Predicting patient-related outcomes after atrial fibrillation ablation: insights from explainable artificial intelligence and digital health

Abstract

Aims Quality of life (QoL) improvement is a primary driver for atrial fibrillation (AF) catheter ablation (CA), yet its determinants remain unclear. We aimed to identify patient phenotypes with distinct post-ablation QoL trajectories, determine their key predictors, and clarify their association with arrhythmia recurrence and reintervention. Methods and results We prospectively followed 213 patients (median age 60 years, 31% female) undergoing AF CA at a tertiary hospital for 2.2 years [interquartile range (IQR): 1.6-2.6]. A digital health application collected real-time electronic patient-reported outcomes (PROs), including the AF Effect on QoL (AFEQT) questionnaire. Reference charts were generated from QoL trajectories of recurrence-free patients. Machine learning (ML) identified subgroups with distinct QoL trajectories, and explainable artificial intelligence (AI) highlighted key predictors. Quality of life improved by +26 AFEQT points [95% confidence interval (CI): 18-33] within 3 months post-ablation and remained stable thereafter, despite significant heterogeneity in individual responses. Patients with AF recurrence showed significantly lower QoL gains (P = 0.010). Machine learning identified three phenotypes: a younger cluster with the largest QoL improvements, an emotive cluster with higher recurrence rates and minimal QoL benefits despite additional antiarrhythmic reinterventions, and an older cluster with established cardiovascular risk factors. Anxiety, age, and AF duration emerged as key discriminators. Conclusion ML defined three clinically coherent phenotypes, each exhibiting distinct QoL trajectories and ablation outcomes. Explainable AI clarified how individual psychological and biological traits interact to shape these outcomes, highlighting the potential for tailored multidisciplinary care beyond individualized rhythm control strategies.