Artificial intelligence software standardizes electrogram-based ablation outcome for persistent atrial fibrillation
© The Authors. Journal of Cardiovascular Electrophysiology published by Wiley Periodicals LLC..
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 < 0.001). No statistically significant differences between outcomes of the primary versus satellite centers were observed for one (p = 0.8) or multiple procedures (p = 0.4), or between outcomes of the entire study population versus the control group (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.
| Errataetall: |
CommentIn: J Cardiovasc Electrophysiol. 2022 Nov;33(11):2261-2262. - PMID 35989539 |
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| Medienart: |
E-Artikel |
| Erscheinungsjahr: |
2022 |
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| Erschienen: |
2022 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:33 |
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| Enthalten in: |
Journal of cardiovascular electrophysiology - 33(2022), 11 vom: 01. Nov., Seite 2250-2260 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Seitz, Julien [VerfasserIn] |
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| Links: |
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| Themen: |
Anti-Arrhythmia Agents |
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| Anmerkungen: |
Date Completed 11.11.2022 Date Revised 10.01.2023 published: Print-Electronic CommentIn: J Cardiovasc Electrophysiol. 2022 Nov;33(11):2261-2262. - PMID 35989539 Citation Status MEDLINE |
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| doi: |
10.1111/jce.15657 |
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| Förderinstitution / Projekttitel: |
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| 245 | 1 | 0 | |a Artificial intelligence software standardizes electrogram-based ablation outcome for persistent atrial fibrillation |
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| 500 | |a CommentIn: J Cardiovasc Electrophysiol. 2022 Nov;33(11):2261-2262. - PMID 35989539 | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a © The Authors. Journal of Cardiovascular Electrophysiology published by Wiley Periodicals LLC. | ||
| 520 | |a 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 | ||
| 520 | |a 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 | ||
| 520 | |a 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 < 0.001). No statistically significant differences between outcomes of the primary versus satellite centers were observed for one (p = 0.8) or multiple procedures (p = 0.4), or between outcomes of the entire study population versus the control group (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 | ||
| 520 | |a 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 | ||
| 650 | 4 | |a Journal Article | |
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| 650 | 4 | |a sinus rhythm | |
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