To develop a machine learning (ML)-based risk prediction model for 1-year mortality in ST-elevation myocardial infarction (STEMI) patients undergoing primary or rescue percutaneous coronary intervention.

Patient data, including demographic, clinical, biochemical, imaging and procedural details, were extracted from electronic medical records. Data were split into training (80%) and test (20%) sets. Eight supervised learning algorithms were evaluated: least absolute shrinkage and selection operator, ridge, Elastic Net (EN, decision tree, support vector machine, random forest, AdaBoost and gradient boosting. Feature selection was performed sequentially with subsets of the top 5/10/15/20/25/30 features. Model hyperparameters were optimised using fivefold cross-validation with area under the curve (AUC) as the scoring metric.

Single, tertiary Australian centre.

We analysed data from 1863 consecutive STEMI patients treated at a tertiary Australian centre from July 2010 to December 2019.

The primary outcome was 1-year all-cause mortality.

The 1-year mortality rate was 13.6% (n=254) in our cohort. The EN model with five key features (parsimonious model) demonstrated superior performance, achieving an AUC of 0.821, which was comparable to the full 30-variable model (AUC 0.821). Advanced age, pre-hospital cardiac arrest and management with balloon angioplasty alone were identified as predictors of increased mortality risk, while family history of premature coronary disease and higher left ventricular ejection fraction were associated with improved survival. To facilitate clinical implementation, we developed a user-friendly web application for individualised risk assessment.

Our ML model accurately predicts 1-year mortality in STEMI patients using only five clinical variables. This tool offers improved accuracy and ease of use compared with existing risk stratification methods, potentially enhancing patient stratification and guiding treatment decisions in STEMI management.