First clinical data on artificial intelligence-guided catheter ablation in long-standing persistent atrial fibrillation.

ablation of dispersions artificial-guided ablation atrial fibrillation catheter ablation high-density mapping

Journal

Journal of cardiovascular electrophysiology
ISSN: 1540-8167
Titre abrégé: J Cardiovasc Electrophysiol
Pays: United States
ID NLM: 9010756

Informations de publication

Date de publication:
10 Jan 2024
Historique:
revised: 20 12 2023
received: 11 09 2023
accepted: 29 12 2023
medline: 10 1 2024
pubmed: 10 1 2024
entrez: 10 1 2024
Statut: aheadofprint

Résumé

Despite advanced ablation strategies and major technological improvements, treatment of persistent atrial fibrillation (AF) remains challenging and the underlying pathophysiology is not fully understood. This study analyzed the multiple procedure outcome and safety of catheter ablation of spatiotemporal dispersions (DISPERS) detected by artificial intelligence (AI)-guided software in patients with long-standing persistent AF. The Volta VX1 software was used for 50 consecutive patients undergoing catheter ablation for persistent AF. First, high-density mapping (78% biatrial) with a multipolar mapping catheter was performed. In addition to pulmonary vein isolation (PVI), ablation of DISPERS was performed aiming at homogenizing, dissecting, isolating, or connecting DISPERS areas to nonconducting anatomical structures. Follow-up contained regular visits at our outpatient clinic at 1, 3, 6, and 12 months including 7-day Holter electrocardiograms. Patients were mainly suffering from long-standing persistent AF (mean AF duration 50.30 ± 54.28 months). Following PVI, ablation of left atrial and right atrial DISPERS areas led to AF cycle length prolongation (mean of 162.0 ± 16.6 to 202.2 ± 21.6 ms after) and AF termination to atrial tachycardia (AT) or sinus rhythm (SR) in 12 patients (24%). No stroke or pericardial effusion occurred; major groin complications (pseudoaneurysm n = 1, atrioventricular fistula n = 1) were detected in two patients. After a blanking period of 6 weeks, recurrence of any atrial arrhythmia was documented in 26 patients (52%). The majority of patients presented with organized AT (n = 15) while AF was present in n = 9 patients and AT/AF was observed in n = 2 patients. Twenty-two patients underwent reablation. During a mean follow-up of 363.14 ± 187.42 days and after an average of 1.46 ± 0.68 procedures, 82% of patients remained in stable SR. DISPERS-guided ablation using machine learning software (the Volta VX1 software) in addition to PVI in long-standing persistent AF ablation resulted in high long-term success rates regarding AF and AT elimination. Most arrhythmia recurrences were reentrant AT. After a total of 1.46 ± 0.68 procedures, freedom from AF/AT was 82%. Despite prolonged procedure times complication rates were low. Randomized studies are necessary to evaluate long-term efficacy of dispersion-guided ablation using AI.

Identifiants

pubmed: 38197476
doi: 10.1111/jce.16184
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Informations de copyright

© 2024 Wiley Periodicals LLC.

Références

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Auteurs

Fabian Bahlke (F)

Department of Electrophysiology, German Heart Center Munich, Technical University of Munich, Munich, Germany.

Florian Englert (F)

Department of Electrophysiology, German Heart Center Munich, Technical University of Munich, Munich, Germany.

Miruna Popa (M)

Department of Electrophysiology, German Heart Center Munich, Technical University of Munich, Munich, Germany.

Felix Bourier (F)

Department of Electrophysiology, German Heart Center Munich, Technical University of Munich, Munich, Germany.

Tilko Reents (T)

Department of Electrophysiology, German Heart Center Munich, Technical University of Munich, Munich, Germany.

Carsten Lennerz (C)

Department of Electrophysiology, German Heart Center Munich, Technical University of Munich, Munich, Germany.

Hannah Kraft (H)

Department of Electrophysiology, German Heart Center Munich, Technical University of Munich, Munich, Germany.

Alex Tunsch Martinez (AT)

Department of Electrophysiology, German Heart Center Munich, Technical University of Munich, Munich, Germany.

Marc Kottmaier (M)

Department of Electrophysiology, German Heart Center Munich, Technical University of Munich, Munich, Germany.

Jan Syväri (J)

Department of Electrophysiology, German Heart Center Munich, Technical University of Munich, Munich, Germany.

Madeleine Tydecks (M)

Department of Electrophysiology, German Heart Center Munich, Technical University of Munich, Munich, Germany.

Marta Telishevska (M)

Department of Electrophysiology, German Heart Center Munich, Technical University of Munich, Munich, Germany.

Sarah Lengauer (S)

Department of Electrophysiology, German Heart Center Munich, Technical University of Munich, Munich, Germany.

Gabriele Hessling (G)

Department of Electrophysiology, German Heart Center Munich, Technical University of Munich, Munich, Germany.

Isabel Deisenhofer (I)

Department of Electrophysiology, German Heart Center Munich, Technical University of Munich, Munich, Germany.

Nico Erhard (N)

Department of Electrophysiology, German Heart Center Munich, Technical University of Munich, Munich, Germany.

Classifications MeSH