Predicting Mortality After Percutaneous Coronary Intervention in a Multiethnic Southeast Asian Population: Insights From Machine Learning.
YM, L., YK, C., PL, N., HY, T., NA, M.S., LK, T., WA, W.A., & KH, C. (2026). Predicting Mortality After Percutaneous Coronary Intervention in a Multiethnic Southeast Asian Population: Insights From Machine Learning.. Journal of cardiovascular translational research. https://doi.org/10.1007/s12265-026-10812-5
YM L, YK C, PL N, HY T, NA MS, LK T, et al. Predicting Mortality After Percutaneous Coronary Intervention in a Multiethnic Southeast Asian Population: Insights From Machine Learning.. Journal of cardiovascular translational research. 2026; doi: 10.1007/s12265-026-10812-5
YM L, YK C, PL N, et al. Predicting Mortality After Percutaneous Coronary Intervention in a Multiethnic Southeast Asian Population: Insights From Machine Learning.[J]. Journal of cardiovascular translational research. 2026. DOI: 10.1007/s12265-026-10812-5.
@article{ym2026,
author = {Liew YM and Chiam YK and Ngo PL and Tan HY and Md Sari NA and Tan LK and Wan Ahmad WA and Chee KH},
title = {Predicting Mortality After Percutaneous Coronary Intervention in a Multiethnic Southeast Asian Population: Insights From Machine Learning.},
journal = {Journal of cardiovascular translational research},
year = {2026},
doi = {10.1007/s12265-026-10812-5},
note = {PMID: 42410287},
}
TY - JOUR AU - Liew YM AU - Chiam YK AU - Ngo PL AU - Tan HY AU - Md Sari NA AU - Tan LK AU - Wan Ahmad WA AU - Chee KH TI - Predicting Mortality After Percutaneous Coronary Intervention in a Multiethnic Southeast Asian Population: Insights From Machine Learning. T2 - Journal of cardiovascular translational research PY - 2026 DO - 10.1007/s12265-026-10812-5 AN - PMID:42410287 ER -
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.