STEMI-OP in-hospital mortality prediction algorithms: Frailty-integrated machine learning in older patients undergoing primary PCI.

ABSTRACT: Despite advances in medical care, older patients with ST-elevation myocardial infarction (STEMI) undergoing primary percutaneous coronary intervention (PCI) currently face high in-hospital mortality rates. Traditional prognostic models, primarily developed in Caucasian populations with fewer older participants and using classical statistical approaches, may not perform well in Southeast Asian settings. This study explores the need for artificial intelligence-based risk assessment models-the STEMI-OP algorithms-designed explicitly for STEMI patients aged 60 and older following primary PCI in Vietnam. Machine learning (ML) models were developed and validated using pre- and post-PCI features, with advanced feature selection techniques to identify key predictors. SHapley Additive exPlanations

SUBMITTER: Nguyen TV 

PROVIDER: S-EPMC12144145 | biostudies-literature | 2025 Jun

REPOSITORIES: biostudies-literature