Jiang, Yumei; School of Medicine, Tongji University, Shanghai, 200065, China.
Shen, Ting;Shi, Cheng;Li, Dejie; et al

BMC cardiovascular disorders,2025 Dec 27

• 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 in this specific patient subgroup, this study developed and validated a long-term mortality prediction model for ACS patients incorporating cardiopulmonary exercise testing (CPET) and other clinical indicators.
• Methods: This retrospective cohort study included ACS patients treated at Tongji Hospital from January 1, 2007, to December 31, 2018. Demographic data, medical histories, CPET indicators, laboratory indicators, and other baseline data were collected, and all-cause mortality was followed up until June 30, 2023. 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 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 model demonstrated good discrimination in the derivation cohort (C-index: 0.83) but this decreased in the validation cohort (C-index: 0.72), suggesting potential overfitting. Time-dependent calibration analysis showed poor agreement at 5 years in the validation cohort (R ² = 0.1819), but improved at 10 years (Slope = 0.8006, R ² = 0.5575) and 15 years (R ² = 0.5638). The model’s applicability is strictly limited to the studied population: a predominantly male, lower-risk subset of ACS survivors capable of completing CPET.
• Conclusions: A model based on four readily available variables-age, BUN, EF, and the key CPET parameter, HRR-may have utility for predicting long-term all-cause mortality. This model provides a preliminary tool for the long-term management of a specific subpopulation of acute coronary syndrome (ACS) survivors, namely a predominantly male, lower-risk cohort with the capacity to complete CPET. Further external validation in similar populations is required before prospective clinical application.