Key result
Machine learning models accurately predict short-term mortality after non-elective PCI with ~0.93 AUC.
Why the study?
Ischemic heart disease remains a major contributor to mortality in Malaysia, where non-elective PCI is frequently performed in high-risk ACS patients.
Observational (n=29,521)
Yes
Answers are grounded in this paper and cite their sources.
Machine learning models demonstrated strong discrimination and positive net clinical benefit for predicting short-term and 1-year mortality after non-elective PCI for ACS in a multiethnic Southeast Asian population.
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.
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Liew et al. (2026) conducted an observational in Acute coronary syndrome (ACS) (n=29,521). Machine learning mortality prediction models was evaluated on In-hospital, 30-day, and 1-year all-cause mortality. Seven machine learning models demonstrated strong discrimination for predicting in-hospital mortality (ROC-AUC 0.927-0.943) and 30-day mortality (ROC-AUC 0.902-0.923) following non-elective PCI.
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