Key result
High-risk phenotype linked to ~71% higher atrial arrhythmia recurrence post-ablation vs lower-risk phenotype.
Why the study?
Catheter ablation of persistent atrial fibrillation yields suboptimal success rates partly due to patient heterogeneity, and identifying distinct phenotypes could improve patient selection and treatment strategies.
Does machine learning-driven cluster analysis identify distinct phenotypes with different risks of atrial arrhythmia recurrence in patients undergoing catheter ablation for persistent atrial fibrillation?
Observational (n=815)
Yes
Does machine learning-driven cluster analysis identify distinct phenotypes with different risks of atrial arrhythmia recurrence in patients undergoing catheter ablation for persistent atrial fibrillation?
Hazard Ratio: 1.71
Absolute Event Rate: 51.7% vs 35%
p-value: p=<0.0001
Machine learning cluster analysis successfully identified a high-risk patient phenotype—characterized by older age, high left atrial fibrosis, elevated BMI, and increased left atrial volume—that is significantly more likely to experience procedural failure and arrhythmia recurrence after catheter ablation for persistent atrial fibrillation.
May support post-ablation risk stratification in persistent AF but should not change practice; leaves open need for prospective validation of phenotype-specific strategies.
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.
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Noujaim et al. (2024) conducted an observational in Persistent atrial fibrillation (n=815). High-risk phenotype (Cluster 1) vs. Lower-risk phenotype (Cluster 2) was evaluated on Atrial arrhythmia recurrence (HR 1.71, p=<0.0001). A high-risk phenotype (Cluster 1) was associated with increased atrial arrhythmia recurrence after catheter ablation compared to Cluster 2 (51.7% vs 35.0%; HR 1.71, p<0.0001).
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