Ab-452675-3 Characteristics of Residual Signals After Pulsed Field Ablation of the Pulmonary Veins

Duytschaever, Mattias; Firth, Karl; Leonard, Olivier; Paamand, Rune; Knecht, Sébastien

doi:10.1016/j.hrthm.2023.03.362

Cited by 1 publication

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“…Since the completion of the study, a new ablation modality employing a single catheter to map and ablate with pulsed field energy has been introduced and appears to have advantages in terms of procedure efficiency and safety. 17 The PVI Analyzer algorithm’s performance with this particular modality has been tested only in a limited number of patients 27 and, due to the nature of the widely spaced, large electrodes, may require further training of the model.…”

Section: Discussionmentioning

confidence: 99%

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

De Pooter,

Timmers,

Boveda

et al. 2024

Europace

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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.

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Section: Discussionmentioning

confidence: 99%