Comparing Phenotypes for Acute and Long-Term Response to Atrial Fibrillation Ablation Using Machine Learning

Long-term and acute responses to AF ablation reflect distinct clinical and electrical physiology, respectively. This de-linking of phenotypes raises the question of whether long-term success operates through factors such as attenuated AF progression. There remains an urgent need to develop procedural predictors of long-term AF ablation success.

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Publication Details

Journal
Circulation Arrhythmia and Electrophysiology
Published
2025-02-01
DOI
https://doi.org/10.1161/circep.124.012860
Citations
1
Primary Topic
Atrial Fibrillation Management and Outcomes
Type
article
Field-Weighted Citation Impact
1.39
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article

Comparing Phenotypes for Acute and Long-Term Response to Atrial Fibrillation Ablation Using Machine Learning

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Circulation Arrhythmia and Electrophysiology
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article

Comparing Phenotypes for Acute and Long-Term Response to Atrial Fibrillation Ablation Using Machine Learning

Albert J. Rogers, Kelly Brennan, Paul Clopton, Ruibin Feng, David E. Krummen, Sanjiv M. Narayan, Tina Baykaner, Ulrich Schotten, Paul J. Wang, Samuel Ruipérez-Campillo, Brototo Deb, Wayne R. Giles, Viren Srivastava, Prasanth Ganesan, Maxime Pedron, Hui Ju Chang
article en
1 citations

Abstract

Long-term and acute responses to AF ablation reflect distinct clinical and electrical physiology, respectively. This de-linking of phenotypes raises the question of whether long-term success operates through factors such as attenuated AF progression. There remains an urgent need to develop procedural predictors of long-term AF ablation success.

Circulation Arrhythmia and ElectrophysiologyVol. 18(2)
University of Calgary (CA), Cardiovascular Institute of the South (US), Maastricht University Medical Centre (NL), Maastricht University (NL), ETH Zurich (CH), University of California San Diego (US), University of California San Diego Medical Center (US)
Openalex Percentile: Top 23%
Atrial Fibrillation Management and Outcomes
1.39
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