Machine Learning-Predicted Progression to Permanent Atrial Fibrillation After Catheter Ablation

We developed a prediction model for atrial fibrillation (AF) progression and tested whether machine learning (ML) could reproduce the prediction power in an independent cohort using pre-procedural non-invasive variables alone. Cohort 1 included 1,214 patients and cohort 2, 658, and all underwent AF catheter ablation (AFCA). AF progression to permanent AF was defined as sustained AF despite repeat AFCA or cardioversion under antiarrhythmic drugs. We developed a risk stratification model for AF progression (STAAR score) and stratified cohort 1 into three groups. We also developed an ML-prediction model to classify three STAAR risk groups without invasive parameters and validated the risk score in cohort 2. The STAAR score consisted of a stroke (2 points, = 0.003), persistent AF (1 point, = 0.049), left atrial (LA) dimension ≥43 mm (1 point, = 0.010), LA voltage