Comparison of machine-learning models for the prediction of 1-year adverse outcomes of patients undergoing primary percutaneous coronary intervention for acute ST-elevation myocardial infarction

Abstract

Background: Acute ST-elevation myocardial infarction (STEMI) is a leading cause of mortality and morbidity worldwide, and primary percutaneous coronary intervention (PCI) is the preferred treatment option. Hypothesis: Machine learning (ML) models have the potential to predict adverse clinical outcomes in STEMI patients treated with primary PCI. However, the comparative performance of different ML models for this purpose is unclear. Methods: This study used a retrospective registry-based design to recruit consecutive hospitalized patients diagnosed with acute STEMI and treated with primary PCI from 2011 to 2019, at Tehran Heart Center, Tehran, Iran. Four ML models, namely Gradient Boosting Machine (GBM), Distributed Random Forest (DRF), Logistic Regression (LR), and Deep Learning (DL), were used to predict major adverse cardiovascular events (MACE) during 1-year follow-up. Results: A total of 4514 patients (3498 men and 1016 women) were enrolled, with MACE occurring in 610 (13.5%) subjects during follow-up. The mean age of the population was 62.1 years, and the MACE group was significantly older than the non-MACE group (66.2 vs. 61.5 years, p