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.