disease risk factors, was significantly associated with the future occurrence of adverse cardiovascular events. Biomarker combination 2 (NT-proBNP, eGFR, LVEF, and RBP4), after adjusting for other traditional cardiovascular disease risk factors, was also significantly associated with the future occurrence of adverse cardiovascular events. This suggests the feasibility of using them together to predict the occurrence of adverse cardiovascular events.

[0088] Table 2

[0089]

[0090] ＊ P value < 0.05

[0091] 3. Screening for predictive power of biomarkers:

[0092] Based on patient follow-up information, clinical information, survival time, and survival status were compiled. Components from the selected biomarker combination 1 and biomarker combination 2 were binary classified according to international authoritative guidelines to meet the needs of rapid clinical risk stratification. See Tables 3-4.

[0093] Table 3

[0094] markers Valid score NT-proBNP ≥ 450 ng / L 1 <![CDATA[eGFR≤90mL / min / 1.73m 2 ]]> 1 RBP4 ≥ 38.18 μg / mL 1

[0095] Table 4

[0096] markers Valid score NT-proBNP ≥ 450 ng / L 1 <![CDATA[eGFR≤90mL / min / 1.73m 2 ]]> 1 RBP4 ≥ 38.18 μg / mL 1 LVEF≤55% 1

[0097] 4. Kaplan-Meier survival analysis was used to further test the prognostic accuracy of biomarker combination 1 and biomarker combination 2, with a selection criterion of p < 0.05. The results are as follows: Figures 3-4 The results showed that all biomarkers in biomarker combination 1 and biomarker combination 2 were significantly associated with the cumulative probability of adverse cardiovascular events in acute coronary syndrome.

[0098] 5. Construction of prognostic detection model:

[0099] Based on differential screening, two prognostic models were obtained: biomarker combination 1 (NT-proBNP, eGFR, and RBP4) and biomarker combination 2 (NT-proBNP, eGFR, LVEF, and RBP4). A generalized additive model was used to construct the prognostic detection model, resulting in two models. The first prognostic detection model was constructed based on biomarker combination 1 (NT-proBNP, eGFR, LVEF, and RBP4), and the second prognostic detection model was also constructed based on biomarker combination 1 (NT-proBNP, eGFR, LVEF, and RBP4).

[0100] 5.1 The first prognostic detection model includes:

[0101] 5.1.1 First prognostic score = Z1(eGFR) + Z2(RBP4) + Z3(NT-proBNP);

[0102] Z1(eGFR) is: If C eGFR Greater than 90 mL / min / 1.73 m 2If C, then Z1 is 0. eGFR Less than or equal to 90 mL / min / 1.73 m 2 If Z1 is 1, then Z1 is 1;

[0103] Z2(RBP4) is: If C RBP4 If the concentration is less than 38.18 μg / mL, then Z2 is 0; if C RBP4 If the concentration is greater than or equal to 38.18 μg / mL, then Z2 is 1;

[0104] Z3(NT-proBNP) is: If C NT-proBNP If it is less than 450 ng / L, then Z3 is 0; if C NT-proBNP If the concentration is greater than or equal to 450 ng / L, then Z3 is 1;

[0105] Where n represents the number of prognostic biomarkers, Z1 represents the effective score of chronic kidney disease markers, Z2 represents the effective score of RBP4, Z3 represents the effective score of NT-proBNP, and C eGFR C represents the patient's glomerular filtration rate. RBP4 The level of RBP4 in a patient, C NT-proBNP This represents the patient's NT-proBNP level.

[0106] 5.1.2 Based on the first prognostic detection model for acute coronary syndrome obtained above, the prognostic score for each patient was calculated. The inflection point value (2) was found using the restricted cubic spline, see [reference needed]. Figure 5 Then, based on the inflection point value, the patient population was divided into low-risk patients (first prognostic model score <2) and high-risk patients (first prognostic model score ≥2), as shown in Table 5.

[0107] Table 5

[0108]

[0109]

[0110] Kaplan-Meier survival analysis was used to compare the cumulative incidence of adverse cardiovascular events over six years between low-risk and high-risk patients. The log-rank test was used to confirm a significant survival difference between the low-risk and high-risk patient groups (p<0.05). See [link to relevant documentation]. Figure 7 . Figure 7 The results suggest that high-risk patients with acute coronary syndrome (prognostic score: 2-3) had a significantly higher cumulative probability of adverse cardiovascular events than low-risk patients with acute coronary syndrome (prognostic score: 0-1), confirming that the prognostic score model can effectively assess the prognosis of acute coronary syndrome.

[0111] 5.2 The second prognostic detection model includes:

[0112] 5.2.1 Second prognostic score = Z1(eGFR) + Z2(RBP4) + Z3(NT-proBNP) + Z4(LVEF);

[0113] Z1(eGFR) is: If C eGFR Greater than 90 mL / min / 1.73 m 2 If C, then Z1 is 0. eGFR Less than or equal to 90 mL / min / 1.73 m 2 If Z1 is 1, then Z1 is 1;

[0114] Z2(RBP4) is: If C RBP4 If the concentration is less than 38.18 μg / mL, then Z2 is 0; if C RBP4 If the concentration is greater than or equal to 38.18 μg / mL, then Z2 is 1;

[0115] Z3(NT-proBNP) is: If C NT-proBNP If it is less than 450 ng / L, then Z3 is 0; if C NT-proBNP If the concentration is greater than or equal to 450 ng / L, then Z3 is 1;

[0116] Z4(LVEF) is: If C LVEF If it is greater than 55%, then Z4 is 0; if C LVEF If it is less than or equal to 55%, then Z4 is 1;

[0117] Where n represents the number of prognostic biomarkers, Z1 represents the effective score of chronic kidney disease markers, Z2 represents the effective score of RBP4, Z3 represents the effective score of NT-proBNP, Z4 represents the effective score of LVEF, and C eGFR C represents the patient's glomerular filtration rate. RBP4 The level of RBP4 in a patient, C NT-proBNP The level of NT-proBNP in a patient, C LVEF This represents the patient's left ventricular ejection fraction.

[0118] 5.2.2 Based on the second prognostic detection model for acute coronary syndrome obtained above, the prognostic score for each patient was calculated. The inflection point value (2) was found using the restricted cubic spline, see [reference needed]. Figure 6 Then, based on the inflection point value, the patient population was divided into low-risk patients (second prognostic model score <2) and high-risk patients (second prognostic model score ≥2), as shown in Table 6.

[0119] Table 6

[0120]

[0121]

[0122] Kaplan-Meier survival analysis was used to compare the cumulative incidence of adverse cardiovascular events over six years between low-risk and high-risk patients. The log-rank test was used to confirm a significant survival difference between the low-risk and high-risk patient groups (p<0.05). See Figure 8 . Figure 8 The results suggest that patients with high-risk acute coronary syndrome (prognostic score ≥2) had a significantly higher cumulative probability of adverse cardiovascular events than patients with low-risk acute coronary syndrome (prognostic score <2), confirming that the prognostic score model can effectively assess the prognosis of acute coronary syndrome.

[0123] Example 2

[0124] This application provides a utility analysis of the two prognostic detection models mentioned above, specifically including:

[0125] 1. The general applicability of the above-mentioned first prognostic detection model was tested in patients with different subtypes of acute coronary syndrome. The results are as follows: Figure 9 As shown in the figure. Specifically, the patients included: smokers (431 cases), male patients (702 cases), ethnic minority patients (182 cases), patients with multivascular disease / left aortic disease (481 cases), patients with early-onset acute coronary syndrome (361 cases), and patients with a history of coronary intervention (127 cases). This first prognostic model was found to be effective in predicting the risk of future adverse cardiovascular events in patients with different types of acute coronary syndrome (p<0.05).

[0126] Figure 9 The first column represents subgroups with different characteristics (e.g., smokers, males, ethnic minorities, etc.). The second column represents the proportion of adverse outcomes among those with high prognostic scores (e.g., 77 out of 308 with low scores experienced adverse outcomes, while 44 out of 123 with high scores experienced adverse outcomes). The third column represents the incidence of adverse outcomes. The fourth and fifth columns show that, compared to those with low scores, those with high scores have a higher risk of adverse outcomes among patients with this characteristic. The sixth column indicates significance. Figure 9 This indicates that the prognostic scores of the first prognostic detection model described above can effectively predict the occurrence of adverse outcomes in patients with different types of acute coronary syndrome.

[0127] 2. The general applicability of the above-mentioned second prognostic detection model was tested in patients with different subtypes of acute coronary syndrome. The results are as follows: Figure 10As shown in the figure. Specifically, the patients included: smokers (431 cases), male patients (702 cases), ethnic minority patients (182 cases), patients with multivascular disease / left aortic disease (481 cases), patients with early-onset acute coronary syndrome (361 cases), and patients with a history of coronary intervention (127 cases). Figure 10 This indicates that the aforementioned second prognostic scoring model can effectively predict the risk of future adverse cardiovascular events in patients with different types of acute coronary syndromes (p<0.05).

[0128] Figure 10 The first column represents subgroups with different characteristics (e.g., smokers, males, ethnic minorities, etc.). The second column represents the proportion of adverse outcomes among those with high prognostic scores within the population with these characteristics (e.g., 61 out of 259 people with low scores experienced adverse outcomes; 60 out of 172 people with high scores experienced adverse outcomes). The third column represents the incidence of adverse outcomes. The fourth and fifth columns show that, among patients with these characteristics, those with high scores have a higher risk of adverse outcomes compared to those with low scores. The sixth column indicates significance. Figure 10 This indicates that the prognostic scores of the second prognostic detection model described above can effectively predict the occurrence of adverse outcomes in different types of high-risk populations.

[0129] Further testing of the effectiveness of the first prognostic detection model in predicting the risk of specific cardiovascular events yielded the following results: Figure 11 As shown in the figure. Specifically, it can effectively predict the risk of future repeat coronary interventions and the risk of future endovascular reconstruction surgeries (p<0.05).

[0130] Similarly, the effectiveness of the second prognostic detection model in predicting the risk of specific cardiovascular events was further examined, and the results are as follows: Figure 12 As shown in the figure. Specifically, it can effectively predict the risk of all-cause mortality, the risk of future repeat coronary interventions, and the risk of future endovascular revascularization (p<0.05).

[0131] Example 3

[0132] This application provides an analysis of the clinical application value of the two prognostic detection models mentioned above, specifically including:

[0133] 1. In a patient population with acute coronary syndrome, multivariate Cox regression analysis was used to study the practical application value of the aforementioned first prognostic detection model. After excluding confounding interferences from adjusted demographic characteristics, medical history, medication history, and biochemical indicators, the prognostic score model still effectively predicted the prognosis of acute coronary syndrome in the complex clinical setting (p<0.05). The results are as follows: Figure 13 As shown.

[0134] Figure 13The blue text represents Model 1. In Model 1, this application adjusted for demographic characteristics in the model for calculating the first prognostic score to predict future adverse outcomes. The results show that compared to individuals with low scores (HR=1), those with high scores have a significantly increased risk of adverse outcomes (the entire HR line, including the confidence interval, is to the right of 1, indicating a significantly increased risk of cardiovascular adverse events). This confirms that the prognostic score can effectively predict the risk of adverse outcomes and is not affected by demographic factors. The yellow text represents Model 2. In Model 2, this application adjusted for demographic characteristics, medical history, and medication history in the model for calculating the first prognostic score to predict future adverse outcomes. The interpretation of the results is the same, confirming that the first prognostic score can effectively predict the risk of adverse outcomes and is not affected by demographic factors, medical history, or medication history. Red represents model 3. In model 3, this application corrected for demographic characteristics, medical history, medication history, and biochemical indicators in the model for calculating the first prognostic score to predict future adverse outcomes. The results were interpreted in the same way, confirming that the first prognostic score can effectively predict the risk of adverse outcomes and is not affected by demographic characteristics, medical history, medication history, and biochemical indicators.

[0135] Compared with the existing risk score GRACE, the first prognostic score model proposed in this application has higher and more significant predictive precision and accuracy, indicating the clinical application value of the first prognostic score model. See Table 7.

[0136] Table 7

[0137]

[0138] In Table 7, delta C-index represents prediction accuracy, demonstrating that the prediction accuracy of the first prognostic score is significantly improved by 2.50% compared to GRACE (as a reference group, REF). IDI represents the Overall Discriminant Improvement Index, indicating that the model with the first prognostic score has a certain improvement in overall prediction performance (successful and unsuccessful predictions) compared to the GRACE score. NRI represents the Net Reclassification Improvement Index, indicating that the prediction model with the first prognostic score has significantly improved accuracy and precision compared to GRACE.

[0139] Following the methods described above, multivariate Cox regression analysis was used to study the practical application value of the second prognostic model in patients with acute coronary syndrome. After excluding confounding interferences from adjusted demographic characteristics, medical history, medication history, and biochemical indicators, the prognostic score model still effectively predicted the prognosis of acute coronary syndrome in the complex clinical setting (p<0.05). Figure 14 As stated above.

[0140] Figure 14 The blue text represents Model 1. In Model 1, this application adjusted for demographic characteristics in the model for calculating the second prognostic score to predict future adverse outcomes. The results show that compared to individuals with low scores (HR=1), those with high scores have a significantly increased risk of adverse outcomes (the entire HR line, including the confidence interval, is to the right of 1, indicating a significantly increased risk of cardiovascular adverse events). This confirms that the prognostic score can effectively predict the risk of adverse outcomes and is not affected by demographic factors. The yellow text represents Model 2. In Model 2, this application adjusted for demographic characteristics, medical history, and medication history in the model for calculating the second prognostic score to predict future adverse outcomes. The interpretation of the results is the same, confirming that the second prognostic score can effectively predict the risk of adverse outcomes and is not affected by demographic factors, medical history, or medication history. Red represents model 3. In model 3, this application corrected for demographic characteristics, medical history, medication history, and biochemical indicators in the model for calculating the second prognostic score to predict future adverse outcomes. The results were interpreted in the same way, confirming that the second prognostic score can effectively predict the risk of adverse outcomes and is not affected by demographic characteristics, medical history, medication history, and biochemical indicators.

[0141] Compared with the existing risk score GRACE, the second prognostic detection model proposed in this application has higher and more significant predictive precision and accuracy, indicating the clinical application value of the prognostic score model. See Table 8.

[0142] Table 8

[0143]

[0144] In Table 8, delta C-index represents prediction accuracy, demonstrating that the second prognostic score significantly improved prediction accuracy by 4.85% compared to GRACE (as a reference group, REF). IDI represents the Overall Discriminant Improvement Index, indicating that the model with the second prognostic score significantly improved overall prediction performance (successful and unsuccessful predictions) compared to the GRACE score. NRI represents the Net Reclassification Improvement Index, indicating that the second prognostic score showed significant improvements in both prediction accuracy and precision compared to GRACE.

[0145] In summary, this application's embodiments utilize principal component analysis based on the baseline risk factor spectrum of Chinese patients with acute coronary syndrome, prioritizing the occurrence of future adverse cardiovascular events. Multiple linear regression is then used to screen variables based on loading values ​​to identify effective biomarkers. After selecting biomarkers through binary classification according to guideline-defined thresholds, the scores are summed to form either a first prognostic score model with a score range of 0–3 or a second prognostic score model with a score range of 0–4. The prognostic score is then binary-classified based on the median to improve its ease of use. The general applicability of the prognostic score is validated in different acute coronary syndrome subtypes, ethnic minorities, and high-risk populations.

[0146] The above description is only a preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. Use of a reagent for detecting a combination of biomarkers in the preparation of a system for predicting the prognostic risk of patients with acute coronary syndrome, characterized in that, The biomarker combination is NT-proBNP, RBP4, and the glomerular filtration rate (eGFR) index.

2. The use of the reagent for detecting the combination of biomarkers according to claim 1 in the preparation of a system for predicting the prognostic risk of patients with acute coronary syndrome, characterized in that, The prognostic risks include all-cause mortality risk, angina risk, myocardial infarction risk, revascularization risk, heart failure risk, or stroke risk.

3. The use of the reagent for detecting the combination of biomarkers according to claim 1 in the preparation of a system for predicting the prognostic risk of patients with acute coronary syndrome, characterized in that, The biomarker combination also includes: cardiovascular-related indicators; the cardiovascular-related indicator is the left ventricular ejection fraction (LVEF) index.

4. The use of the reagent for detecting the combination of biomarkers according to claim 1 in the preparation of a system for predicting the prognostic risk of patients with acute coronary syndrome, characterized in that, The system includes: A protein detection module is used to detect NT-proBNP and RBP4 in plasma samples from patients with acute coronary syndrome, and to determine the levels of NT-proBNP and RBP4 in the patients. The chronic kidney disease index detection module is used to detect the eGFR index in patients with acute coronary syndrome and determine the eGFR value in the patient. The prognostic score determination module is used to determine the prognostic score of patients with acute coronary syndrome based on the NT-proBNP content, the RBP4 content, the eGFR value, and a prognostic detection model; wherein, the prognostic detection model is: Prognostic score = Z1 (eGFR) + Z2 (RBP4) + Z3 (NT-proBNP); The Z1 (eGFR) is: if C eGFR Greater than 90 mL / min / 1.73 m 2 If C, then Z1 is 0. eGFR Less than or equal to 90 mL / min / 1.73 m 2 If Z1 is 1, then Z1 is 1; The Z2 (RBP4) is: if C RBP4 If the concentration is less than 38.18 μg / mL, then Z2 is 0; if C RBP4 If the concentration is greater than or equal to 38.18 μg / mL, then Z2 is 1; The Z3 (NT-proBNP) is: if C NT-proBNP If it is less than 450 ng / L, then Z3 is 0; if C NT-proBNP If the concentration is greater than or equal to 450 ng / L, then Z3 is 1; Where n represents the number of prognostic biomarkers, Z1 represents the effective score of chronic kidney disease markers, Z2 represents the effective score of RBP4, Z3 represents the effective score of NT-proBNP, and C eGFR C represents the patient's glomerular filtration rate. RBP4 The level of RBP4 in a patient, C NT-proBNP The level of NT-proBNP in a patient; The prognostic risk assessment module is used to determine the prognostic risk of patients with acute coronary syndrome based on the prognostic score.

5. The use of the reagent for detecting the combination of biomarkers according to claim 4 in the preparation of a system for predicting the prognostic risk of patients with acute coronary syndrome, characterized in that, The protein detection module includes a kit for detecting NT-proBNP protein content and a kit for detecting RBP4 protein content.

6. The use of the reagent for detecting the combination of biomarkers according to claim 5 in the preparation of a system for predicting the prognostic risk of patients with acute coronary syndrome, characterized in that, The kit is selected from ELISA kits.

7. The use of the reagent for detecting the combination of biomarkers according to claim 4 in the preparation of a system for predicting the prognostic risk of patients with acute coronary syndrome, characterized in that, The chronic kidney disease index detection module includes a calculation formula for measuring the eGFR index.

8. The use of the reagent for detecting the combination of biomarkers according to claim 4 in the preparation of a system for predicting the prognostic risk of patients with acute coronary syndrome, characterized in that, The prognostic risk assessment module uses the following methods to determine the prognostic risk of patients with acute coronary syndrome: If the prognostic score is less than 2, the patient with acute coronary syndrome is considered to have a good prognosis. A prognostic score greater than or equal to 2 indicates a poor prognosis for patients with acute coronary syndrome.

9. The use of the reagent for detecting the combination of biomarkers according to claim 4 in the preparation of a system for predicting the prognostic risk of patients with acute coronary syndrome, characterized in that, The system also includes: The cardiovascular-related index detection module is used to detect the LVEF index in patients with acute coronary syndrome and determine the value of LVEF in the patient. The prognostic score determination module is used to determine the prognostic score of patients with acute coronary syndrome based on the NT-proBNP content, the RBP4 content, the eGFR value, the LVEF value, and a prognostic detection model; wherein, the prognostic detection model is: Prognostic score = Z1 (eGFR) + Z2 (RBP4) + Z3 (NT-proBNP) + Z4 (LVEF); The Z1 (eGFR) is: if C eGFR Greater than 90 mL / min / 1.73 m 2 If C, then Z1 is 0. eGFR Less than or equal to 90 mL / min / 1.73 m 2 If Z1 is 1, then Z1 is 1; The Z2 (RBP4) is: if C RBP4 If the concentration is less than 38.18 μg / mL, then Z2 is 0; if C RBP4 If the concentration is greater than or equal to 38.18 μg / mL, then Z2 is 1; The Z3 (NT-proBNP) is: if C NT-proBNP If it is less than 450 ng / L, then Z3 is 0; if C NT-proBNP If the concentration is greater than or equal to 450 ng / L, then Z3 is 1; The Z4 (LVEF) is: if C LVEF If it is greater than 55%, then Z4 is 0; if C LVEF If it is less than or equal to 55%, then Z4 is 1; Where n represents the number of prognostic biomarkers, Z1 represents the effective score of chronic kidney disease markers, Z2 represents the effective score of RBP4, Z3 represents the effective score of NT-proBNP, Z4 represents the effective score of LVEF, and C eGFR C represents the patient's glomerular filtration rate. RBP4 The level of RBP4 in a patient, C NT-proBNP The level of NT-proBNP in a patient, C LVEF This represents the patient's left ventricular ejection fraction.

10. The use of the reagent for detecting the combination of biomarkers according to claim 9 in the preparation of a system for predicting the prognostic risk of patients with acute coronary syndrome, characterized in that, The prognostic risk assessment module uses the following methods to determine the prognostic risk of patients with acute coronary syndrome: If the prognostic score is less than 2, the patient with acute coronary syndrome is considered to have a good prognosis. A prognostic score greater than or equal to 2 indicates a poor prognosis for patients with acute coronary syndrome.

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