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 Cardiology2026
Machine learning prediction of outcome following pulsed-field atrial fibrillation ablation: patient selection and risk factors.
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, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05617456 (Advanced TecHnologies For SuccEssful AblatioN of AF in Clinical Practice), which is not on this map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Advanced TecHnologies For SuccEssful AblatioN of AF in Clinical Practice: ATHENA Study
Who cites it
1 citing paper in PubMed.
- Artificial intelligence for patient selection in pulsed-field ablation: promise, pragmatism, and the need for standards.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
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Authors and funding
25 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDespite being the most effective option for maintaining sinus rhythm, atrial fibrillation (AF) catheter ablation reaches few patients. For this reason, identifying candidates with the highest likelihood of success or individualizing counselling to a specific patient to improve procedural outcome could enhance clinical benefits and cost-effectiveness.
objectiveTo integrate machine learning (ML) into an outcome prediction model based on a large cohort of AF patients undergoing pulsed field ablation (PFA).
methodsConsecutive AF patients undergoing transcatheter PFA between June 2022 and December 2024 were prospectively enrolled in the ATHENA registry. All procedures were performed with a penta-splines 12F over-the-wire PFA catheter (FARAWAVE™, Boston Scientific). Clinical and procedural variables were collected to train five predictive models estimating 1 year arrhythmic recurrence; model interpretability was assessed using SHAP (SHapley Additive exPlanations) analysis.
resultsThe study included 1688 AF patients with a median follow-up of 365 days (interquartile range 202-393), arrhythmic recurrence occurred in 314 patients (18.6%). The Boruta algorithm identified diagnosis-to-ablation time (DAT), CHA₂DS₂-VASc score, age, and body mass index (BMI) as most significant predictors. Among the five ML models developed to predict 1 year arrhythmic recurrence probability, Random Forest achieved the best performance (AUC = 0.75, 95% CI 0.69-0.82). SHAP analysis confirmed DAT, BMI, and indexed left atrial volume as major contributors to recurrence.
conclusionThis is the first ML model exclusively trained and validated on AF patients undergoing PFA providing actionable insights for personalized treatment planning. Routine use of the model holds the potential to optimize patient selection and improve procedural outcome, supporting individualized counselling and outcome-driven care pathways, moving from static to interactive risk prediction. CLINICAL
trial registrationAdvanced TecHnologies For SuccEssful AblatioN of AF in Clinical Practice (ATHENA). URL: http://clinicaltrials.gov/ Identifier: NCT05617456.
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