ArticleEuropace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology2023
AFA-Recur: an ESC EORP AFA-LT registry machine-learning web calculator predicting atrial fibrillation recurrence after ablation.
Article in Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers, 2 of them syntheses that pooled it.
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Who cites it
41 citing papers in PubMed, 2 syntheses or guidelines pooled it, 52 citations in OpenAlex.
- A Systematic Review on the Effectiveness of Machine Learning in the Detection of Atrial Fibrillation.Current cardiology reviews · 2025Pooled it
- Predictive Value of Machine Learning for Recurrence of Atrial Fibrillation after Catheter Ablation: A Systematic Review and Meta-Analysis.Reviews in cardiovascular medicine · 2023Pooled it
- Review
- Prognostic Value of Right Ventricular Performance and Left Atrial Mechanical Efficiency in Paroxysmal Atrial Fibrillation.Journal of cardiovascular development and disease · 2026Article
- Machine learning prediction of outcome following pulsed-field atrial fibrillation ablation: patient selection and risk factors.Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology · 2026Article
- AI-driven voltage map analysis for optimizing catheter ablation strategy in atrial fibrillation: a proof-of-concept study.European heart journal. Digital health · 2026Article
- Evaluating a digital decision aid for atrial fibrillation rhythm control in a hybrid implementation-effectiveness trial.NPJ digital medicine · 2026Article
- Repeat ablation strategies in atrial fibrillation patients with durably isolated pulmonary veins: insights from the Netherlands Heart Registration.Netherlands heart journal : monthly journal of the Netherlands Society of Cardiology and the Netherlands Heart Foundation · 2026Article
- Comparative study of 11 machine learning algorithms for predicting recurrence risk after atrial fibrillation catheter ablation based on a real-world cohort: a retrospective study.BMC cardiovascular disorders · 2026Article
- Development and Validation of an Explainable Machine Learning Model for Predicting Repeat Catheter Ablation for Atrial Fibrillation: A Single-Center Retrospective Cohort Study.International journal of general medicine · 2026Article
- A paradigm shift toward full-cycle management of atrial fibrillation: integrating digital twins and artificial intelligence.Frontiers in cardiovascular medicine · 2026Review
- Managing Atrial Fibrillation in Heart Failure: In Whom, When, and How?International journal of heart failure · 2026Review
- Accurate estimation of lesion metrics in radiofrequency ablation using machine learning model.European heart journal open · 2026Article
- Brazilian Atrial Fibrillation Guidelines - 2025.Arquivos brasileiros de cardiologia · 2025Article
- Artificial Intelligence in Cardiac Electrophysiology: A Clinically Oriented Review with Engineering Primers.Bioengineering (Basel, Switzerland) · 2025Review
- Implementation and Updating of Clinical Prediction Models: A Systematic Review.Mayo Clinic proceedings. Digital health · 2025Review
- An online explainable ensemble machine learning model for predicting epidermal growth factor receptor mutation status in lung adenocarcinoma.Translational lung cancer research · 2025Article
- Predicting Atrial Fibrillation Relapse Using Bayesian Networks: Explainable AI Approach.JMIR cardio · 2025Article
- Machine Learning in the Management of Patients Undergoing Catheter Ablation for Atrial Fibrillation: Scoping Review.Journal of medical Internet research · 2025Article
- Comparing Phenotypes for Acute and Long-Term Response to Atrial Fibrillation Ablation Using Machine Learning.Circulation. Arrhythmia and electrophysiology · 2025Article
Corrections and comments
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Authors and funding
11 authors at 8 institutions in 6 countries.
Funding
Abstract
aimsAtrial 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
resultsPatients 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/.
conclusionAFA-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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.