Arrhythmias & electrophysiology
Machine learning prediction of outcome following pulsed-field atrial fibrillation ablation: patient selection and risk factors
Authors' conclusion
This is the first ML model exclusively trained and validated on AF patients undergoing PFA providing actionable insights for personalized treatment planning.
Study context
- Population
- Consecutive atrial fibrillation (AF) patients undergoing transcatheter pulsed field ablation (PFA) (FARAWAVE catheter), ATHENA registry, June 2022–December 2024
- Participants
- 1,688 patients
- Outcome
- Arrhythmic recurrence (prediction by ML models)
- Follow-up
- 1 year
Methodological detailsmachine learning, consecutive patients
- Machine learning
- Consecutive patients
Performance
As reported in the abstract
Prediction of 1-year arrhythmic recurrence
| Test or model | AUC |
|---|---|
| Random Forest | 0.75 (95% CI 0.69-0.82) |
Random Forest
AUC0.75 (95% CI 0.69-0.82)
- Arrhythmic recurrence
- 314 patients (18.6%)
Original publication
Machine learning prediction of outcome following pulsed-field atrial fibrillation ablation: patient selection and risk factors. Europace. Published 15 May 2026. doi:10.
Composed by AI from the published abstract and checked against it. How Briefs are made →