Prediction of in-hospital atrial fibrillation after acute myocardial infarction: a cross-cohort study in Italian and Finnish patients.

Abstract

The development of atrial fibrillation (AF) during hospitalization for acute myocardial infarction (AMI) is common and associated with worse prognosis. We aimed to train and, for the first time, cross-nationally validate a machine learning model to predict AF developing during the cardiac intensive care unit (CICU) stay in post-AMI patients admitted in sinus rhythm, using only routine, non-electrocardiographic clinical parameters available at CICU admission. We developed and tested classification pipelines on two independent cohorts of post-AMI patients from Italy (ITA, Sinus Rhythm = 2,200, AF = 240) and Finland (FIN, Sinus Rhythm = 3,631, AF = 452). Models were first evaluated via nested cross-validation on held out samples of the training cohort (internal testing) and then cross-cohort (external testing). An L2-regularized logistic regression model performed optimally in each case, achieving comparable performances in internal (AUROC: 0.740 ITA, 0.755 FIN) and external (AUROC: 0.735 ITA-on-FIN, 0.723 FIN-on-ITA) testing. A combined-cohort model achieved an AUROC of 0.749. A posteriori feature relevance analysis confirmed the predictive value of established risk factors including advanced age, lower left ventricular ejection fraction and hypertension in both cohorts. Ongoing aspirin therapy and non-ST-elevation myocardial infarction emerged as additional protective factors exclusive to cohort ITA, whereas a higher high-sensitivity C-reactive protein (risk factor) and the current smoking status (protective factor) were specific to cohort FIN. Our study demonstrates that machine learning models using routinely collected clinical parameters could help predict in-hospital AF in post-AMI patients across different populations and national healthcare systems. Notably, a simple and fully interpretable logistic regression model achieved robust performance in the first cross-national validation for this prediction task, suggesting that more complex, black-box approaches may offer limited additional benefit for this specific task. Further improvements are needed to reach real-world application in clinical contexts for early risk stratification.