Role of Structural Versus Cellular Remodeling in Atrial Arrhythmogenesis: Insights From Personalized Digital Twins

医学 心房颤动 内科学 心脏病学
作者
Andrey V. Pikunov,Roman A. Syunyaev,Rheeda L. Ali,Adityo Prakosa,Anna Gams,Patrick M. Boyle,Vanessa Steckmeister,Ingo Kutschka,Eric Rytkin,Niels Voigt,Natalia A. Trayanova,Igor R. Efimov
出处
期刊:Circulation-arrhythmia and Electrophysiology [Lippincott Williams & Wilkins]
卷期号:18 (9): e013898-e013898
标识
DOI:10.1161/circep.125.013898
摘要

BACKGROUND: Atrial fibrillation (AF) is a progressive disease involving both structural and functional remodeling. Although over the past decade, digital twin-guided therapy has been proposed and applied, accounting for cardiomyocyte functional remodeling remains challenging. We aimed to investigate the contribution of functional remodeling at the cellular level to AF pathogenesis in patients with fibrotic remodeling and to develop novel techniques to predict the location of reentrant drivers. METHODS: To investigate the contribution of cell-scale functional remodeling to AF pathogenesis under the conditions of fibrotic remodeling, we combined 3-dimensional atrial digital twins with pathology-specific single-cell models. The latter were developed using recordings in myocytes isolated from patients in sinus rhythm, paroxysmal, postoperative, and persistent AF. To quantify AF dynamics in the digital twins, we developed a novel algorithm for locating reentrant drivers by backtracking the conduction velocity field from the wavebreak regions. RESULTS: We demonstrate that our novel algorithm is at least 700× faster than the traditional phase singularity analysis. The inducibility of simulated AF was not pathology-dependent, but pathological models demonstrate a more extensive arrhythmogenic substrate than the sinus rhythm. We observed a correlation between wavebreak probability and fibrosis density, with the highest regression slope for the persistent AF model and the lowest for the sinus rhythm model. CONCLUSIONS: AF driver locations in atrial fibrotic substrates depend on electrophysiological remodeling; differences between pathology-specific models are explained by differences in wavebreak patterns. Specifically, reentrant drivers tend to dwell in the regions with the highest wavebreak probability.
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