Machine learning model for predicting late recurrence of atrial fibrillation after catheter ablation

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Abstract Late recurrence of atrial fibrillation (LRAF) during the first year after catheter ablation is a common and significant clinical problem. Our study aimed to create a machine-learning model for predicting 1-year arrhythmic recurrence after catheter ablation. The study comprised 201 consecutive patients (age: 61.8±8.1; women 36%) with paroxysmal, persistent, and long-standing persistent atrial fibrillation (AF) who underwent cryoballoon (61%) and radiofrequency ablation (39%). Five different supervised machine-learning models (decision tree, logistic regression, random forest, XGBoost, support vector machines) were developed for predicting 1-year AF recurrence after catheter ablation. Next, SHapley Additive exPlanations (SHAP) were derived to explain the predictions using 82 parameters from clinical, laboratory, and procedural variables collected from each patient. The models were trained and validated using stratified 5-fold cross-validation, and feature selection was performed with permutation importance. The XGBoost model with 12 variables showed the best performance on the testing cohort, with the highest AUC of 0.75 [95% confidence interval 0.7395, 0.7653]. The machine-learned model, based on the easily available 12 clinical and laboratory data, predicted the late recurrence of AF up to 1 year after catheter ablation with good performance, which may provide a valuable tool in clinical practice for better patient selection and personalized atrial fibrillation strategy after the procedure.

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