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
Machine learning prediction of outcome following pulsed-field atrial fibrillation ablation: patient selection and risk factors.
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Anselmino Matteo, Bianchi Stefano, De Lucia Raffele, Tondo Claudio, Iacopino Saverio, Solimene Francesco, Rossillo Antonio, Bertini Matteo, Themistoclakis Sakis, Meynet Ilaria, Russo Maurizio, Dello Russo Antonio, Zingarini Gianluca, Bandino Stefano, Volpicelli Mario, Rossi Pietro, Bianchini Lorenzo, Schillaci Vincenzo, De Simone Antonio, Scaglione Marco, Rovaris Giovanni, De Ferrari Gaetano Maria, Malacrida Maurizio, Saglietto Andrea, Zucchelli Giulio
Аннотация
BACKGROUND: Despite 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.
OBJECTIVE: To integrate machine learning (ML) into an outcome prediction model based on a large cohort of AF patients undergoing pulsed field ablation (PFA).
METHODS: Consecutive 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.
RESULTS: The 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.
CONCLUSION: This 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 REGISTRATION: Advanced TecHnologies For SuccEssful AblatioN of AF in Clinical Practice (ATHENA). URL: http://clinicaltrials.gov/ Identifier: NCT05617456.
OBJECTIVE: To integrate machine learning (ML) into an outcome prediction model based on a large cohort of AF patients undergoing pulsed field ablation (PFA).
METHODS: Consecutive 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.
RESULTS: The 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.
CONCLUSION: This 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 REGISTRATION: Advanced TecHnologies For SuccEssful AblatioN of AF in Clinical Practice (ATHENA). URL: http://clinicaltrials.gov/ Identifier: NCT05617456.
Предметные рубрики MeSH
HumansAtrial FibrillationFemaleRisk FactorsMalePredictive Learning ModelsMachine LearningTreatment OutcomeMiddle AgedPatient SelectionAgedCatheter AblationRisk AssessmentRecurrenceRegistriesProspective StudiesRandom ForestPrediction AlgorithmsClassification AlgorithmsIrreversible Electroporation Therapy