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.
Can machine learning models accurately predict in-hospital, 30-day, and 1-year mortality in patients undergoing percutaneous coronary intervention?
Population
29,521 patients from a nationwide registry in Malaysia
Comparison
Seven machine learning models for predicting mortality
Design
Nationwide registry-based prognostic model development and validation study
Follow-up
1-year
Key result
Machine learning models demonstrated high discrimination for predicting in-hospital mortality after non-elective PCI, with ROC-AUC ranging from 0.927 to 0.943 in external validation.
Authors
Loading...
ML models may aid ACS PCI mortality risk stratification; leaves open clinical utility pending prospective validation.
Cohort (n=29,521)
Yes
Can machine learning models accurately predict in-hospital, 30-day, and 1-year mortality in patients undergoing percutaneous coronary intervention?
Effect estimate: ROC-AUC 0.927 to 0.943 (in-hospital, TEST1)
Machine learning models can accurately predict short- and long-term mortality following PCI in a multiethnic Southeast Asian population, identifying age, hemodynamic status, and renal function as key predictors.
Liew et al. (2026) conducted a cohort in Ischemic heart disease / Acute coronary syndrome (n=29,521). Machine learning (ML) models was evaluated on In-hospital, 30-day, and 1-year mortality (ROC-AUC 0.927 to 0.943 (in-hospital, TEST1)). Machine learning models demonstrated high discrimination for predicting in-hospital mortality after non-elective PCI, with ROC-AUC ranging from 0.927 to 0.943 in external validation.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: