Editorial: Recent Advances in Understanding the Basic Mechanisms of Atrial Fibrillation Using Novel Computational Approaches
- 1Bioengineering Institute, The University of Auckland, New Zealand
- 2School of Biomedical Engineering and Imaging Sciences, King’s College London, United Kingdom
- 3University Medical Center Hamburg-Eppendorf, Germany
- 4Royal Melbourne Hospital, Australia
- 5Department of Medicine and Therapeutics, Chinese University of Hong Kong, China
- 6Institut de rythmologie et modélisation cardiaque (IHU-Liryc), France
Where we are at regarding atrial fibrillation
Atrial fibrillation (AF) is the most common sustained heart rhythm disturbance, associated with substantial morbidity and mortality (Andrade et al., 2014). The current prevalence of AF is ~2% of the general population worldwide and is projected to more than double in the following decades, becoming a global epidemic due to the aging population and the increasing incidence of heart failure and other comorbidities such as hypertension and diabetes (Colilla et al., 2013;Krijthe et al., 2013). Current clinical treatment for AF is suboptimal. Ablation treatment for persistent and permanent AF and AF with concurrent cardiac diseases is disappointing with long term success rates being < 30% for single ablation procedures (Brooks et al., 2010;Nishida and Nattel, 2014). Furthermore, anti-arrhythmic drugs (AADs) often lose their efficacy and have side effects (Woods and Olgin, 2014). The poor clinical outcomes are primarily due to a lack of basic understanding of the AF mechanism and quantitative tools to optimize treatment strategies in a clinical setting (Haissaguerre et al., 2007;Hansen et al., 2015).
Novel computational approaches and techniques are playing an important role in our understanding and treatment of AF. Multi-scale computer models of the human atria have been used to investigate the important role of fibrosis in AF and consistently demonstrated that AF is perpetuated by the re-entrant circuits persisting in the fibrotic boundary zones (Bayer et al., 2016;Morgan et al., 2016;Vigmond et al., 2016;Zahid et al., 2016;Zhao et al., 2017). Moreover, models have been applied to propose efficient ablation (Bayer et al., 2016) and AAD (Varela et al., 2016) treatments for AF. To improve patients outcomes, novel computational analysis-aided ablation strategies have also been proposed. Narayan and co-workers have identified stable AF re-entrant drivers in patients using phase singularity analysis and atrial cellular restitution properties and demonstrated that it was possible to reverse AF in 80.3% of patients by directly targeting these regions in their Focal Impulse and Rotor Modulation (FIRM) trial (Narayan et al., 2014). In addition to the FIRM trial study, Haissaguerre et al. studied 103 patients with persistent AF using a noninvasive ECG imaging (ECGI) approach (Haissaguerre et al., 2014) and concluded that AF is sustained by localized spatially stable drivers where targeted ablation led to 85% of patients being freed from AF at 12-months post ablation. These high success rates are yet to be confirmed in a multi-center randomized clinical trial and the recent REAFFIRM clinical trial presented during a late-breaking session at Heart Rhythm 2019, however, failed to provide evidence of the superiority of the FIRM approach over pulmonary vein isolation. Meanwhile, machine learning is proving to be a promising tool for helping us to understand AF. For example, deep convolutional neural networks have been used to classify AF from single-lead ECGs (Hannun et al., 2019) and to reconstruct 3D left atrial chambers from gadolinium-enhanced MRIs (Xiong et al., 2019) with superior performance.
The aim of this research topic was to collect a series of reviews and original research articles presenting recent advances towards a better understanding and treatment of AF through the development or use of: 1) structure-detailed computer modeling; 2) biophysics-based atrial cellular modeling; 3) signal processing and clinical mapping; and 4) meta-analysis and clinical studies. A total of 27 accepted articles were published under this research topic. Here in this editorial, we will summarize the new knowledge and approaches generated, and discuss how these can contribute to an improved understanding of AF mechanisms and clinical treatment, as well as how they may shape future research directions.
Critical insights learned from structure-detailed computer modeling
Improvements in clinical imaging and mapping allow detailed characterization of atrial anatomy, structure and electrophysiology. Computer models of atrial electrical activation provide a powerful computational framework for understanding the structure-function relationship that underlies atrial re-entrant arrhythmias. Atrial structure, including wall thickness, fibrosis, and myofiber orientation, have been suggested to dictate the locations of AF re-entrant drivers in explanted human heart studies (Zhao et al., 2015; Bishop et al., 2015; Zhao et al., 2017). Of all atrial structures, fibrosis, the hallmark of structural remodeling, has been investigated extensively in this Research Topic. Clayton studied the effect of the spatial scale of simulated fibrosis on electrical propagations by smoothly varying the diffusion coefficient in 2D atrial tissue models. His study concludes that the spatial scale of fibrosis has important effects on both dispersion of recovery and vulnerability to re-entry. The Aslanidi group evaluated the effects of both atrial wall thickness and fibrosis on AF re-entrant drivers using two sets of computer models, a simple model of an atrial tissue slab with a step change in wall thickness and a synthetic fibrosis patch, and a set of 3D patient-specific computer models based on MRI (Roy et al.). In the slab model, they observed that an AF re-entrant driver drifted towards and along the regions with changes/gradients in wall thickness. Furthermore, they discovered that additional patchy fibrosis would pull the AF re-entrant driver towards it, and that the locations of AF re-entrant drivers were determined by both fibrosis and wall thickness gradients. On the other hand, results from the patient-specific computer models suggested that the interaction between wall thickness and fibrosis plays a very important role in the right atrium due to extensive trabecular structure, whilst fibrosis performs a more decisive role in the left atrium due to a comparably smaller trabecular structure and more extensive fibrotic remodeling (Roy et al.). In another study conducted by Stephenson and his co-workers using micro-CT imaging and anatomically accurate computer modeling, morphological substrates for atrial arrhythmogenesis were discovered in archived human hearts with atrioventricular septal defect (Stephenson et al.). To directly link computer modeling to clinical treatment, Boyle et al. have carried out a multi-modal assessment of the arrhythmogenic propensity of the fibrotic substrate in patients with persistent AF by comparing locations of AF driver regions found in patient-specific computer simulations to those detected by the clinical FIRM approach.
Keywords: Atrial Fibrillation, computer models, computational modeling, arrhythmia mechanisms, Cardiac Electrophysiology
Received: 06 Jun 2019;
Accepted: 02 Aug 2019.
Edited by:Raimond L. Winslow, Johns Hopkins University, United States
Copyright: © 2019 Zhao, Aslanidi, Kuklik, Lee, Tse, Niederer and Vigmond. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Dr. Jichao Zhao, The University of Auckland, Bioengineering Institute, Auckland, New Zealand, email@example.com