Comparing Inducibility of Reentrant Arrhythmia in Patient-Specific Computational Models to Clinical Atrial Fibrillation Phenotypes

Computational modeling of reentrant arrhythmia in patients with persistent atrial fibrillation is feasible using image-derived data on anatomy and fibrotic substrate. Models incorporate changes to electrophysiological properties due to fibrosis but use the same default parameterization regardless of...

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Bibliographic Details
Published in:JACC. Clinical electrophysiology 2023-08, Vol.9 (10), p.2149-2162
Main Authors: Macheret, Fima, Bifulco, Savannah F., Scott, Griffin D., Kwan, Kirsten T., Chahine, Yaacoub, Afroze, Tanzina, McDonagh, Rosemary, Akoum, Nazem, Boyle, Patrick M.
Format: Article
Language:English
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Summary:Computational modeling of reentrant arrhythmia in patients with persistent atrial fibrillation is feasible using image-derived data on anatomy and fibrotic substrate. Models incorporate changes to electrophysiological properties due to fibrosis but use the same default parameterization regardless of patient-specific variability, which may limit model agreement with clinical observations. Executing simulations with a range of conduction velocity values produced a set of possible predictions with improved model/clinical agreement compared to simulations that were run with default conduction velocity only. Personalization of conduction velocity could thus significantly improve model/clinical agreement, especially in patients with increased left atrial fibrosis or body mass index.
ISSN:2405-500X
2405-5018
DOI:10.1016/j.jacep.2023.06.015