Abstract
Identifying patients who would benefit from extensive catheter ablation along with pulmonary vein isolation (PVI) among those with persistent atrial fibrillation (AF) has been a subject of controversy. The objective of this study was to apply uplift modeling, a machine learning method for analyzing individual causal effect, to identify such patients in the EARNEST-PVI trial, a randomized trial in patients with persistent AF. We developed 16 uplift models using different machine learning algorithms, and determined that the best performing model was adaptive boosting using Qini coefficients. The optimal uplift score threshold was 0.0124. Among patients with an uplift score ≥ 0.0124, those who underwent extensive catheter ablation (PVI-plus) showed a significantly lower recurrence rate of AF compared to those who received only PVI (PVI-alone) (HR 0.40; 95% CI 0.19-0.84; P-value = 0.015). In contrast, among patients with an uplift score < 0.0124, recurrence of AF did not significantly differ between PVI-plus and PVI-alone (HR 1.17; 95% CI 0.57-2.39; P-value = 0.661). By employing uplift modeling, we could effectively identify a subset of patients with persistent AF who would benefit from PVI-plus. This model could be valuable in stratifying patients with persistent AF who need extensive catheter ablation before the procedure.