Atrial fibrillation phenotypes derived from multimodal data exhibit distinct electrophysiological substrate characteristics, symptom trajectories, and ablation outcomes

Abstract

The heterogeneity in response to catheter ablation amongst atrial fibrillation (AF) patients classified according to arrhythmia duration highlights the need for improved phenotyping. Multimodal assessment may better characterise the spectrum of pathophysiology present amongst patients classified as having paroxysmal or persistent AF. To perform comprehensive multimodal pre-ablation phenotyping in patients undergoing AF ablation and evaluate associations with post-ablation arrhythmia recurrence, symptom burden, and bi-atrial substrate remodelling. In this multicentre prospective cohort study, 86 patients referred for first-time AF ablation (42% persistent AF) underwent pre-ablation phenotyping beyond usual care, incorporating patch-based ambulatory rhythm monitoring, atrial structural and functional MRI assessment, and formal symptom evaluation. Unsupervised K-means clustering was used to derive phenotypes. The primary outcome was 12-month arrhythmia recurrence; secondary analyses assessed symptom trajectories and bi-atrial substrate characteristics. Four AF phenotypes were identified. Arrhythmia recurrence occurred in 56.0%, 42.9%, 22.2%, and 19.0% of patients in Phenotypes 1–4, respectively (log-rank p=0.032). Symptom trajectories differed across phenotypes, with the greatest improvements observed in Phenotypes 3 and 4. Exploratory electroanatomic mapping analyses demonstrated significant between-phenotype differences in bi-atrial bipolar voltage aligned with clinical trajectories. Importantly, phenotypes were not fully explained by conventional classification: Phenotype 2 experienced relatively high recurrence despite comprising predominantly paroxysmal AF patients whilst Phenotype 3 demonstrated favourable outcomes despite a high prevalence of persistent AF. Multimodal phenotyping identified distinct AF subgroups associated with differences in post-ablation recurrence, symptom trajectories, and electrophysiological substrate. This approach could improve pre-ablation risk stratification and shared decision-making.