Phenotypic clustering of atrial fibrillation patients using latent class analysis and its potential utility for ablation strategy: insights from the DIRECT-Extend and EARNEST-PVI studies.

Abstract

Aims: AF is a heterogeneous syndrome. We aimed to assess whether latent class analysis (LCA) can identify phenotypes that stratify prognosis and predict differential responses to catheter ablation strategies.

Methods and results: We first developed an LCA-based phenotyping model using the DIRECT-Extend registry (N = 7512), a pooled AF cohort of patients receiving anticoagulation therapy. Patients with echocardiographic data (N = 2741) were classified into latent clusters based on 16 clinical, laboratory, and echocardiographic variables. This phenotypic classification was then applied to the randomized Effect of Extensive Ablation on Recurrence in Patients with Persistent AF Treated with Pulmonary Vein Isolation (EARNEST-PVI) trial to evaluate whether the effectiveness of ablation strategies-pulmonary vein isolation (PVI) alone vs. PVI plus extensive ablation (PVI-plus)-varied across phenotypes. In the DIRECT-Extend registry, three phenotypes were identified: Phenotype 1 (N = 1565; 'low comorbidity'), Phenotype 2 (N = 981; 'cardiomyopathy & chronic kidney disease'), and Phenotype 3 (N = 195; 'inflammatory-enriched AF'). Over a median of 600 days, the primary endpoint-a composite of all-cause death, acute myocardial infarction, any stroke, and major bleeding differed across phenotypes (log-rank P < 0.001). In EARNEST-PVI, patients were stratified into Phenotype 1 (N = 328), Phenotype 2 (N = 139), and Phenotype 3 (N = 30) according to the classification model. AF recurrence was significantly lower in the PVI-plus group compared to the PVI-alone group in Phenotype 1 (log-rank P = 0.013), whereas there was no significant difference in Phenotypes 2 and 3.

Conclusion: LCA identified distinct AF phenotypes associated with prognoses. These phenotypes may also provide insights into heterogeneity in response to ablation strategies. This phenotyping approach may support risk stratification and individualized AF management.