Development and Validation of a Long-Term Mortality Prediction Model for Acute Coronary Syndrome Patients Based on Cardiopulmonary Exercise Testing

Development and Validation of a Long-Term Mortality Prediction Model for Acute Coronary Syndrome Patients Based on Cardiopulmonary Exercise Testing | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Development and Validation of a Long-Term Mortality Prediction Model for Acute Coronary Syndrome Patients Based on Cardiopulmonary Exercise Testing Yumei Jiang, Ting Shen, Cheng Shi, Dejie Li, Mengyi Zhan, Guanghe Li, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7592594/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 27 Dec, 2025 Read the published version in BMC Cardiovascular Disorders → Version 1 posted 11 You are reading this latest preprint version Abstract Background. Acute coronary syndrome (ACS) is a major global health burden with a high risk of adverse outcomes. Existing predictive models (e.g., GRACE) primarily rely on static indicators and focus on short-term prognosis, limiting their ability to comprehensively assess patient status and predict long-term mortality. To address the need for improved long-term risk prediction, this study developed and validated a long-term mortality prediction model for ACS patients based on cardiopulmonary exercise testing (CPET) and other clinical indicators. Methods . This study included ACS patients who were treated at Tongji Hospital in Shanghai from January 1, 2007, to December 31, 2018, according to the inclusion criteria. Demographic data, medical histories, CPET indicators, laboratory indicators, and other baseline data of all included patients were collected, and their mortality was followed up. All data sets were randomly divided into derivation and validation cohorts in a ratio of 7/3. Least absolute shrinkage and selection operator regression and Cox multivariate analysis were used to identify independent risk factors affecting ACS prognosis, and a risk prediction model was established using nomograms. Results . A total of 299 patients were included in this cohort (211 in the derivation cohort and 88 in the validation cohort), with an average age of 57.00 years, including 280 males (93.6%). The median follow-up time was 3821 days, and 46 cases (15.4%) reached the study endpoint. The derivation cohort identified four independent predictive factors: age, blood urea nitrogen (BUN), ejection fraction (EF), and heart rate reserve (HRR), and a Nomogram scoring model was constructed based on these factors. The C indexes values of the derivation and the validation cohorts were 0.83 (0.76, 0.89) and 0.72 (0.56, 0.88), respectively. Calibration curves indicated good consistency between model predictions and actual observations. Conclusions . A model established based on four CPET indicators—age, BUN, EF, and HRR—can effectively predict the long-term all-cause mortality risk of ACS, providing a new tool for the long-term management of ACS. Trial registration. Registry: Chinese Clinical Trial Registry; TRN: ChiCTR2100052199; Registration date: October 22, 2021. Risk prediction Mortality rate Acute coronary syndrome Figures Figure 1 Figure 2 Figure 3 Introduction Acute coronary syndrome (ACS), characterized by a sudden reduction in cardiac blood supply, encompasses ST-segment elevation myocardial infarction (STEMI), non-ST-segment elevation myocardial infarction (NSTEMI), and unstable angina. Globally, over 7 million individuals are diagnosed with ACS annually, imposing a substantial burden on global health. [ 1 ] [ 2 ] Despite a decline in ACS mortality over the past 60 years, the risk of adverse outcomes remains high for patients presenting with extensive myocardial damage. [ 3 ] Refined risk stratification may further improve survival rates. Several predictive models have been published internationally, such as the GRACE study. [ 3 , 4 ] This study developed a risk prediction tool that reliably forecasts the risk of death or myocardial infarction within 6 months, incorporating nine independent factors (including age, development or history of heart failure, peripheral vascular disease, systolic blood pressure, Killip class, initial serum creatinine concentration, initial elevation of cardiac markers, cardiac arrest at admission, and ST-segment deviation), with a C-index of 0.81. The predictive accuracy remains consistent across subgroups of acute coronary syndrome. Stuart J Pocock et al. [ 5 ] developed a model to reliably predict the two-year mortality risk after hospital discharge for ACS patients, identifying 17 independent mortality prediction factors. Jun Ke et al. [ 6 ] established a predictive model for in-hospital mortality risk in ACS patients, including independent predictive factors such as age, NSTEMI, Killip III, Killip IV, D-dimer levels, cardiac troponin I, creatine kinase, N-terminal pro-brain natriuretic peptide, high-density lipoprotein cholesterol, and statin use. The existing predictive models have the advantage of being easily obtainable and not limited to a single risk factor. However, most models utilize static indicators, which are insufficient to comprehensively reflect the overall condition of patients. Additionally, the follow-up periods in most studies are relatively short, making it difficult to accurately assess long-term prognosis. Therefore, the development of a novel predictive model for long-term mortality risk in ACS is particularly necessary. This study selected ACS patients from Shanghai Tongji Hospital as the target population for modeling and conducted cardiopulmonary exercise testing (CPET) to monitor respiratory and circulatory parameters during exercise, thereby obtaining comprehensive indicators of cardiopulmonary function. Research indicates that cardiopulmonary health provides additional prognostic value for mortality risk in patients with coronary artery disease, surpassing traditional cardiovascular risk factors. [ 7 ] Notably, the follow-up period of this study spans 16 years, which facilitates a comprehensive assessment of the long-term prognosis of ACS patients. By incorporating CPET indicators, this study proposes a long-term mortality prediction model for ACS, providing a basis for clinical ACS treatment and revealing potential intervention targets for improving prognosis. Method Sample Size Calculation The minimum sample size required for model development was calculated using the pmsampsize function in R software (version 3.6.1, R Foundation for Statistical Computing, Vienna, Austria). The new predictive model may include up to four candidate parameters. Based on the research by Henderson RA et al. [ 8 ] the 10-year all-cause mortality rate for patients with acute coronary syndrome (ACS) is 0.25. We aimed to establish a 10-year ACS long-term prognosis risk model, and the final calculated minimum sample size was 289. Data Sources and Processing: This study adhered to the TRIPOD reporting guidelines. Clinical data were sourced from ACS patients who visited Shanghai Tongji Hospital between January 1, 2007, and December 31, 2018, totaling 1,880 cases. Inclusion criteria were: (1) diagnosis confirmed according to the 2023 ESC criteria for ACS; (2) age between 18 and 80 years; (3) ability to complete cardiopulmonary exercise testing (CPET). Exclusion criteria included: (1) acute myocardial infarction within 2 days; (2) uncontrolled unstable angina; (3) uncontrolled arrhythmias or heart failure; (4) severe aortic stenosis and descending aortic aneurysm; (5) acute pulmonary embolism, deep vein thrombosis, endocarditis, pericarditis, myocarditis, aortic dissection, pulmonary edema; (6) thyrotoxicosis; (7) renal failure; (8) acute infections or acute febrile diseases; (9) other physical disabilities affecting the test (inability to perform exercise testing or safety concerns). Following these criteria, 1,581 patients were excluded, resulting in the inclusion of 299 ACS patients (see Fig. 1 ). Potential Predictive Variables Demographic characteristics included age, gender, waist circumference, height, weight, body mass index (BMI), smoking status, etc.; other relevant medical histories included hypertension, diabetes, stroke, etc. CPET indicators were collected by professionally trained therapists/technicians. The equipment used included the MasterScreen series pulmonary function testing system from CareFusion, Germany, the LABTECH EC-12s exercise testing system from Hungary, and the ergoselect 150 exercise bicycle from ergoline, Germany. Patient CPET data were obtained from the cardiac rehabilitation center: peak oxygen pulse, peak oxygen consumption, oxygen consumption at anaerobic threshold, resting heart rate, peak heart rate, heart rate reserve (HRR) [ 9 ] (HRR calculated as the difference between the maximum heart rate achieved and the supine resting heart rate), oxygen work efficiency, carbon dioxide ventilation equivalent slope, blood pressure changes during exercise (increase in systolic pressure, stable systolic pressure, decrease in systolic pressure), electrocardiogram changes during exercise (no arrhythmias and/or ST changes, arrhythmias and/or ST changes but not leading to test termination, arrhythmias and/or ST changes leading to test termination), etc.; auxiliary examinations included total cholesterol, N-terminal pro-brain natriuretic peptide, uric acid, blood urea nitrogen (BUN), high-sensitivity C-reactive protein, C-reactive protein, serum albumin to creatinine ratio, serum sodium, cystatin C, homocysteine, etc., echocardiography (performed by professionally trained echocardiographers using the EPI0 7C device from Philips Medical Systems), etc.; medication use included aspirin, clopidogrel, statins, etc. Study Design In this study, a total of 43 variables were incorporated as potential prognostic factors. Patients who succumbed during the follow-up period were categorized into the deceased group, while those who remained alive were assigned to the survivor group. The study employed an open-label design. To mitigate bias, the examiners, researchers responsible for collecting outcome data, data managers, and statisticians were blinded to the patients' identities. Outcomes The endpoint of this study was defined in accordance with the key clinical data elements and cardiovascular endpoint event definitions jointly released by the American College of Cardiology, the U.S. Food and Drug Administration, and the Cardiovascular Trial Standards Data Collection Plan. The endpoint of this study was all-cause mortality. Data from electronic medical records and follow-up outcomes were utilized to measure this endpoint. Follow-Up Since July 2022, follow-up assessments have been conducted every six months via telephone calls or reviews of electronic medical records to collect information on mortality over the preceding period, continuing until the patient's death or the termination of the follow-up study on June 30, 2023. The time of death was documented by the follow-up personnel. Incorrect phone numbers, non-responses, and refusals to continue follow-up were considered as loss to follow-up. Statistical Analysis Statistical analyses were performed using SPSS version 20.0 (IBM Corp, Armonk, NY, USA) and R software (version 3.6.1, R Foundation for Statistical Computing, Vienna, Austria). Quantitative data were tested for normal distribution. Normally distributed quantitative data were expressed as mean (standard deviation), with independent samples t-test used for comparisons between two groups and analysis of variance for comparisons among multiple groups. Non-normally distributed quantitative data were expressed as median (range), with the Mann-Whitney U test used for comparisons between groups. Categorical and ordinal data were expressed as frequency (percentage), with the χ2 test used for comparisons between groups. Additionally, for variables with missing values less than 20%, multiple imputation was conducted using the mice package in R. Modeling and Validation Data were randomly allocated into a derivation cohort and a validation cohort in a 7:3 ratio (Fig. 1 ). In the validation cohort, LASSO regression and Cox multivariate analysis were employed to identify independent risk factors influencing the prognosis of acute coronary syndrome (ACS). A nomogram was developed to construct a risk prediction model, and the C-index values were calculated for both the derivation and validation cohorts. Discrimination was assessed, and calibration curves were plotted to evaluate the calibration. Finally, patients were stratified into high-risk (greater than the average risk score) and low-risk (not greater than the average risk score) categories, and Kaplan-Meier (KM) curves were used for survival analysis. [ 10 ] Results Baseline Characteristics This cohort comprised 299 patients, with 211 in the derivation cohort and 88 in the validation cohort. The mean age was 57.0 years, and males constituted the majority (93.6%, n = 280). The median follow-up duration was 3,821 days, with a loss-to-follow-up rate of 8.7%. Compared to survivors, non-survivors exhibited the following characteristics:(i) Older age;(ii) Higher prevalence of diabetes and stroke history;(iii) Elevated N-terminal pro-B-type natriuretic peptide (NT-proBNP), reduced estimated glomerular filtration rate, increased blood urea nitrogen (BUN), decreased albumin, and elevated homocysteine levels;(iv) Lower ejection fraction (EF);(v) Inferior cardiopulmonary exercise testing (CPET) metrics, including reduced peak oxygen consumption (VO₂), lower oxygen uptake at anaerobic threshold, diminished peak oxygen pulse, decreased peak heart rate, attenuated heart rate recovery (HRR), impaired oxygen work efficiency, and elevated carbon dioxide ventilation equivalent slope (Table 1 ). Table 1 Characteristics of Patients with Acute Coronary Syndrome Characteristic Overall The survivor group The deceased group χ 2 / Z/t p (n = 299) (n = 253) (n = 46) Follow-up duration(days) 3821.00 [2767.50, 4626.00] 3852.00 [2772.00, 4686.00] 3524.00 [2546.25, 4414.50] -1.448 0.148 Age(years) 57.00 [52.00, 65.00] 56.00 [51.00, 63.00] 65.00 [57.00, 71.75] -4.897 < 0.001 Male (%) 280 (93.6) 239 (94.5) 41 (89.1) 1.074 0.300 Smoking(%) 229 (76.6) 195 (77.1) 34 (73.9) 0.217 0.641 Hypertension(%) 180 (60.2) 149 (58.9) 31 (67.4) 1.173 0.279 Diabetes(%) 74 (24.7) 57 (22.5) 17 (37.0) 4.350 0.037 Stroke (%) 21 (7.0) 14 (5.5) 7 (15.2) 4.205 0.040 Previous MI(%) 9 (3.0) 7 (2.8) 2 (4.3) 0.012 0.914 Infarct location(%) 2.156 0.827 Extensive anterior/Anterior wall 132 (44.1) 110 (43.5) 22 (47.8) Anteroseptal 17 (5.7) 14 (5.5) 3 (6.5) High lateral 9 (3.0) 9 (3.6) 0 (0.0) Right ventricle 32 (10.7) 28 (11.1) 4 (8.7) Inferior wall 104 (34.8) 88 (34.8) 16 (34.8) Other 5 (1.7) 4 (1.6) 1 (2.2) Killip class (%) 5.297 0.151 Class I 257 (86.0) 222 (87.7) 35 (76.1) Class II 32 (10.7) 24 (9.5) 8 (17.4) Class III 2 (0.7) 1 (0.4) 1 (2.2) Class IV 8 (2.7) 6 (2.4) 2 (4.3) Gensini score 46.00 [28.00, 80.00] 44.00 [26.00, 80.00] 52.00 [30.50, 77.50] -0.255 0.799 Waist circumference(cm) 93.00 [87.00, 97.00] 93.00 [88.00, 97.00] 93.50 [87.00, 98.00] -0.339 0.735 BMI (kg/m2) 24.80 [23.07, 26.94] 24.82 [23.10, 27.00] 24.50 [22.86, 26.75] -0.606 0.544 Total cholesterol(mmol/L) 4.66 [4.04, 5.38] 4.66 [4.03, 5.38] 4.74 [4.10, 5.32] -0.221 0.825 NT-proBNP (pg/ml) 241.00 [74.81, 1164.00] 229.00 [73.40, 997.50] 898.22 [122.00, 2338.00] -2.586 0.010 BNP(pg/ml) 107.00 [60.00, 214.50] 107.00 [60.00, 212.00] 100.15 [71.25, 226.50] -0.376 0.707 eGFR (mL/min/1.73m 2 ) 89.70 [76.25, 99.20] 91.10 [80.70, 100.10] 79.10 [66.60, 89.68] -3.876 < 0.001 BUN (mmol/l) 4.90 [3.80, 6.20] 4.80 [3.63, 5.90] 5.70 [4.85, 6.40] -3.075 0.002 Uric acid(umol/l) 341.00 [294.00, 404.00] 341.00 [293.00, 404.00] 347.50 [303.75, 401.75] -0.579 0.562 hs-CRP(mg/L) 9.10 [3.80, 23.10] 9.10 [4.00, 22.90] 7.60 [3.20, 31.95] -0.479 0.632 CRP(mg/L) 6.48 [3.00, 12.72] 6.00 [3.00, 13.00] 8.00 [4.25, 10.00] -0.630 0.529 Serum albumin(g/L) 37.42 (4.17) 37.64 (4.10) 36.25 (4.43) -2.094 0.037 Creatinine(umol/l) 80.00 [71.00, 91.00] 79.00 [71.00, 90.00] 84.00 [72.25, 98.75] -1.764 0.078 Albumin/creatinine(g/mmol) 0.47 [0.40, 0.53] 0.47 [0.41, 0.54] 0.46 [0.34, 0.51] -1.948 0.051 Serum sodium (mmol/L) 140.00 [138.00, 142.20] 140.00 [138.00, 142.20] 140.00 [138.00, 142.00] -0.095 0.925 Cystatin C (mg/L) 1.25 [1.08, 1.65] 1.25 [1.08, 1.63] 1.29 [1.09, 1.80] -0.945 0.345 Homocysteine(umol/L) 13.50 [11.00, 17.00] 13.00 [11.00, 16.60] 15.60 [12.25, 19.77] -3.217 0.001 White blood cell count (10^9/L) 9.90 [8.11, 12.10] 10.00 [8.00, 12.10] 9.55 [8.66, 11.87] -0.319 0.750 Hemoglobin(g/l) 140.00 [131.00, 150.00] 140.00 [131.00, 150.00] 140.50 [128.25, 147.75] -0.963 0.335 RDW (%) 12.90 [12.60, 13.40] 12.90 [12.60, 13.40] 13.10 [12.60, 13.30] -0.216 0.829 LVEF(%) 0.58 [0.51, 0.64] 0.58 [0.51, 0.65] 0.53 [0.47, 0.61] -2.390 0.017 Peak VO₂ (mL/ (kg·min) 15.00 [13.00, 17.10] 15.30 [13.30, 17.20] 13.35 [11.00, 15.60] -3.614 < 0.001 AT VO₂ (mL/ (kg·min) 10.70 [9.20, 12.00] 10.90 [9.40, 12.00] 9.60 [8.40, 11.83] -2.248 0.025 Peak VO₂/HR (ml/beat) 9.90 [8.20, 11.40] 10.00 [8.59, 11.60] 8.65 [6.73, 10.47] -2.969 0.003 HR rest (bpm) 73.00 [66.00, 80.00] 72.00 [66.00, 80.00] 73.50 [67.25, 79.75] -0.484 0.628 HR peak (bpm) 113.00 [103.00, 120.00] 113.00 [105.00, 120.00] 107.00 [96.50, 114.00] -2.678 0.007 HRR (bpm) 38.80 (12.92) 39.87 (12.73) 32.93 (12.53) -3.407 < 0.001 ΔVO₂/ΔWR (ml/min/watt) 7.80 [6.40, 9.00] 7.90 [6.50, 9.00] 7.20 [5.10, 8.70] -2.318 0.020 VE/VCO₂ slope 33.60 [30.50, 38.35] 33.50 [30.50, 37.20] 35.70 [30.68, 43.18] -2.172 0.030 Exercise BP change(%) 3.677 0.159 Systolic BP elevation 180 (60.2) 148 (58.5) 32 (69.6) Systolic BP flat 105 (35.1) 91 (36.0) 14 (30.4) Systolic BP decline 14 (4.7) 14 (5.5) 0 (0.0) Aspirin (%) 283 (94.6) 238 (94.1) 45 (97.8) 0.469 0.493 Clopidogrel (%) 293 (98.0) 249 (98.4) 44 (95.7) 0.435 0.510 Statins (%) 295 (98.7) 250 (98.8) 45 (97.8) 0.000 1.000 ACEI/ARB (%) 230 (76.9) 195 (77.1) 35 (76.1) 0.021 0.884 Beta-blockers (%) 223 (74.6) 192 (75.9) 31 (67.4) 1.483 0.223 Notes: BMI = body mass index; NT-proBNP: N-terminal pro-B-type Natriuretic Peptide; BNP: B-type Natriuretic Peptide; eGFR: Estimated Glomerular Filtration Rate; BUN: Blood Urea Nitrogen; hs-CRP: High-sensitivity C-reactive Protein; CRP: C-reactive Protein; RDW: Red blood cell Distribution Width; LVEF: Left Ventricular Ejection Fraction; PeakVO₂ = peak oxygen consumption; ATVO₂ = oxygen uptake at anaerobic threshold; HR: Heart Rate; PeakVO₂/HR = peak oxygen pulse; HR rest = resting heart rate; HR peak = peak heart rate; HRR = heart rate reserve; ΔVO₂/ΔWR = oxygen work efficiency; VE/VCO₂ slope = carbon dioxide ventilation equivalent slope; BP: Blood Pressure;ACEI: Angiotensin-Converting Enzyme Inhibitor; ARB: Angiotensin II Receptor Blocker. Risk Model Development and Validation The cohort was randomly divided via computer-generated random numbers, with 70% (n = 211) allocated to the derivation cohort and 30% (n = 88) to the validation cohort. No significant differences in baseline characteristics were observed between the two cohorts (Supplementary Table 1). Among 41 candidate variables screened by LASSO regression, seven variables (age, diabetes, stroke history, BUN, homocysteine, EF, HRR) were retained. Subsequent univariate and multivariate Cox regression analyses identified four independent predictors (P < 0.05): age, BUN, EF, and HRR (Supplementary Fig. 1; Table 2 ). Table 2 Analysis of Risk Event Indicators in Acute Coronary Syndrome Indicator Univariate Cox regression Analysis Multivariate Cox Regression Analysis HR(95%CI) P HR(95%CI) P Age 1.09 (1.05–1.13) < 0.001 1.06(1.02–1.10) 0.006 Diabetes 2.18 (1.05–4.52) 0.036 1.90(0.85–4.25) 0.119 History of Stroke 3.33 (1.43–7.78) 0.005 2.02(0.73–5.58) 0.174 BUN 1.29 (1.14–1.46) < 0.001 1.22(1.05–1.42) 0.008 Homocysteine 1.04 (1.01–1.06) 0.002 1.02(0.99–1.05) 0.245 EF 0.00 (0.00-0.05) < 0.001 0.01(0.00-0.34) 0.011 HRR 0.96 (0.93–0.98) 0.002 0.96(0.93–0.99) 0.010 Notes:HR: Hazard Ratio; CI: Confidence Interval; BUN: Blood Urea Nitrogen; EF: Ejection Fraction; HRR: Heart Rate Reserve. Multivariable Cox Regression and Nomogram Construction Based on the regression coefficients from the multivariable Cox analysis of the four predictors (age, BUN, EF, and HRR), a prognostic risk model was developed. These factors—independently associated with higher mortality risk—were integrated into a risk scoring system, visualized as a nomogram (Fig. 2 ). The ROC curve of the prediction model is shown in Supplementary Fig. 2. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) for the model in both derivation and validation cohorts are summarized in Supplementary Table 2. The model demonstrated moderate discriminative ability, with C-index values of 0.83 (95% CI: 0.76–0.89) in the derivation cohort and 0.72 (95% CI: 0.56–0.88) in the validation cohort. Calibration plots revealed close alignment between predicted and observed 5-, 10-, and 15-year clinical outcomes (Supplementary Fig. 3). Using nomogram-derived risk scores, the derivation cohort was stratified into high-risk and low-risk groups. Kaplan-Meier survival analysis with the Log-rank test showed statistically significant divergence (P < 0.05) in survival curves between risk groups across all cohorts (derivation, validation, and combined cohorts). In the derivation cohort, the high-risk group exhibited cumulative mortality rates of 13%, 53%, and 89% at 5, 10, and 15 years, respectively, compared to 6%, 38%, and 83% in the low-risk group (Fig. 3 ; Supplementary Fig. 4). Discussion In this study, we developed a novel prognostic model for acute coronary syndrome (ACS) patients by integrating concise parameters derived from clinical profiles, laboratory markers, cardiopulmonary exercise testing (CPET), and other measurements. The model demonstrated moderate predictive performance, with C-index values of 0.83 (95% CI: 0.76–0.89) in the derivation cohort and 0.72 (95% CI: 0.56–0.88) in the validation cohort. Calibration plots showed excellent agreement between predicted and observed 5-, 10-, and 15-year outcomes. Compared to traditional ACS risk models, our model offers distinct advantages: it requires only four readily available clinical variables (age, BUN, EF, and HRR), which simplifies data collection and implementation; incorporates CPET-derived HRR to enhance risk stratification by providing granular insights into cardiopulmonary functional capacity; and demonstrates robust long-term predictive stability through a median follow-up of 3,821 days (maximum 16 years). These features underscore its potential for widespread clinical adoption as a practical, simple, and efficient tool for long-term mortality risk evaluation, thereby supporting clinical decision-making and improving the accessibility of prognostic models. The model incorporates four predictors: age, BUN, EF, and HRR. HRR (heart rate reserve), which reflects cardiac sympathetic reserve, has demonstrated independent prognostic value in risk stratification for chronic coronary syndromes and heart failure [ 11 ] . Reduced HRR—manifested as blunted heart rate elevation during exercise—indicates autonomic imbalance and is strongly associated with susceptibility to fatal ventricular arrhythmias [ 12 ] . Furthermore, HRR has been identified as a robust predictor of cardiovascular mortality in young males [ 13 ] . a finding corroborated by our study linking diminished HRR to increased mortality risk. Despite the availability of multiple exercise-derived risk markers, HRR is widely adopted due to its consistent measurability across tests. Future studies should validate its generalizability in broader populations. Notably, high-intensity interval training (HIIT) has been shown to improve HRR [ 14 ] . Fergal Grace et al. reported that while no statistically significant changes in resting or maximal heart rates were observed under sedentary conditions, HIIT-induced HRR enhancement suggests improved chronotropic plasticity, likely mediated by restored sympathovagal balance during HIIT interventions [ 15 ] . These findings underscore the potential of exercise rehabilitation targeting HRR to mitigate long-term mortality in ACS patients, providing a scientific rationale for integrating HIIT into post-ACS care protocols. The selection of cardiopulmonary exercise testing (CPET) for HRR measurement offers unique advantages. On one hand, CPET objectively captures the pathophysiological changes and functional impairments across incremental exercise intensities by documenting symptom occurrence and physiological responses, thereby providing objective HRR data free from subjective interpretation. On the other hand, CPET enables the detection of exercise-induced myocardial ischemia and microvascular dysfunction. During graded exercise, stroke volume and heart rate synergistically increase to maintain cardiac output. In patients with coronary artery disease, when exercise intensity exceeds the ischemic threshold, stroke volume declines, triggering a compensatory surge in heart rate to preserve cardiac output. This dynamic sensitivity to heart rate modulation underscores CPET’s superiority in evaluating HRR, particularly for identifying autonomic and hemodynamic perturbations in at-risk populations. Furthermore, this study confirmed BUN and EF as critical prognostic factors in ACS, consistent with existing literature [ 16 – 19 ] . Elevated BUN levels may reflect a state of renal hypoperfusion secondary to hypovolemia, renal vascular disease, or diminished cardiac output [ 20 ] . BUN has been associated with adverse clinical outcomes and is now incorporated into ACS risk prediction models. Keerth AJ et al [ 20 ] demonstrated that elevated blood urea nitrogen (BUN) levels were associated with increased mortality among patients with unstable coronary syndrome. Similarly, Adam AM et al [ 16 ] indicated that BUN may serve as a significant tool for assessing mortality risk in patients with acute coronary syndrome (ACS). Furthermore, Li H et al [ 21 ] included BUN in their decision tree model for predicting in-hospital cardiac arrest among ACS patients. Ejection fraction (EF) is an independent predictor of in-hospital and 1-year mortality in patients with ST-segment elevation myocardial infarction (STEMI); independently predicts major adverse cardiac events in STEMI patients; and predicts prognosis in patients with non-ST-segment elevation myocardial infarction (NSTEMI) [ 22 ] . Yahud E et al [ 23 ] found in their 2000–2016 study of Israeli ACS patients that those with preserved EF had lower 1-year and 3-year mortality rates compared to patients with reduced EF. Furtado RHM et al [ 24 ] also demonstrated that in ACS patients, mildly reduced EF during the acute phase was associated with higher long-term mortality compared to patients with normal EF. Currently, there exist some ACS prediction models utilizing combined CPET parameters. In 2020, Suping Niu et al. [ 25 ] conducted a study involving 184 patients with a median follow-up of 51 months. They found that four CPET-related variables could predict major adverse cardiac events: premature CPET termination, peak oxygen uptake, heart rate reserve, and ventilatory equivalent for carbon dioxide slope. In 2024, Zhengyan Li et al. [ 26 ] conducted a study involving 375 patients with a 5-year follow-up. They identified peak oxygen uptake (Peak VO₂), carbon dioxide ventilation equivalent slope(VE/VCO₂ slope), and peak end-tidal carbon dioxide partial pressure(Peak PETCO₂) as three independent risk factors for recurrent acute myocardial infarction (re-AMI), heart failure (HF), and death after percutaneous coronary intervention (PCI) for AMI.This study featured a median follow-up of 3,821 days (approximately 10.5 years), with the longest reaching 16 years, demonstrating significant predictive value for the long-term prognosis of ACS patients. This study has several limitations. First, as a single-center study, sample selection may have been influenced by the characteristics of the specific region and population, potentially introducing selection bias. Due to the specific study design requirement (performing CPET), there was a higher proportion of male participants. Consequently, the applicability of this prediction model to female patients may be limited. Second, the endpoint event was confined to all-cause mortality, which restricts our ability to conduct in-depth analysis of the specific causes of death. Additionally, the model has not undergone external validation, meaning its generalizability and reliability have not yet been fully established. In future research, we plan to conduct extensive external validation of the model and enhance its representativeness and statistical power by incorporating additional research centers. We will also track and analyze multiple outcome events to enable a more comprehensive assessment of the model's predictive performance and further optimize it accordingly. Conclusion This study has successfully developed a prognostic assessment model for ACS leveraging key indicators such as CPET. This model effectively evaluates the long-term mortality risk in ACS patients, serving as a valuable reference for clinicians to formulate treatment plans, patients to make informed treatment choices, and families to participate in decision-making. Abbreviations ACS Acute coronary syndrome CPET cardiopulmonary exercise testing BUN blood urea nitrogen EF ejection fraction HRR heart rate reserve STEMI ST-segment elevation myocardial infarction NSTEMI non-ST-segment elevation myocardial infarction BMI body mass index HIIT high-intensity interval training PPV positive predictive value NPV negative predictive value HF heart failure PCI percutaneous coronary intervention Declarations Ethics approval and consent to participate The study complied with the Helsinki Declaration of Ethics and was approved by the Ethics Committee of Shanghai Tongji Hospital (ethics number 2021 − 125), with a waiver for informed consent.Prior to analysis, patient records/information were anonymized and de-identified. Consent for publication Not applicable. Competing interests The authors declare that they have no competing financial interests or personal relationships that could have influenced the work reported in this manuscript. Funding 2021 Tongji Hospital Clinical Research Cultivation Project (ITJ(QN)2108), 2023 Tongji Hospital Clinical Research Cultivation Project (ITJ(QN)2304), Key Research and Development Plan Project of Autonomous Region (2022B03023-3), Key Discipline Support Plan Project of Shanghai Municipal Health System (2023ZDFC0302), Shanghai Hospital Development Center Foundation—Shanghai Municipal Hospital Rehabilitation Medicine Specialty Alliance(SHDC22023304)and Scientific Research Project of Shanghai Municipal Health Commission (20234Y0186). Author Contribution T S: Study design; Manuscript drafting. YM J: Patient follow-up; Statistical analysis; Figure preparation. C S, DJ L: Data curation. MY Z, GH L: Data collection; Patient follow-up. L Q, QC H: Data collection. L Z, L C: Data analysis; Study supervision. LM W: Study supervision; Manuscript critical review. YQ S: Study supervision; Manuscript critical review; Funding acquisition. Acknowledgements Not applicable. Data Availability The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request References Bhatt DL, Lopes RD, Harrington RA. Diagnosis and Treatment of Acute Coronary Syndromes[J]. Jama,2022,327(7). Roth GA, Mensah GA, Johnson CO et al. Global Burden of Cardiovascular Diseases and Risk Factors, 1990–2019[J]. J Am Coll Cardiol 2020,76(25):2982–3021. Georgiopoulos G, Kraler S, Mueller-Hennessen M et al. Modification of the GRACE Risk Score for Risk Prediction in Patients With Acute Coronary Syndromes[J]. JAMA Cardiology,2023,8(10). Fox KAA, Dabbous OH, Goldberg RJ et al. Prediction of risk of death and myocardial infarction in the six months after presentation with acute coronary syndrome: prospective multinational observational study (GRACE)[J]. Bmj,2006,333(7578). Pocock SJ, Huo Y, Van de Werf F et al. Predicting two-year mortality from discharge after acute coronary syndrome: An internationally-based risk score[J]. 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Trends in long-term prognosis according to left ventricular ejection fraction after acute coronary syndrome[J]. J Cardiol 2020,76(3):303–8. Furtado RHM, Juliasz MG, Chiu FYJ, et al. Long-term mortality after acute coronary syndromes among patients with normal, mildly reduced, or reduced ejection fraction[J]. ESC Heart Fail. 2023;10(1):442–52. Niu S, Wang F, Yang S et al. Predictive value of cardiopulmonary fitness parameters in the prognosis of patients with acute coronary syndrome after percutaneous coronary intervention[J]. J Int Med Res,2020,48(8). Li Z, Fan B, Wu Y et al. Prognostic value of cardiopulmonary exercise test in patients with acute myocardial infarction after percutaneous coronary intervention[J]. Scientific Reports,2024,14(1). Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterials.pdf Cite Share Download PDF Status: Published Journal Publication published 27 Dec, 2025 Read the published version in BMC Cardiovascular Disorders → Version 1 posted Editorial decision: Revision requested 13 Oct, 2025 Reviews received at journal 09 Oct, 2025 Reviews received at journal 09 Oct, 2025 Reviewers agreed at journal 03 Oct, 2025 Reviewers agreed at journal 29 Sep, 2025 Reviewers agreed at journal 29 Sep, 2025 Reviewers invited by journal 26 Sep, 2025 Editor invited by journal 19 Sep, 2025 Editor assigned by journal 17 Sep, 2025 Submission checks completed at journal 17 Sep, 2025 First submitted to journal 11 Sep, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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14:51:49","extension":"xml","order_by":41,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":152656,"visible":true,"origin":"","legend":"","description":"","filename":"27d2348c445446eb8273a488555998921structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7592594/v1/9b0748baba2351d25e4195b2.xml"},{"id":93241557,"identity":"2c7e8b52-c5c8-44c5-856b-de757af555c3","added_by":"auto","created_at":"2025-10-10 14:51:49","extension":"html","order_by":42,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":162524,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7592594/v1/a484ab98e0663a9c33b2297e.html"},{"id":93241516,"identity":"a717145a-7c01-427a-8a69-e171e75b42b9","added_by":"auto","created_at":"2025-10-10 14:51:48","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":34124,"visible":true,"origin":"","legend":"\u003cp\u003eResearch process\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7592594/v1/48134b8fa89962601bba6ec0.jpg"},{"id":93241519,"identity":"a84bb5b8-c881-46ff-8de7-ddf1ca9f300c","added_by":"auto","created_at":"2025-10-10 14:51:48","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":113604,"visible":true,"origin":"","legend":"\u003cp\u003eNomogram for predicting mortality risk in Chinese patients with acute coronary syndrome\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7592594/v1/de83a2e940e543d81ae4cfc1.jpg"},{"id":93243657,"identity":"1d4e2539-0961-49aa-9be8-d131f7291e8d","added_by":"auto","created_at":"2025-10-10 14:59:48","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":98420,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier survival analysis in the derivation cohort\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7592594/v1/157818ae842a35a9ce8c9589.jpg"},{"id":99172232,"identity":"bd359a11-7a02-4c49-8671-a39235aeaa89","added_by":"auto","created_at":"2025-12-29 16:04:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1435702,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7592594/v1/1cfab0f7-8ef3-452f-a147-ef3d175223e3.pdf"},{"id":93244635,"identity":"2644b1cc-a9db-41bf-89b9-9ec32c52f508","added_by":"auto","created_at":"2025-10-10 15:07:48","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1070172,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterials.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7592594/v1/fcf38eca31803f63fe2720c9.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Development and Validation of a Long-Term Mortality Prediction Model for Acute Coronary Syndrome Patients Based on Cardiopulmonary Exercise Testing","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAcute coronary syndrome (ACS), characterized by a sudden reduction in cardiac blood supply, encompasses ST-segment elevation myocardial infarction (STEMI), non-ST-segment elevation myocardial infarction (NSTEMI), and unstable angina. Globally, over 7\u0026nbsp;million individuals are diagnosed with ACS annually, imposing a substantial burden on global health. \u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eDespite a decline in ACS mortality over the past 60 years, the risk of adverse outcomes remains high for patients presenting with extensive myocardial damage. \u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e Refined risk stratification may further improve survival rates.\u003c/p\u003e\u003cp\u003eSeveral predictive models have been published internationally, such as the GRACE study. \u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003eThis study developed a risk prediction tool that reliably forecasts the risk of death or myocardial infarction within 6 months, incorporating nine independent factors (including age, development or history of heart failure, peripheral vascular disease, systolic blood pressure, Killip class, initial serum creatinine concentration, initial elevation of cardiac markers, cardiac arrest at admission, and ST-segment deviation), with a C-index of 0.81. The predictive accuracy remains consistent across subgroups of acute coronary syndrome.\u003c/p\u003e\u003cp\u003eStuart J Pocock et al. \u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e developed a model to reliably predict the two-year mortality risk after hospital discharge for ACS patients, identifying 17 independent mortality prediction factors.\u003c/p\u003e\u003cp\u003eJun Ke et al. \u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e established a predictive model for in-hospital mortality risk in ACS patients, including independent predictive factors such as age, NSTEMI, Killip III, Killip IV, D-dimer levels, cardiac troponin I, creatine kinase, N-terminal pro-brain natriuretic peptide, high-density lipoprotein cholesterol, and statin use.\u003c/p\u003e\u003cp\u003eThe existing predictive models have the advantage of being easily obtainable and not limited to a single risk factor. However, most models utilize static indicators, which are insufficient to comprehensively reflect the overall condition of patients. Additionally, the follow-up periods in most studies are relatively short, making it difficult to accurately assess long-term prognosis. Therefore, the development of a novel predictive model for long-term mortality risk in ACS is particularly necessary.\u003c/p\u003e\u003cp\u003eThis study selected ACS patients from Shanghai Tongji Hospital as the target population for modeling and conducted cardiopulmonary exercise testing (CPET) to monitor respiratory and circulatory parameters during exercise, thereby obtaining comprehensive indicators of cardiopulmonary function. Research indicates that cardiopulmonary health provides additional prognostic value for mortality risk in patients with coronary artery disease, surpassing traditional cardiovascular risk factors. \u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eNotably, the follow-up period of this study spans 16 years, which facilitates a comprehensive assessment of the long-term prognosis of ACS patients. By incorporating CPET indicators, this study proposes a long-term mortality prediction model for ACS, providing a basis for clinical ACS treatment and revealing potential intervention targets for improving prognosis.\u003c/p\u003e"},{"header":"Method","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eSample Size Calculation\u003c/h2\u003e\u003cp\u003eThe minimum sample size required for model development was calculated using the pmsampsize function in R software (version 3.6.1, R Foundation for Statistical Computing, Vienna, Austria). The new predictive model may include up to four candidate parameters. Based on the research by Henderson RA et al. \u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003ethe 10-year all-cause mortality rate for patients with acute coronary syndrome (ACS) is 0.25. We aimed to establish a 10-year ACS long-term prognosis risk model, and the final calculated minimum sample size was 289.\u003c/p\u003e\u003cp\u003e Data Sources and Processing: This study adhered to the TRIPOD reporting guidelines. Clinical data were sourced from ACS patients who visited Shanghai Tongji Hospital between January 1, 2007, and December 31, 2018, totaling 1,880 cases. Inclusion criteria were: (1) diagnosis confirmed according to the 2023 ESC criteria for ACS; (2) age between 18 and 80 years; (3) ability to complete cardiopulmonary exercise testing (CPET). Exclusion criteria included: (1) acute myocardial infarction within 2 days; (2) uncontrolled unstable angina; (3) uncontrolled arrhythmias or heart failure; (4) severe aortic stenosis and descending aortic aneurysm; (5) acute pulmonary embolism, deep vein thrombosis, endocarditis, pericarditis, myocarditis, aortic dissection, pulmonary edema; (6) thyrotoxicosis; (7) renal failure; (8) acute infections or acute febrile diseases; (9) other physical disabilities affecting the test (inability to perform exercise testing or safety concerns). Following these criteria, 1,581 patients were excluded, resulting in the inclusion of 299 ACS patients (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). \u003cb\u003ePotential Predictive Variables\u003c/b\u003e\u003c/p\u003e\u003cp\u003eDemographic characteristics included age, gender, waist circumference, height, weight, body mass index (BMI), smoking status, etc.; other relevant medical histories included hypertension, diabetes, stroke, etc. CPET indicators were collected by professionally trained therapists/technicians. The equipment used included the MasterScreen series pulmonary function testing system from CareFusion, Germany, the LABTECH EC-12s exercise testing system from Hungary, and the ergoselect 150 exercise bicycle from ergoline, Germany. Patient CPET data were obtained from the cardiac rehabilitation center: peak oxygen pulse, peak oxygen consumption, oxygen consumption at anaerobic threshold, resting heart rate, peak heart rate, heart rate reserve (HRR) \u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e (HRR calculated as the difference between the maximum heart rate achieved and the supine resting heart rate), oxygen work efficiency, carbon dioxide ventilation equivalent slope, blood pressure changes during exercise (increase in systolic pressure, stable systolic pressure, decrease in systolic pressure), electrocardiogram changes during exercise (no arrhythmias and/or ST changes, arrhythmias and/or ST changes but not leading to test termination, arrhythmias and/or ST changes leading to test termination), etc.; auxiliary examinations included total cholesterol, N-terminal pro-brain natriuretic peptide, uric acid, blood urea nitrogen (BUN), high-sensitivity C-reactive protein, C-reactive protein, serum albumin to creatinine ratio, serum sodium, cystatin C, homocysteine, etc., echocardiography (performed by professionally trained echocardiographers using the EPI0 7C device from Philips Medical Systems), etc.; medication use included aspirin, clopidogrel, statins, etc.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eStudy Design\u003c/h3\u003e\n\u003cp\u003eIn this study, a total of 43 variables were incorporated as potential prognostic factors. Patients who succumbed during the follow-up period were categorized into the deceased group, while those who remained alive were assigned to the survivor group. The study employed an open-label design. To mitigate bias, the examiners, researchers responsible for collecting outcome data, data managers, and statisticians were blinded to the patients' identities.\u003c/p\u003e\n\u003ch3\u003eOutcomes\u003c/h3\u003e\n\u003cp\u003eThe endpoint of this study was defined in accordance with the key clinical data elements and cardiovascular endpoint event definitions jointly released by the American College of Cardiology, the U.S. Food and Drug Administration, and the Cardiovascular Trial Standards Data Collection Plan. The endpoint of this study was all-cause mortality. Data from electronic medical records and follow-up outcomes were utilized to measure this endpoint.\u003c/p\u003e\n\u003ch3\u003eFollow-Up\u003c/h3\u003e\n\u003cp\u003eSince July 2022, follow-up assessments have been conducted every six months via telephone calls or reviews of electronic medical records to collect information on mortality over the preceding period, continuing until the patient's death or the termination of the follow-up study on June 30, 2023. The time of death was documented by the follow-up personnel. Incorrect phone numbers, non-responses, and refusals to continue follow-up were considered as loss to follow-up.\u003c/p\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eStatistical analyses were performed using SPSS version 20.0 (IBM Corp, Armonk, NY, USA) and R software (version 3.6.1, R Foundation for Statistical Computing, Vienna, Austria). Quantitative data were tested for normal distribution. Normally distributed quantitative data were expressed as mean (standard deviation), with independent samples t-test used for comparisons between two groups and analysis of variance for comparisons among multiple groups. Non-normally distributed quantitative data were expressed as median (range), with the Mann-Whitney U test used for comparisons between groups. Categorical and ordinal data were expressed as frequency (percentage), with the χ2 test used for comparisons between groups. Additionally, for variables with missing values less than 20%, multiple imputation was conducted using the mice package in R.\u003c/p\u003e\u003cp\u003eModeling and Validation Data were randomly allocated into a derivation cohort and a validation cohort in a 7:3 ratio (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). In the validation cohort, LASSO regression and Cox multivariate analysis were employed to identify independent risk factors influencing the prognosis of acute coronary syndrome (ACS). A nomogram was developed to construct a risk prediction model, and the C-index values were calculated for both the derivation and validation cohorts. Discrimination was assessed, and calibration curves were plotted to evaluate the calibration. Finally, patients were stratified into high-risk (greater than the average risk score) and low-risk (not greater than the average risk score) categories, and Kaplan-Meier (KM) curves were used for survival analysis. \u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003eBaseline Characteristics\u003c/h2\u003e\u003cp\u003eThis cohort comprised 299 patients, with 211 in the derivation cohort and 88 in the validation cohort. The mean age was 57.0 years, and males constituted the majority (93.6%, n\u0026thinsp;=\u0026thinsp;280). The median follow-up duration was 3,821 days, with a loss-to-follow-up rate of 8.7%. Compared to survivors, non-survivors exhibited the following characteristics:(i) Older age;(ii) Higher prevalence of diabetes and stroke history;(iii) Elevated N-terminal pro-B-type natriuretic peptide (NT-proBNP), reduced estimated glomerular filtration rate, increased blood urea nitrogen (BUN), decreased albumin, and elevated homocysteine levels;(iv) Lower ejection fraction (EF);(v) Inferior cardiopulmonary exercise testing (CPET) metrics, including reduced peak oxygen consumption (VO₂), lower oxygen uptake at anaerobic threshold, diminished peak oxygen pulse, decreased peak heart rate, attenuated heart rate recovery (HRR), impaired oxygen work efficiency, and elevated carbon dioxide ventilation equivalent slope (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCharacteristics of Patients with Acute Coronary Syndrome\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eCharacteristic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOverall\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eThe survivor group\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eThe deceased group\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cem\u003eχ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e/ Z/t\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;299)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;253)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;46)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFollow-up duration(days)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3821.00 \u003c/p\u003e\u003cp\u003e[2767.50, 4626.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3852.00 \u003c/p\u003e\u003cp\u003e[2772.00, 4686.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3524.00 \u003c/p\u003e\u003cp\u003e[2546.25, 4414.50]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-1.448\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.148\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge(years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e57.00 \u003c/p\u003e\u003cp\u003e[52.00, 65.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e56.00 \u003c/p\u003e\u003cp\u003e[51.00, 63.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e65.00 \u003c/p\u003e\u003cp\u003e[57.00, 71.75]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-4.897\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e280 (93.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e239 (94.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e41 (89.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.074\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.300\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmoking(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e229 (76.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e195 (77.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e34 (73.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.217\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.641\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypertension(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e180 (60.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e149 (58.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e31 (67.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.173\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.279\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiabetes(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e74 (24.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e57 (22.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e17 (37.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4.350\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.037\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStroke (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e21 (7.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14 (5.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7 (15.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4.205\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.040\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrevious MI(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9 (3.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7 (2.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2 (4.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.012\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.914\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInfarct location(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.156\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.827\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExtensive anterior/Anterior wall\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e132 (44.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e110 (43.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e22 (47.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAnteroseptal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e17 (5.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14 (5.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3 (6.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh lateral\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9 (3.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9 (3.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRight ventricle\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e32 (10.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e28 (11.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4 (8.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInferior wall\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e104 (34.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e88 (34.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e16 (34.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5 (1.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4 (1.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1 (2.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKillip class (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5.297\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.151\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClass I\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e257 (86.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e222 (87.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e35 (76.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClass II\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e32 (10.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24 (9.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8 (17.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClass III\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2 (0.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (0.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1 (2.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClass IV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8 (2.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6 (2.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2 (4.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGensini score\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e46.00 \u003c/p\u003e\u003cp\u003e[28.00, 80.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e44.00 \u003c/p\u003e\u003cp\u003e[26.00, 80.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e52.00 \u003c/p\u003e\u003cp\u003e[30.50, 77.50]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.255\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.799\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWaist circumference(cm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e93.00 \u003c/p\u003e\u003cp\u003e[87.00, 97.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e93.00 \u003c/p\u003e\u003cp\u003e[88.00, 97.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e93.50 \u003c/p\u003e\u003cp\u003e[87.00, 98.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.339\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.735\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI (kg/m2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e24.80 \u003c/p\u003e\u003cp\u003e[23.07, 26.94]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24.82 \u003c/p\u003e\u003cp\u003e[23.10, 27.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e24.50\u003c/p\u003e\u003cp\u003e[22.86, 26.75]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.606\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.544\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal cholesterol(mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.66 \u003c/p\u003e\u003cp\u003e[4.04, 5.38]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.66 \u003c/p\u003e\u003cp\u003e[4.03, 5.38]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.74 \u003c/p\u003e\u003cp\u003e[4.10, 5.32]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.221\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.825\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNT-proBNP (pg/ml)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e241.00 \u003c/p\u003e\u003cp\u003e[74.81, 1164.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e229.00 \u003c/p\u003e\u003cp\u003e[73.40, 997.50]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e898.22 \u003c/p\u003e\u003cp\u003e[122.00, 2338.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-2.586\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.010\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBNP(pg/ml)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e107.00 \u003c/p\u003e\u003cp\u003e[60.00, 214.50]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e107.00 \u003c/p\u003e\u003cp\u003e[60.00, 212.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e100.15 \u003c/p\u003e\u003cp\u003e[71.25, 226.50]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.376\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.707\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eeGFR (mL/min/1.73m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e89.70 \u003c/p\u003e\u003cp\u003e[76.25, 99.20]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e91.10 \u003c/p\u003e\u003cp\u003e[80.70, 100.10]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e79.10 \u003c/p\u003e\u003cp\u003e[66.60, 89.68]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-3.876\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBUN (mmol/l)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.90 \u003c/p\u003e\u003cp\u003e[3.80, 6.20]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.80 \u003c/p\u003e\u003cp\u003e[3.63, 5.90]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.70 \u003c/p\u003e\u003cp\u003e[4.85, 6.40]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-3.075\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUric acid(umol/l)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e341.00 \u003c/p\u003e\u003cp\u003e[294.00, 404.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e341.00 \u003c/p\u003e\u003cp\u003e[293.00, 404.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e347.50 \u003c/p\u003e\u003cp\u003e[303.75, 401.75]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.579\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.562\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ehs-CRP(mg/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9.10 \u003c/p\u003e\u003cp\u003e[3.80, 23.10]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9.10 \u003c/p\u003e\u003cp\u003e[4.00, 22.90]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.60 \u003c/p\u003e\u003cp\u003e[3.20, 31.95]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.479\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.632\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCRP(mg/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.48 \u003c/p\u003e\u003cp\u003e[3.00, 12.72]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.00 \u003c/p\u003e\u003cp\u003e[3.00, 13.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.00 \u003c/p\u003e\u003cp\u003e[4.25, 10.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.630\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.529\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSerum albumin(g/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e37.42 (4.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e37.64 (4.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e36.25 (4.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-2.094\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.037\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCreatinine(umol/l)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e80.00 \u003c/p\u003e\u003cp\u003e[71.00, 91.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e79.00 \u003c/p\u003e\u003cp\u003e[71.00, 90.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e84.00 \u003c/p\u003e\u003cp\u003e[72.25, 98.75]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-1.764\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.078\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlbumin/creatinine(g/mmol)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.47 \u003c/p\u003e\u003cp\u003e[0.40, 0.53]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.47 \u003c/p\u003e\u003cp\u003e[0.41, 0.54]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.46 \u003c/p\u003e\u003cp\u003e[0.34, 0.51]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-1.948\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.051\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSerum sodium (mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e140.00 \u003c/p\u003e\u003cp\u003e[138.00, 142.20]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e140.00 \u003c/p\u003e\u003cp\u003e[138.00, 142.20]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e140.00 \u003c/p\u003e\u003cp\u003e[138.00, 142.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.095\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.925\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCystatin C (mg/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.25 \u003c/p\u003e\u003cp\u003e[1.08, 1.65]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.25 \u003c/p\u003e\u003cp\u003e[1.08, 1.63]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.29 \u003c/p\u003e\u003cp\u003e[1.09, 1.80]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.945\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.345\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHomocysteine(umol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13.50 \u003c/p\u003e\u003cp\u003e[11.00, 17.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13.00 \u003c/p\u003e\u003cp\u003e[11.00, 16.60]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15.60 \u003c/p\u003e\u003cp\u003e[12.25, 19.77]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-3.217\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWhite blood cell count (10^9/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9.90 \u003c/p\u003e\u003cp\u003e[8.11, 12.10]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10.00 \u003c/p\u003e\u003cp\u003e[8.00, 12.10]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.55 \u003c/p\u003e\u003cp\u003e[8.66, 11.87]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.319\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.750\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHemoglobin(g/l)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e140.00 \u003c/p\u003e\u003cp\u003e[131.00, 150.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e140.00 \u003c/p\u003e\u003cp\u003e[131.00, 150.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e140.50 \u003c/p\u003e\u003cp\u003e[128.25, 147.75]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.963\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.335\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRDW (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12.90 \u003c/p\u003e\u003cp\u003e[12.60, 13.40]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12.90 \u003c/p\u003e\u003cp\u003e[12.60, 13.40]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13.10 \u003c/p\u003e\u003cp\u003e[12.60, 13.30]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.216\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.829\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLVEF(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.58 \u003c/p\u003e\u003cp\u003e[0.51, 0.64]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.58 \u003c/p\u003e\u003cp\u003e[0.51, 0.65]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.53 \u003c/p\u003e\u003cp\u003e[0.47, 0.61]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-2.390\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.017\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePeak VO₂ (mL/ (kg\u0026middot;min)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15.00 \u003c/p\u003e\u003cp\u003e[13.00, 17.10]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15.30 \u003c/p\u003e\u003cp\u003e[13.30, 17.20]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13.35 \u003c/p\u003e\u003cp\u003e[11.00, 15.60]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-3.614\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAT VO₂ (mL/ (kg\u0026middot;min)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10.70 \u003c/p\u003e\u003cp\u003e[9.20, 12.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10.90 \u003c/p\u003e\u003cp\u003e[9.40, 12.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.60 \u003c/p\u003e\u003cp\u003e[8.40, 11.83]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-2.248\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.025\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePeak VO₂/HR (ml/beat)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9.90 \u003c/p\u003e\u003cp\u003e[8.20, 11.40]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10.00 \u003c/p\u003e\u003cp\u003e[8.59, 11.60]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.65 \u003c/p\u003e\u003cp\u003e[6.73, 10.47]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-2.969\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHR rest (bpm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e73.00 \u003c/p\u003e\u003cp\u003e[66.00, 80.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e72.00 \u003c/p\u003e\u003cp\u003e[66.00, 80.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e73.50 \u003c/p\u003e\u003cp\u003e[67.25, 79.75]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.484\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.628\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHR peak (bpm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e113.00 \u003c/p\u003e\u003cp\u003e[103.00, 120.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e113.00 \u003c/p\u003e\u003cp\u003e[105.00, 120.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e107.00 \u003c/p\u003e\u003cp\u003e[96.50, 114.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-2.678\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.007\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHRR (bpm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e38.80 (12.92)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e39.87 (12.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e32.93 (12.53)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-3.407\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eΔVO₂/ΔWR (ml/min/watt)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.80 \u003c/p\u003e\u003cp\u003e[6.40, 9.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.90 \u003c/p\u003e\u003cp\u003e[6.50, 9.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.20 \u003c/p\u003e\u003cp\u003e[5.10, 8.70]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-2.318\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.020\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVE/VCO₂ slope\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e33.60 \u003c/p\u003e\u003cp\u003e[30.50, 38.35]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e33.50 \u003c/p\u003e\u003cp\u003e[30.50, 37.20]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e35.70 \u003c/p\u003e\u003cp\u003e[30.68, 43.18]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-2.172\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.030\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExercise BP change(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.677\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.159\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSystolic BP elevation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e180 (60.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e148 (58.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e32 (69.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSystolic BP flat\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e105 (35.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e91 (36.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e14 (30.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSystolic BP decline\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14 (4.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14 (5.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAspirin (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e283 (94.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e238 (94.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e45 (97.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.469\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.493\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClopidogrel (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e293 (98.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e249 (98.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e44 (95.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.435\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.510\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStatins (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e295 (98.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e250 (98.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e45 (97.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eACEI/ARB (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e230 (76.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e195 (77.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e35 (76.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.884\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBeta-blockers (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e223 (74.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e192 (75.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e31 (67.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.483\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.223\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eNotes: BMI\u0026thinsp;=\u0026thinsp;body mass index; NT-proBNP: N-terminal pro-B-type Natriuretic Peptide; BNP: B-type Natriuretic Peptide; eGFR: Estimated Glomerular Filtration Rate; BUN: Blood Urea Nitrogen; hs-CRP: High-sensitivity C-reactive Protein; CRP: C-reactive Protein; RDW: Red blood cell Distribution Width; LVEF: Left Ventricular Ejection Fraction; PeakVO₂ = peak oxygen consumption; ATVO₂ = oxygen uptake at anaerobic threshold; HR: Heart Rate; PeakVO₂/HR\u0026thinsp;=\u0026thinsp;peak oxygen pulse; HR rest\u0026thinsp;=\u0026thinsp;resting heart rate; HR peak\u0026thinsp;=\u0026thinsp;peak heart rate; HRR\u0026thinsp;=\u0026thinsp;heart rate reserve; ΔVO₂/ΔWR\u0026thinsp;=\u0026thinsp;oxygen work efficiency; VE/VCO₂ slope\u0026thinsp;=\u0026thinsp;carbon dioxide ventilation equivalent slope; BP: Blood Pressure;ACEI: Angiotensin-Converting Enzyme Inhibitor; ARB: Angiotensin II Receptor Blocker.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eRisk Model Development and Validation\u003c/h3\u003e\n\u003cp\u003eThe cohort was randomly divided via computer-generated random numbers, with 70% (n\u0026thinsp;=\u0026thinsp;211) allocated to the derivation cohort and 30% (n\u0026thinsp;=\u0026thinsp;88) to the validation cohort. No significant differences in baseline characteristics were observed between the two cohorts (Supplementary Table\u0026nbsp;1). Among 41 candidate variables screened by LASSO regression, seven variables (age, diabetes, stroke history, BUN, homocysteine, EF, HRR) were retained. Subsequent univariate and multivariate Cox regression analyses identified four independent predictors (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05): age, BUN, EF, and HRR (Supplementary Fig.\u0026nbsp;1; Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAnalysis of Risk Event Indicators in Acute Coronary Syndrome\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eIndicator\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eUnivariate Cox regression Analysis\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eMultivariate Cox Regression \u003c/p\u003e\u003cp\u003eAnalysis\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHR(95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHR(95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.09 (1.05\u0026ndash;1.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.06(1.02\u0026ndash;1.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.006\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiabetes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.18 (1.05\u0026ndash;4.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.036\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.90(0.85\u0026ndash;4.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.119\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHistory of Stroke\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.33 (1.43\u0026ndash;7.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.005\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.02(0.73\u0026ndash;5.58)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.174\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBUN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.29 (1.14\u0026ndash;1.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.22(1.05\u0026ndash;1.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.008\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHomocysteine\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.04 (1.01\u0026ndash;1.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.02(0.99\u0026ndash;1.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.245\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.00 (0.00-0.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.01(0.00-0.34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.011\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHRR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.96 (0.93\u0026ndash;0.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.96(0.93\u0026ndash;0.99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.010\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eNotes:HR: Hazard Ratio; CI: Confidence Interval; BUN: Blood Urea Nitrogen; EF: Ejection Fraction; HRR: Heart Rate Reserve.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eMultivariable Cox Regression and Nomogram Construction\u003c/h2\u003e\u003cp\u003eBased on the regression coefficients from the multivariable Cox analysis of the four predictors (age, BUN, EF, and HRR), a prognostic risk model was developed. These factors\u0026mdash;independently associated with higher mortality risk\u0026mdash;were integrated into a risk scoring system, visualized as a nomogram (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe ROC curve of the prediction model is shown in Supplementary Fig.\u0026nbsp;2. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) for the model in both derivation and validation cohorts are summarized in Supplementary Table\u0026nbsp;2.\u003c/p\u003e\u003cp\u003eThe model demonstrated moderate discriminative ability, with C-index values of 0.83 (95% CI: 0.76\u0026ndash;0.89) in the derivation cohort and 0.72 (95% CI: 0.56\u0026ndash;0.88) in the validation cohort. Calibration plots revealed close alignment between predicted and observed 5-, 10-, and 15-year clinical outcomes (Supplementary Fig.\u0026nbsp;3).\u003c/p\u003e\u003cp\u003eUsing nomogram-derived risk scores, the derivation cohort was stratified into high-risk and low-risk groups. Kaplan-Meier survival analysis with the Log-rank test showed statistically significant divergence (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in survival curves between risk groups across all cohorts (derivation, validation, and combined cohorts). In the derivation cohort, the high-risk group exhibited cumulative mortality rates of 13%, 53%, and 89% at 5, 10, and 15 years, respectively, compared to 6%, 38%, and 83% in the low-risk group (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e; Supplementary Fig.\u0026nbsp;4).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we developed a novel prognostic model for acute coronary syndrome (ACS) patients by integrating concise parameters derived from clinical profiles, laboratory markers, cardiopulmonary exercise testing (CPET), and other measurements. The model demonstrated moderate predictive performance, with C-index values of 0.83 (95% CI: 0.76\u0026ndash;0.89) in the derivation cohort and 0.72 (95% CI: 0.56\u0026ndash;0.88) in the validation cohort. Calibration plots showed excellent agreement between predicted and observed 5-, 10-, and 15-year outcomes. Compared to traditional ACS risk models, our model offers distinct advantages: it requires only four readily available clinical variables (age, BUN, EF, and HRR), which simplifies data collection and implementation; incorporates CPET-derived HRR to enhance risk stratification by providing granular insights into cardiopulmonary functional capacity; and demonstrates robust long-term predictive stability through a median follow-up of 3,821 days (maximum 16 years). These features underscore its potential for widespread clinical adoption as a practical, simple, and efficient tool for long-term mortality risk evaluation, thereby supporting clinical decision-making and improving the accessibility of prognostic models.\u003c/p\u003e\u003cp\u003eThe model incorporates four predictors: age, BUN, EF, and HRR. HRR (heart rate reserve), which reflects cardiac sympathetic reserve, has demonstrated independent prognostic value in risk stratification for chronic coronary syndromes and heart failure\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. Reduced HRR\u0026mdash;manifested as blunted heart rate elevation during exercise\u0026mdash;indicates autonomic imbalance and is strongly associated with susceptibility to fatal ventricular arrhythmias\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. Furthermore, HRR has been identified as a robust predictor of cardiovascular mortality in young males\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. a finding corroborated by our study linking diminished HRR to increased mortality risk. Despite the availability of multiple exercise-derived risk markers, HRR is widely adopted due to its consistent measurability across tests. Future studies should validate its generalizability in broader populations. Notably, high-intensity interval training (HIIT) has been shown to improve HRR\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. Fergal Grace et al. reported that while no statistically significant changes in resting or maximal heart rates were observed under sedentary conditions, HIIT-induced HRR enhancement suggests improved chronotropic plasticity, likely mediated by restored sympathovagal balance during HIIT interventions \u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. These findings underscore the potential of exercise rehabilitation targeting HRR to mitigate long-term mortality in ACS patients, providing a scientific rationale for integrating HIIT into post-ACS care protocols.\u003c/p\u003e\u003cp\u003eThe selection of cardiopulmonary exercise testing (CPET) for HRR measurement offers unique advantages. On one hand, CPET objectively captures the pathophysiological changes and functional impairments across incremental exercise intensities by documenting symptom occurrence and physiological responses, thereby providing objective HRR data free from subjective interpretation. On the other hand, CPET enables the detection of exercise-induced myocardial ischemia and microvascular dysfunction. During graded exercise, stroke volume and heart rate synergistically increase to maintain cardiac output. In patients with coronary artery disease, when exercise intensity exceeds the ischemic threshold, stroke volume declines, triggering a compensatory surge in heart rate to preserve cardiac output. This dynamic sensitivity to heart rate modulation underscores CPET\u0026rsquo;s superiority in evaluating HRR, particularly for identifying autonomic and hemodynamic perturbations in at-risk populations.\u003c/p\u003e\u003cp\u003eFurthermore, this study confirmed BUN and EF as critical prognostic factors in ACS, consistent with existing literature\u003csup\u003e[\u003cspan additionalcitationids=\"CR17 CR18\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. Elevated BUN levels may reflect a state of renal hypoperfusion secondary to hypovolemia, renal vascular disease, or diminished cardiac output\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. BUN has been associated with adverse clinical outcomes and is now incorporated into ACS risk prediction models. Keerth AJ et al \u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e demonstrated that elevated blood urea nitrogen (BUN) levels were associated with increased mortality among patients with unstable coronary syndrome. Similarly, Adam AM et al \u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003eindicated that BUN may serve as a significant tool for assessing mortality risk in patients with acute coronary syndrome (ACS). Furthermore, Li H et al \u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003eincluded BUN in their decision tree model for predicting in-hospital cardiac arrest among ACS patients.\u003c/p\u003e\u003cp\u003eEjection fraction (EF) is an independent predictor of in-hospital and 1-year mortality in patients with ST-segment elevation myocardial infarction (STEMI); independently predicts major adverse cardiac events in STEMI patients; and predicts prognosis in patients with non-ST-segment elevation myocardial infarction (NSTEMI) \u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. Yahud E et al \u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003efound in their 2000\u0026ndash;2016 study of Israeli ACS patients that those with preserved EF had lower 1-year and 3-year mortality rates compared to patients with reduced EF. Furtado RHM et al \u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e also demonstrated that in ACS patients, mildly reduced EF during the acute phase was associated with higher long-term mortality compared to patients with normal EF.\u003c/p\u003e\u003cp\u003eCurrently, there exist some ACS prediction models utilizing combined CPET parameters. In 2020, Suping Niu et al. \u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003econducted a study involving 184 patients with a median follow-up of 51 months. They found that four CPET-related variables could predict major adverse cardiac events: premature CPET termination, peak oxygen uptake, heart rate reserve, and ventilatory equivalent for carbon dioxide slope. In 2024, Zhengyan Li et al. \u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003econducted a study involving 375 patients with a 5-year follow-up. They identified peak oxygen uptake (Peak VO₂), carbon dioxide ventilation equivalent slope(VE/VCO₂ slope), and peak end-tidal carbon dioxide partial pressure(Peak PETCO₂) as three independent risk factors for recurrent acute myocardial infarction (re-AMI), heart failure (HF), and death after percutaneous coronary intervention (PCI) for AMI.This study featured a median follow-up of 3,821 days (approximately 10.5 years), with the longest reaching 16 years, demonstrating significant predictive value for the long-term prognosis of ACS patients.\u003c/p\u003e\u003cp\u003eThis study has several limitations. First, as a single-center study, sample selection may have been influenced by the characteristics of the specific region and population, potentially introducing selection bias. Due to the specific study design requirement (performing CPET), there was a higher proportion of male participants. Consequently, the applicability of this prediction model to female patients may be limited. Second, the endpoint event was confined to all-cause mortality, which restricts our ability to conduct in-depth analysis of the specific causes of death. Additionally, the model has not undergone external validation, meaning its generalizability and reliability have not yet been fully established. In future research, we plan to conduct extensive external validation of the model and enhance its representativeness and statistical power by incorporating additional research centers. We will also track and analyze multiple outcome events to enable a more comprehensive assessment of the model's predictive performance and further optimize it accordingly.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study has successfully developed a prognostic assessment model for ACS leveraging key indicators such as CPET. This model effectively evaluates the long-term mortality risk in ACS patients, serving as a valuable reference for clinicians to formulate treatment plans, patients to make informed treatment choices, and families to participate in decision-making.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eACS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAcute coronary syndrome\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCPET\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ecardiopulmonary exercise testing\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eBUN\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eblood urea nitrogen\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eEF\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eejection fraction\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eHRR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eheart rate reserve\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSTEMI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eST-segment elevation myocardial infarction\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eNSTEMI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003enon-ST-segment elevation myocardial infarction\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eBMI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ebody mass index\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eHIIT\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ehigh-intensity interval training\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePPV\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003epositive predictive value\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eNPV\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003enegative predictive value\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eHF\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eheart failure\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePCI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003epercutaneous coronary intervention\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003cp\u003e The study complied with the Helsinki Declaration of Ethics and was approved by the Ethics Committee of Shanghai Tongji Hospital (ethics number 2021\u0026thinsp;\u0026minus;\u0026thinsp;125), with a waiver for informed consent.Prior to analysis, patient records/information were anonymized and de-identified.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003cp\u003eNot applicable.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eCompeting interests\u003c/h2\u003e\u003cp\u003eThe authors declare that they have no competing financial interests or personal relationships that could have influenced the work reported in this manuscript.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003e2021 Tongji Hospital Clinical Research Cultivation Project (ITJ(QN)2108), 2023 Tongji Hospital Clinical Research Cultivation Project (ITJ(QN)2304), Key Research and Development Plan Project of Autonomous Region (2022B03023-3), Key Discipline Support Plan Project of Shanghai Municipal Health System (2023ZDFC0302), Shanghai Hospital Development Center Foundation\u0026mdash;Shanghai Municipal Hospital Rehabilitation Medicine Specialty Alliance(SHDC22023304)and Scientific Research Project of Shanghai Municipal Health Commission (20234Y0186).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eT S: Study design; Manuscript drafting. YM J: Patient follow-up; Statistical analysis; Figure preparation. C S, DJ L: Data curation. MY Z, GH L: Data collection; Patient follow-up. L Q, QC H: Data collection. L Z, L C: Data analysis; Study supervision. LM W: Study supervision; Manuscript critical review. YQ S: Study supervision; Manuscript critical review; Funding acquisition.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e\u003cp\u003eNot applicable.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBhatt DL, Lopes RD, Harrington RA. Diagnosis and Treatment of Acute Coronary Syndromes[J]. Jama,2022,327(7).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRoth GA, Mensah GA, Johnson CO et al. Global Burden of Cardiovascular Diseases and Risk Factors, 1990\u0026ndash;2019[J]. J Am Coll Cardiol 2020,76(25):2982\u0026ndash;3021.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGeorgiopoulos G, Kraler S, Mueller-Hennessen M et al. Modification of the GRACE Risk Score for Risk Prediction in Patients With Acute Coronary Syndromes[J]. JAMA Cardiology,2023,8(10).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFox KAA, Dabbous OH, Goldberg RJ et al. Prediction of risk of death and myocardial infarction in the six months after presentation with acute coronary syndrome: prospective multinational observational study (GRACE)[J]. Bmj,2006,333(7578).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePocock SJ, Huo Y, Van de Werf F et al. Predicting two-year mortality from discharge after acute coronary syndrome: An internationally-based risk score[J]. Eur Heart Journal: Acute Cardiovasc Care 2017,8(8):727\u0026ndash;37.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKe J, Chen Y, Wang X et al. Machine learning-based in-hospital mortality prediction models for patients with acute coronary syndrome[J]. Am J Emerg Med 2022,53:127\u0026ndash;34.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEzzatvar Y, Izquierdo M, N\u0026uacute;\u0026ntilde;ez J, et al. Cardiorespiratory fitness measured with cardiopulmonary exercise testing and mortality in patients with cardiovascular disease: A systematic review and meta-analysis[J]. J Sport Health Sci. 2021;10(6):609\u0026ndash;19.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHenderson RA, Jarvis C, Clayton T et al. 10-Year Mortality Outcome of a Routine Invasive Strategy Versus a Selective Invasive Strategy in Non\u0026ndash;ST-Segment Elevation Acute Coronary Syndrome[J]. J Am Coll Cardiol 2015,66(5):511\u0026ndash;20.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOmiya K, Minami K, Sato Y et al. Impaired β-cell function attenuates training effects by reducing the increase in heart rate reserve in patients with myocardial infarction[J]. J Cardiol 2015,65(2):128\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCeglarek U, Schellong P, Rosolowski M et al. The novel cystatin C, lactate, interleukin-6, and N-terminal pro-B-type natriuretic peptide (CLIP)-based mortality risk score in cardiogenic shock after acute myocardial infarction[J]. Eur Heart J 2021,42(24):2344\u0026ndash;52.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCortigiani L, Vecchi A, Bovenzi F et al. Reduced coronary flow velocity reserve and blunted heart rate reserve identify a higher risk group in patients with chest pain and negative emergency department evaluation[J]. Intern Emerg Med 2022,17(7):2103\u0026ndash;11.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKurl S, Jae SY, Voutilainen A et al. Exercise heart rate reserve and recovery as risk factors for sudden cardiac death[J]. Progress Cardiovasc Dis 2021,68:7\u0026ndash;11.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDuarte CV, Myers J, de Ara\u0026uacute;jo C, G S. Exercise heart rate gradient: A novel index to predict all-cause mortality[J]. Eur J Prev Cardiol 2014,22(5):629\u0026ndash;35.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eConcei\u0026ccedil;\u0026atilde;o LSR, Gois CO, Fernandes RES et al. Effect of High-Intensity Interval Training on Aerobic Capacity and Heart Rate Control of Heart Transplant Recipients: a Systematic Review with Meta-Analysis[J]. Brazilian Journal of Cardiovascular Surgery,2021,36(1).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGrace F, Herbert P, Elliott AD, et al. High intensity interval training (HIIT) improves resting blood pressure, metabolic (MET) capacity and heart rate reserve without compromising cardiac function in sedentary aging men[J]. Exp Gerontol. 2018;109:75\u0026ndash;81.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAdam AM, Nasir SAR, Merchant AZ, et al. Efficacy of serum blood urea nitrogen, creatinine and electrolytes in the diagnosis and mortality risk assessment of patients with acute coronary syndrome[J]. Indian Heart J. 2018;70(3):353\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eShin SH, Claggett B, Pfeffer MA et al. Hyperglycaemia, ejection fraction and the risk of heart failure or cardiovascular death in patients with type 2 diabetes and a recent acute coronary syndrome[J]. Eur J Heart Fail 2020,22(7):1133\u0026ndash;43.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYahud E, Tzuman O, Fink N et al. Trends in long-term prognosis according to left ventricular ejection fraction after acute coronary syndrome[J]. J Cardiol 2020,76(3):303\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLenell J, Lindahl B, Erlinge D et al. Global longitudinal strain in long-term risk prediction after acute coronary syndrome: an investigation of added prognostic value to ejection fraction[J]. Clinical Research in Cardiology,2024.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKirtane AJ, Leder DM, Waikar SS et al. Serum Blood Urea Nitrogen as an Independent Marker of Subsequent Mortality Among Patients With Acute Coronary Syndromes and Normal to Mildly Reduced Glomerular Filtration Rates[J]. J Am Coll Cardiol 2005,45(11):1781\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi H, Wu TT, Yang DL, et al. Decision tree model for predicting in-hospital cardiac arrest among patients admitted with acute coronary syndrome[J]. Clin Cardiol. 2019;42(11):1087\u0026ndash;93.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKırıs T, Avcı E, \u0026ccedil;elik A. Combined value of left ventricular ejection fraction and the Model for End-Stage Liver Disease (MELD) score for predicting mortality in patients with acute coronary syndrome who were undergoing percutaneous coronary intervention[J]. BMC Cardiovascular Disorders,2018,18(1).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYahud E, Tzuman O, Fink N et al. Trends in long-term prognosis according to left ventricular ejection fraction after acute coronary syndrome[J]. J Cardiol 2020,76(3):303\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFurtado RHM, Juliasz MG, Chiu FYJ, et al. Long-term mortality after acute coronary syndromes among patients with normal, mildly reduced, or reduced ejection fraction[J]. ESC Heart Fail. 2023;10(1):442\u0026ndash;52.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNiu S, Wang F, Yang S et al. Predictive value of cardiopulmonary fitness parameters in the prognosis of patients with acute coronary syndrome after percutaneous coronary intervention[J]. J Int Med Res,2020,48(8).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi Z, Fan B, Wu Y et al. Prognostic value of cardiopulmonary exercise test in patients with acute myocardial infarction after percutaneous coronary intervention[J]. Scientific Reports,2024,14(1).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-cardiovascular-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcar","sideBox":"Learn more about [BMC Cardiovascular Disorders](http://bmccardiovascdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcar/default.aspx","title":"BMC Cardiovascular Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Risk prediction, Mortality rate, Acute coronary syndrome","lastPublishedDoi":"10.21203/rs.3.rs-7592594/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7592594/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground.\u003c/h2\u003e\u003cp\u003eAcute coronary syndrome (ACS) is a major global health burden with a high risk of adverse outcomes. Existing predictive models (e.g., GRACE) primarily rely on static indicators and focus on short-term prognosis, limiting their ability to comprehensively assess patient status and predict long-term mortality. To address the need for improved long-term risk prediction, this study developed and validated a long-term mortality prediction model for ACS patients based on cardiopulmonary exercise testing (CPET) and other clinical indicators.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003e. This study included ACS patients who were treated at Tongji Hospital in Shanghai from January 1, 2007, to December 31, 2018, according to the inclusion criteria. Demographic data, medical histories, CPET indicators, laboratory indicators, and other baseline data of all included patients were collected, and their mortality was followed up. All data sets were randomly divided into derivation and validation cohorts in a ratio of 7/3. Least absolute shrinkage and selection operator regression and Cox multivariate analysis were used to identify independent risk factors affecting ACS prognosis, and a risk prediction model was established using nomograms.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003e. A total of 299 patients were included in this cohort (211 in the derivation cohort and 88 in the validation cohort), with an average age of 57.00 years, including 280 males (93.6%). The median follow-up time was 3821 days, and 46 cases (15.4%) reached the study endpoint. The derivation cohort identified four independent predictive factors: age, blood urea nitrogen (BUN), ejection fraction (EF), and heart rate reserve (HRR), and a Nomogram scoring model was constructed based on these factors. The C indexes values of the derivation and the validation cohorts were 0.83 (0.76, 0.89) and 0.72 (0.56, 0.88), respectively. Calibration curves indicated good consistency between model predictions and actual observations.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003e. A model established based on four CPET indicators\u0026mdash;age, BUN, EF, and HRR\u0026mdash;can effectively predict the long-term all-cause mortality risk of ACS, providing a new tool for the long-term management of ACS.\u003c/p\u003e\u003ch2\u003eTrial registration.\u003c/h2\u003e\u003cp\u003eRegistry: Chinese Clinical Trial Registry; TRN: ChiCTR2100052199; Registration date: October 22, 2021.\u003c/p\u003e","manuscriptTitle":"Development and Validation of a Long-Term Mortality Prediction Model for Acute Coronary Syndrome Patients Based on Cardiopulmonary Exercise Testing","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-10 14:51:43","doi":"10.21203/rs.3.rs-7592594/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-13T11:17:17+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-09T19:36:56+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-09T16:48:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"239774653537937743989152584179650564281","date":"2025-10-03T10:45:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"201326186480567893102925258808013815339","date":"2025-09-29T16:30:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"167942652372434965606297484155107093521","date":"2025-09-29T15:18:34+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-26T18:42:51+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-09-19T08:23:53+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-17T14:09:20+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-17T14:07:35+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cardiovascular Disorders","date":"2025-09-11T13:43:57+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cardiovascular-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcar","sideBox":"Learn more about [BMC Cardiovascular Disorders](http://bmccardiovascdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcar/default.aspx","title":"BMC Cardiovascular Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4200fe03-eea1-4589-a66b-fcd4433ae512","owner":[],"postedDate":"October 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-12-29T15:59:18+00:00","versionOfRecord":{"articleIdentity":"rs-7592594","link":"https://doi.org/10.1186/s12872-025-05465-2","journal":{"identity":"bmc-cardiovascular-disorders","isVorOnly":false,"title":"BMC Cardiovascular Disorders"},"publishedOn":"2025-12-27 15:57:07","publishedOnDateReadable":"December 27th, 2025"},"versionCreatedAt":"2025-10-10 14:51:43","video":"","vorDoi":"10.1186/s12872-025-05465-2","vorDoiUrl":"https://doi.org/10.1186/s12872-025-05465-2","workflowStages":[]},"version":"v1","identity":"rs-7592594","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7592594","identity":"rs-7592594","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}