AIMS: Atrial fibrillation (AF) recurrence during the first year after catheter ablation remains common. Patient-specific prediction of arrhythmic recurrence would improve patient selection, and, potentially, avoid futile interventions. Available prediction algorithms, however, achieve unsatisfactory performance. Aim of the present study was to derive from ESC-EHRA Atrial Fibrillation Ablation Long-Term Registry (AFA-LT) a machine-learning scoring system based on pre-procedural, easily accessible clinical variables to predict the probability of 1-year arrhythmic recurrence after catheter ablation. METHODS AND RESULTS: Patients were randomly split into a training (80%) and a testing cohort (20%). Four different supervised machine-learning models (decision tree, random forest, AdaBoost, and k-nearest neighbour) were developed on the training cohort and hyperparameters were tuned using 10-fold cross validation. The model with the best discriminative performance on the testing cohort (area under the curve-AUC) was selected and underwent further optimization, including re-calibration. A total of 3128 patients were included. The random forest model showed the best performance on the testing cohort; a 19-variable version achieved good discriminative performance [AUC 0.721, 95% confidence interval (CI) 0.680-0.764], outperforming existing scores (e.g. APPLE score: AUC 0.557, 95% CI 0.506-0.607). Platt scaling was used to calibrate the model. The final calibrated model was implemented in a web calculator, freely available at http://afarec.hpc4ai.unito.it/. CONCLUSION: AFA-Recur, a machine-learning-based probability score predicting 1-year risk of recurrent atrial arrhythmia after AF ablation, achieved good predictive performance, significantly better than currently available tools. The calculator, freely available online, allows patient-specific predictions, favouring tailored therapeutic approaches for the individual patient.

ANMCO Research Centre Florence Italy

Cardiology Unit J Medical Turin Italy

Cardiovascular Department Maria Cecilia Hospital GVM Care and Research Cotignola Italy

Centro de Investigación Biomédica en Red de Enfermedades Cardiovasculares Madrid Spain

Department of Cardiology Cardiovascular Institut Hospital Clinic de Barcelona Universitat de Barcelona Barcelona Spain

Department of Cardiology Institute for Clinical and Experimental Medicine Prague Czech Republic

Department of Electrophysiology Heart Center Leipzig at University of Leipzig Leipzig Germany

Department of Medical Science and Cardiology Uppsala University Uppsala Sweden

Division of Cardiology Department of Medical Sciences 'Città della Salute e della Scienza di Torino' Hospital University of Turin Turin Italy

EURObservational Research Programme European Society of Cardiology Sophia Antipolis France

Hospital Clínic Pediatric Arrhythmia Unit Cardiovascular Institute Hospital Sant Joan de Déu University of Barcelona Barcelona Spain

Institut d'Investigació August Pi iSunyer Barcelona Spain

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A perspective on genetic and polygenic risk scores-advances and limitations and overview of associated tools