Environmental exposure-integrated risk stratification for adverse outcomes after acute coronary syndrome: a multi-cohort machine learning approach.

Authors

Mingyue Zhang, Jingwei Zhang, Yushan Cui, Jingya Wang, Mengmeng Wang, Wenlong Zheng

Published in

International journal of medical informatics. Volume 220. Pages 106635. Jul 24, 2026. Epub Jul 24, 2026.

Abstract

Acute coronary syndrome (ACS) causes substantial post-discharge mortality. Ambient air pollution and meteorological conditions are associated with recurrent cardiovascular events, but existing clinical risk scores rely only on static admission parameters without incorporating post-discharge environmental exposures. Most machine learning (ML) models for ACS prognosis are single-center, single-horizon, fail to integrate both meteorological and air-quality variables, and lack external validation or interpretability.
We developed an interpretable multi-horizon ML framework for 3-month, 6-month, and 1-year post-discharge mortality using 24,120 ACS patients from 28 institutions in the Tianjin Chest Pain Center registry, split into training (n = 15,436), internal validation (n = 3,860), and single-region, multi-institution external validation (n = 4,824) cohorts. Seventeen predictors (3 demographic/clinical, 14 environmental) were evaluated across 5 algorithms. Discrimination was assessed by AUC (95% CI, DeLong method), calibration by Brier scores and calibration plots, and survival differences by log-rank test. Missing data were handled via multiple imputation, with SHAP analysis quantifying predictor contributions.
CatBoost demonstrated the most stable cross-cohort performance, with 1-year AUCs of 0.625 (0.596-0.654) and 0.629 (0.602-0.654) in internal and external validation, significantly outperforming random forest and XGBoost (P < 0.05). Top predictors included winter minimum relative humidity, length of hospital stay, summer mean temperature, sex, and annual PM_2.5. Predefined risk thresholds yielded significantly separated Kaplan-Meier curves (log-rank P < 0.001). Given missing key admission-severity indicators (Killip class, cardiac biomarkers, ECG findings), SHAP-based rankings are conditional on the available feature set and may overstate environmental exposures' relative contribution.
With moderate discrimination (AUC 0.62-0.65) and single-region design, this multi-horizon ML framework integrating meteorological and air-quality variables was validated via single-region, multi-institution external validation. It is intended as a low-cost triage signal to prioritize follow-up, not a stand-alone decision tool, and does not replace clinical judgment. Cross-regional validation is required.

PMID:
42508148
Bibliographic data and abstract were imported from PubMed on 28 Jul 2026.

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