Epicardial Fibrosis Explains Increased Transmural Conduction in a Computer Model of Atrial Fibrillation

Gharaviri, Ali; Potse, Mark; Verheule, Sander; Krause, Rolf; Auricchio, Angelo; Schotten, Ulrich

doi:10.22489/cinc.2016.071-216

Cited by 1 publication

(1 citation statement)

References 13 publications

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“…However, these factors can be identified and filtered using computational techniques of wave propagation, tissue physiology, and electrode size. This hypothesis was motivated by evidence that electrode spacing, orientation, and size alter electrogram shape and impact mapping 7 , as do wavefront direction 43 , wavefront number, signal type 26 , and tissue factors including fibrosis 10 .…”

Section: Introductionmentioning

confidence: 99%

Interpreting Activation Mapping of Atrial Fibrillation: A Hybrid Computational/Physiological Study

et al. 2017

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Atrial fibrillation is the most common rhythm disorder of the heart associated with a rapid and irregular beating of the upper chambers. Activation mapping remains the gold standard to diagnose and interpret atrial fibrillation. However, fibrillatory activation maps are highly sensitive to far-field effects, and often disagree with other optical mapping modalities. Here we show that computational modeling can identify spurious non-local components of atrial fibrillation electrograms and improve activation mapping. We motivate our approach with a cohort of patients with potential drivers of persistent atrial fibrillation. In a computational study using a monodomain Maleckar model, we demonstrate that in organized rhythms, electrograms successfully track local activation, whereas in atrial fibrillation, electrograms are sensitive to spiral wave distance and number, spiral tip trajectories, and effects of fibrosis. In a clinical study, we analyzed n = 15 patients with persistent atrial fibrillation that was terminated by limited ablation. In five cases, traditional activation maps revealed a spiral wave at sites of termination; in ten cases, electrogram timings were ambiguous and activation maps showed incomplete reentry. By adjusting electrogram timing through computational modeling, we found rotational activation, which was undetectable with conventional methods. Our results demonstrate that computational modeling can identify non-local deflections to improve activation mapping and explain how and where ablation can terminate persistent atrial fibrillation. Our hybrid computational/physiological approach has the potential to optimize map-guided ablation and improve ablation therapy in atrial fibrillation.

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