Machine Learning-Driven Identification of Distinct Persistent Atrial Fibrillation Phenotypes: A Cluster Analysis of DECAAF II.

INTRODUCTION

Catheter ablation of persistent atrial fibrillation yields sub-optimal success rates partly due to the considerable heterogeneity within the patient population. Identifying distinct patient phenotypes based on post-ablation prognosis could improve patient selection for additional therapies and optimize treatment strategies.

METHODS

We studied all patients who underwent catheter ablation of persistent atrial fibrillation in the DECAAF II trial. Out of 44 participating centers, 25% were randomly chosen as a validation set. A Gradient Boosting Method determined essential features for arrhythmia recurrence prediction and the number of clusters was determined according to the average silhouette width. K-medoids cluster analysis identified subgroups based on these features, and Kaplan-Meier curves were further compared among different clusters.

RESULTS

Among 815 patients, 570 served as a training set and 245 as a validation set. Using the training set, the GBM model achieved an AUC of 0.874. K-medoids cluster analysis used LA volume, BMI, baseline fibrosis, and age, resulting in two clusters. Cluster 1 patients were older, had higher baseline fibrosis, higher BMI, and greater LA volume compared to Cluster 2. Atrial arrhythmia recurrence rates were significantly higher in Cluster 1 (51.7% vs. 35.0%, p = 0.0002), and survival analysis showed a significant difference in primary recurrence outcomes (HR = 1.71, p < 0.0001). The validation set confirmed these findings.

CONCLUSION

Utilizing machine learning, we identified a high-risk cluster for procedural failure in catheter ablation of persistent atrial fibrillation within the DECAAF II trial population. The primary differentiating factors of this high-risk cluster include older age, high left atrial fibrosis, elevated BMI, and increased left atrial volume.