Predicting Mortality After Percutaneous Coronary Intervention in a Multiethnic Southeast Asian Population: Insights From Machine Learning.

Authors

Yih Miin Liew, Yin Kia Chiam, Pei Ling Ngo, Hui Yee Tan, Nor Ashikin Md Sari, Li Kuo Tan, Wan Azman Wan Ahmad, Kok Han Chee

Published in

Journal of cardiovascular translational research. Volume 19. Issue 1. Jul 06, 2026. Epub Jul 06, 2026.

Abstract

Ischemic heart disease remains a major contributor to mortality in Malaysia, with non-elective percutaneous coronary intervention (PCI) frequently performed in high-risk acute coronary syndrome (ACS) patients. Using nationwide registry data (2007-2020), we evaluated 29,521 patients and compared seven machine learning (ML) models for predicting in-hospital, 30-day, and 1-year mortality. Models were developed in a training cohort and externally validated using hospital-level (TEST1) and prospective temporal (TEST2) cohorts. After logistic recalibration, discrimination for in-hospital mortality ranged from 0.927 to 0.943 (TEST1) and 0.865-0.884 (TEST2). For 30-day mortality, ROC-AUC ranged from 0.902 to 0.923 (TEST1) and 0.753-0.838 (TEST2), and for 1-year mortality from 0.833 to 0.859 (TEST1) and 0.750-0.801 (TEST2). Calibration remained acceptable, and decision curve analysis demonstrated positive net benefit across clinically relevant thresholds. Cross-model stability analysis consistently identified age, haemodynamic status, and renal function as key predictors.

PMID:
42410287
Bibliographic data and abstract were imported from PubMed on 07 Jul 2026.

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