Premature ventricular contraction–mediated ventricular fibrillation: Clinical characteristics, application of machine-learning algorithm, and outcomes of catheter ablation—A case series

Background: Although premature ventricular contractions (PVCs) are commonly seen, in some patients, PVCs are associated with malignant ventricular arrhythmias including ventricular fibrillation (VF). Objective: This study aimed to assess electrocardiographic (ECG) characteristics and apply a machine-learning (ML) algorithm in patients with PVC-triggered VF. Methods: We analyzed data from an international cohort of 82 patients undergoing ablation for PVCs (41 with PVC-triggered VF and 41 controls). We evaluated the prevalence of ECG characteristics in patients with PVC-triggered VF, including (1) early repolarization (ER) in inferior/lateral leads and (2) QRS notching of the sinus beat or PVC, and also applied an ML algorithm to assess the differences in the 2 patient cohorts. Results: In 41 patients with PVC-triggered VF, there was a median of 8 implantable cardioverter-defibrillator shocks per patient before PVC ablation. The mean coupling interval of the PVC to the antecedent sinus beat was 313 ± 130 ms. Compared with controls, ER (39% vs 17%) and QRS notching (71% vs 24%) were significantly more prevalent in the PVC-triggered VF group. After a median of 1 ablation (interquartile range 1-3), 82% of patients remained free of ventricular tachycardia/VF and implantable cardioverter-defibrillator shocks over a median follow-up of 400 days (90-2490). The ML ECG algorithm demonstrated reasonable discrimination between the 2 groups (area under the receiver-operating characteristic curve 0.85 [0.56-1.0]). Anterior ST-segment deviation and left bundle branch-like delay were salient contributors to ML prediction. Conclusion: In patients with PVC-triggered VF, ER and QRS notching were more prevalent than in patients with PVC without VF. An ML-based ECG algorithm effectively distinguished between the 2 groups.