Aditi Roy, Marta Varela, Henry Chubb, Robert MacLeod, Jules C. Hancox, Tobias Schaeffter, Oleg Aslanidi
Abstract
Clinical evidence suggests a link between fibrosis in the left atrium (LA) and atrial fibrillation (AF), the most common sustained arrhythmia. Image-derived fibrosis is increasingly used for patient stratification and therapy guidance. However, locations of re-entrant drivers (RDs) sustaining AF are unknown and therapy success rates remain suboptimal. This study used image-derived LA models to explore the dynamics of RD stabilization in fibrotic regions and generate maps of RD locations. LA models with patient-specific geometry and fibrosis distribution were derived from late gadolinium enhanced magnetic resonance imaging of 6 AF patients. In each model, RDs were initiated at multiple locations, and their trajectories were tracked and overlaid on the LA fibrosis distributions to identify the most likely regions where the RDs stabilized. The simulations showed that the RD dynamics were strongly influenced by the amount and spatial distribution of fibrosis. In patients with fibrosis burden greater than 25%, RDs anchored to specific locations near large fibrotic patches. In patients with fibrosis burden below 25%, RDs either moved near small fibrotic patches or anchored to anatomical features. The patient-specific maps of RD locations showed that areas that harboured the RDs were much smaller than the entire fibrotic areas, indicating potential targets for ablation therapy. Ablating the predicted locations and connecting them to the existing pulmonary vein ablation lesions was the most effective in-silico ablation strategy.
Introduction
The prevalence of atrial fibrillation (AF) is increasing to epidemic proportions: worldwide over 33 million individuals have AF [1]. Rhythm control strategies for maintaining sinus rhythm, such as antiarrhythmic drugs, can lead to significant improvements of cardiac output and quality of life. Over recent decades, catheter ablation (CA) therapy has also become a first-line treatment for AF. Radiofrequency CA is aimed at destroying arrhythmogenic tissue areas in the atria via high energy delivery through a catheter, and it is the only treatment with a proven long-term curative effect [2]. However, treatments of AF are complicated by its mechanisms for self-sustenance, such as the presence of AF-induced electrical and structural remodelling that generates more treatment-resistant arrhythmia [3,4]. Therefore, even advanced CA procedures have suboptimal long-term outcomes in patients with chronic forms of AF: over half of the patients return for additional treatment within three years [5]. This can be explained by the highly empirical nature of CA therapy, which targets “usual suspect” areas without knowledge of the underlying arrhythmogenic mechanisms. Thus, CA therapy based on electrical isolation of the pulmonary veins (PV) has low success rates in chronic AF patients, where extensive ablation of remodelled non-PV areas is commonly applied [6].
Methods
The study applied fibrosis distributions derived from patient LGE MRI data to build realistic 3D LA models and simulate the patient-specific RD dynamics. The models were generated using the general image-based computational workflow illustrated in Fig 1.
Discussion
This study developed a novel image-based computational workflow for the identification of patient-specific locations of RDs sustaining AF. Specifically, we: 1) developed 3D LA models with patient-specific geometry and distribution of fibrosis obtained from LGE-MRI of 6 AF patients, 2) applied the models to explore the dynamics of RD stabilisation in the presence of slow-conducting fibrotic patches, 3) identified patient-specific TAs for CA using the RD locations, relative to the distribution of fibrosis and 4) evaluated AF termination by simulating several ablation protocols, including TA-guided ones.
Citation: Roy A, Varela M, Chubb H, MacLeod R, Hancox JC, Schaeffter T, et al. (2020) Identifying locations of re-entrant drivers from patient-specific distribution of fibrosis in the left atrium. PLoS Comput Biol 16(9): e1008086. https://doi.org/10.1371/journal.pcbi.1008086
Editor: Alexander V. Panfilov, Universiteit Gent, BELGIUM
Received: March 6, 2020; Accepted: June 22, 2020; Published: September 23, 2020
Copyright: © 2020 Roy et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All relevant data are within the manuscript and its Supporting Information files.
Funding: This work was supported by grants from the British Heart Foundation [PG/15/8/31138] (JCH, RM, OVA), the Engineering and Physical Sciences Research Council [EP/L015226/1] (AR, OVA, TS) and the Wellcome/EPSRC Centre for Medical Engineering [WT 203148/Z/16/Z] (OVA).
Competing interests: The authors have declared that no competing interests exist.
