Sensitivity of reentrant driver localization to electrophysiological parameter variability in image-based computational models of persistent atrial fibrillation sustained by a fibrotic substrate

Abstract

Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia, causing morbidity and mortality in millions worldwide. The atria of patients with persistent AF (PsAF) are characterized by the presence of extensive and distributed atrial fibrosis, which facilitates the formation of persistent reentrant drivers (RDs, i.e., spiral waves), which promote fibrillatory activity. Targeted catheter ablation of RD-harboring tissues has shown promise as a clinical treatment for PsAF, but the outcomes remain sub-par. Personalized computational modeling has been proposed as a means of non-invasively predicting optimal ablation targets in individual PsAF patients, but it remains unclear how RD localization dynamics are influenced by inter-patient variability in the spatial distribution of atrial fibrosis, action potential duration (APD), and conduction velocity (CV). Here, we conduct simulations in computational models of fibrotic atria derived from the clinical imaging of PsAF patients to characterize the sensitivity of RD locations to these three factors. We show that RDs consistently anchor to boundaries between fibrotic and non-fibrotic tissues, as delineated by late gadolinium-enhanced magnetic resonance imaging, but those changes in APD/CV can enhance or attenuate the likelihood that an RD will anchor to a specific site. These findings show that the level of uncertainty present in patient-specific atrial models reconstructed without any invasive measurements (i.e., incorporating each individual's unique distribution of fibrotic tissue from medical imaging alongside an average representation of AF-remodeled electrophysiology) is sufficiently high that a personalized ablation strategy based on targeting simulation-predicted RD trajectories alone may not produce the desired result.

Is human atrial fibrillation stochastic or deterministic?—Insights from missing ordinal patterns and causal entropy-complexity plane analysis

“…For example, our results validate the effort to develop a deterministic or a nonlinear stochastic model to simulate AF. [6][7][8][9][10] In addition, our results justify the use of nonlinear dynamical tools to describe AF properties.…”

Wavelength and Fibrosis Affect Phase Singularity Locations During Atrial Fibrillation

“…In the case of fibrotic atria, propagation is constrained by the distribution of fibrosis through the physiological and/or structural changes made to the tissue, depending on the formulation of the implementation method. Previous clinical and computational studies have shown that tissue areas with higher fibrosis density attract more re-entrant activity than other areas (Deng et al, 2017 ; Cochet et al, 2018 ), but the trajectories can change, particularly when there are several fibrotic areas close to each other.…”

“…Deng et al ( 2017 ) investigated the sensitivity of rotor location to variations in APD and CV, for simulations incorporating fibrosis modeled with modified ionic and conductivity properties. In their simulations, rotors were overwhelmingly located in border regions between fibrotic and healthy tissue regardless of APD and CV values; however, the specific border regions that contained rotors varied depending on the APD and CV parameters.…”

“…Previous studies have shown that modifying WL properties (through action potential duration, APD, variation) affects AF dynamics (Qu et al, 1999 ). However, Deng et al ( 2017 ) found that with modest changes in electrophysiological properties, reentrant drivers localized to the same areas, at the edges of high fibrotic regions.…”

Computational Modeling of Electrophysiology and Pharmacotherapy of Atrial Fibrillation: Recent Advances and Future Challenges

“…Although much is still unknown about the role of structural and electrophysiological heterogeneities in AF, computational studies have contributed to the systematic characterization of the mechanisms of arrhythmogenesis. In some cases, these have highlighted the importance of patient-specific aspects for clinical therapy, as, for instance, the role of patient-specific fibrosis patterns for guiding catheter ablation procedures ( McDowell et al, 2015 ; Boyle et al, 2016 ; Deng et al, 2017 ; Cochet et al, 2018 ).…”