Validation of a Machine Learning Algorithm to Identify Pulmonary Vein Isolation during Ablation Procedures for the Treatment of Atrial Fibrillation: Results of the PVISION Study

Abstract

Background and Aims Pulmonary Vein Isolation (PVI) is the cornerstone of ablation for atrial fibrillation. Confirmation of PVI can be challenging due to far field electrograms and sometimes requires additional pacing maneuvers or mapping. This prospective multicenter study assessed the agreement between a previously trained automated algorithm designed to determine vein isolation status with expert opinion in real-world clinical setting. Method Consecutive patients scheduled for PVI were recruited at four centers. The ECGenius electrophysiology recording system (CathVision ApS, Denmark) was connected in parallel with the lab’s existing system. Electrograms from a circular mapping catheter were annotated during sinus rhythm at baseline pre-ablation, time of isolation, and post-ablation. The ground truth for isolation status was based on operator opinion. The algorithm was applied to the collected PV signals off-line and compared to expert opinion. The primary endpoint was a sensitivity and specificity exceeding 80%. Results Overall, 498 electrograms (248 at baseline and 250 at PVI) with 5,473 individual PV beats from 89 patients (32 females, 62 ±12 years) were analyzed. The algorithm performance reached an area under curve (AUC) of 92% and met the primary study endpoint with a sensitivity and specificity of 86% and 87%, respectively (p = 0.005; p = 0.004). The algorithm had an accuracy of 87% in classifying the time of isolation. Conclusion This study validated an automated algorithm using machine learning (ML) to assess the isolation status of pulmonary veins in patients undergoing PVI with different ablation modalities. The algorithm reached an AUC of 92% with both sensitivity and specificity exceeding the primary study endpoints.