Influence of atrial substrate on local capture induced by rapid pacing of atrial fibrillation

Understanding the Beat-to-Beat Variations of P-Waves Morphologies in AF Patients During Sinus Rhythm: A Scoping Review of the Atrial Simulation Studies

The remarkable advances in high-performance computing and the resulting increase of the computational power have the potential to leverage computational cardiology toward improving our understanding of the pathophysiological mechanisms of arrhythmias, such as Atrial Fibrillation (AF). In AF, a complex interaction between various triggers and the atrial substrate is considered to be the leading cause of AF initiation and perpetuation. In electrocardiography (ECG), P-wave is supposed to reflect atrial depolarization. It has been found that even during sinus rhythm (SR), multiple P-wave morphologies are present in AF patients with a history of AF, suggesting a higher dispersion of the conduction route in this population. In this scoping review, we focused on the mechanisms which modify the electrical substrate of the atria in AF patients, while investigating the existence of computational models that simulate the propagation of the electrical signal through different routes. The adopted review methodology is based on a structured analytical framework which includes the extraction of the keywords based on an initial limited bibliographic search, the extensive literature search and finally the identification of relevant articles based on the reference list of the studies. The leading mechanisms identified were classified according to their scale, spanning from mechanisms in the cell, tissue or organ level, and the produced outputs. The computational modeling approaches for each of the factors that influence the initiation and the perpetuation of AF are presented here to provide a clear overview of the existing literature. Several levels of categorization were adopted while the studies which aim to translate their findings to ECG phenotyping are highlighted. The results denote the availability of multiple models, which are appropriate under specific conditions. However, the consideration of complex scenarios taking into account multiple spatiotemporal scales, personalization of electrophysiological and anatomical models and the reproducibility in terms of ECG phenotyping has only partially been tackled so far.

Atrial fibrillation septal pacing: translation of modelling results

AIMS

Atrial fibrillation (AF) septal pacing consists of rapid pacing from a ring of electrodes around the atrial septum, leading to local capture of both atria during AF. The present model-based study evaluated the impact of the number of stimulation electrodes in the septal ring on AF capture for different types of sustained AF dynamics.

METHODS AND RESULTS

Using a biophysical model of AF based on CT scans from an AF patient, models with different AF substrates (Cholinergic AF and Meandering Wavelets) were created by varying the atrial membrane kinetics. Rapid pacing was applied from the septum area with a ring of 1, 2, 3, 4, 6, 8, or 12 electrodes during 20 seconds at a pacing cycle lengths (PCLs) in the range 60-100% of AF cycle length (AFCL), in 4% steps. Percentage of captured tissue during rapid pacing was determined using 24 sensing electrode pairs evenly distributed on the atrial surface. Results were averaged over 10 AF simulations. For Cholinergic AF, the number of stimulation electrodes on the septal ring had no significant impact on AF capture independently of AF dynamics. For Meandering Wavelets, more electrodes were needed to achieve AF capture in the presence of complex AF.

CONCLUSION

Changes in AF substrate significantly impacted septal pacing outcomes and response to rapid AF pacing may similarly vary patient-to-patient. The number of stimulation electrodes had a lesser impact, suggesting that the design of a ring with 3-4 electrodes around the septum would be sufficient for most AF dynamics.

How computer simulations of the human heart can improve anti-arrhythmia therapy

Over the last decade, the state-of-the-art in cardiac computational modelling has progressed rapidly. The electrophysiological function of the heart can now be simulated with a high degree of detail and accuracy, opening the doors for simulation-guided approaches to anti-arrhythmic drug development and patient-specific therapeutic interventions. In this review, we outline the basic methodology for cardiac modelling, which has been developed and validated over decades of research. In addition, we present several recent examples of how computational models of the human heart have been used to address current clinical problems in cardiac electrophysiology. We will explore the use of simulations to improve anti-arrhythmic pacing and defibrillation interventions; to predict optimal sites for clinical ablation procedures; and to aid in the understanding and selection of arrhythmia risk markers. Together, these studies illustrate how the tremendous advances in cardiac modelling are poised to revolutionize medical treatment and prevention of arrhythmia.