Predicting long-term major adverse cardiac events after percutaneous coronary intervention in individuals with acute coronary syndrome and nonvalvular atrial fibrillation: a machine learning study

This study was designed to identify risk factors for long-term major adverse cardiac events (MACE) in individuals with high-risk acute coronary syndrome (ACS) and concomitant nonvalvular atrial fibrillation (NVAF) after percutaneous coronary intervention (PCI), and to present these findings in a visual predictive format. The least absolute shrinkage and selection operator (LASSO) regression was applied to identify predictors of long-term MACE. Using these predictors, three models were developed: