The VX1 artificial intelligence software for electrogram-based ablation in persistent atrial fibrillation standardized outcomes across centers and showed similar results to a visual control group (p>0.2).
Cohort (n=85)
Yes
Does VX1 artificial intelligence software standardize and maintain acute and long-term outcomes for electrogram-based ablation in patients with persistent atrial fibrillation compared to visual guidance?
An AI software algorithm (VX1) for electrogram-guided ablation in persistent AF is feasible and standardizes outcomes across different centers, achieving results comparable to visually-guided ablation by trained operators.
p-value: p=>0.2
INTRODUCTION: Multiple groups have reported on the usefulness of ablating in atrial regions exhibiting abnormal electrograms during atrial fibrillation (AF). Still, previous studies have suggested that ablation outcomes are highly operator- and center-dependent. This study sought to evaluate a novel machine learning software algorithm named VX1 (Volta Medical), trained to adjudicate multipolar electrogram dispersion. METHODS: This study was a prospective, multicentric, nonrandomized study conducted to assess the feasibility of generating VX1 dispersion maps. In 85 patients, 8 centers, and 17 operators, we compared the acute and long-term outcomes after ablation in regions exhibiting dispersion between primary and satellite centers. We also compared outcomes to a control group in which dispersion-guided ablation was performed visually by trained operators. RESULTS: The study population included 29% of long-standing persistent AF. AF termination occurred in 92% and 83% of the patients in primary and satellite centers, respectively, p = 0.31. The average rate of freedom from documented AF, with or without antiarrhythmic drugs (AADs), was 86% after a single procedure, and 89% after an average of 1.3 procedures per patient (p = 0.4). The rate of freedom from any documented atrial arrhythmia, with or without AADs, was 54% and 73% after a single or an average of 1.3 procedures per patient, respectively (p 0.2). Interestingly, intraprocedural AF termination and type of recurrent arrhythmia (i.e., AF vs. AT) appear to be predictors of the subsequent clinical course. CONCLUSION: VX1, an expertise-based artificial intelligence software solution, allowed for robust center-to-center standardization of acute and long-term ablation outcomes after electrogram-based ablation.
Seitz et al. (Mon,) conducted a cohort in persistent atrial fibrillation (n=85). VX1 machine learning software algorithm vs. visual dispersion-guided ablation was evaluated on acute and long-term outcomes after ablation (p=>0.2). The VX1 artificial intelligence software for electrogram-based ablation in persistent atrial fibrillation standardized outcomes across centers and showed similar results to a visual control group (p>0.2).