Prediction of New-Onset Atrial Fibrillation in Patients with Acute Myocardial Infarction: A Machine Learning Approach

Background/Objectives: New-onset atrial fibrillation is a common complication of acute myocardial infarction and is associated with increased mortality, heart failure, and recurrent cardiovascular events. Prediction models for the occurrence of atrial fibrillation may facilitate early risk stratification, improve patient monitoring, and support personalized therapeutic decision-making in clinical practice. Methods: A prospective study included 150 patients with acute myocardial infarction admitted within 24 h of symptom onset. Clinical, laboratory, electrocardiographic, and echocardiographic data were collected, and a machine learning model was developed to predict new-onset atrial fibrillation. Model performance was evaluated using ROC-AUC, while SHAP analysis was applied to identify and quantify the contribution of individual predictors. Results: The machine learning model demonstrated excellent predictive performance, achieving an accuracy of 97.0%, ROC-AUC of 0.991, and precision–recall AUC of 0.991 in the independent test set. SHapley Additive Explanations (SHAP) and permutation importance analyses identified the E/e’ ratio, left ventricular mass index, left ventricular ejection fraction, left atrial volume index, oxidative stress markers (malondialdehyde and superoxide dismutase), NT-proBNP, and complete revascularization as the most influential predictors of new-onset atrial fibrillation after acute myocardial infarction. Conclusions: Machine learning combined with SHAP-based explainability showed high potential for predicting new-onset atrial fibrillation after acute myocardial infarction. This approach may improve individualized risk assessment and clinical decision-making, pending validation in larger multicenter cohorts.