Atrial septal pacing for the termination of atrial fibrillation: study in a biophysical model of human atria

Automatic Extraction of Recurrent Patterns of High Dominant Frequency Mapping During Human Persistent Atrial Fibrillation

Purpose: Identifying targets for catheter ablation remains challenging in persistent atrial fibrillation (persAF). The dominant frequency (DF) of atrial electrograms during atrial fibrillation (AF) is believed to primarily reflect local activation. Highest DF (HDF) might be responsible for the initiation and perpetuation of persAF. However, the spatiotemporal behavior of DF remains not fully understood. Some DFs during persAF were shown to lack spatiotemporal stability, while others exhibit recurrent behavior. We sought to develop a tool to automatically detect recurrent DF patterns in persAF patients. Methods: Non-contact mapping of the left atrium (LA) was performed in 10 patients undergoing persAF HDF ablation. 2,048 virtual electrograms (vEGMs, EnSite Array, Abbott Laboratories, USA) were collected for up to 5 min before and after ablation. Frequency spectrum was estimated using fast Fourier transform and DF was identified as the peak between 4 and 10 Hz and organization index (OI) was calculated. The HDF maps were identified per 4-s window and an automated pattern recognition algorithm was used to find recurring HDF spatial patterns. Dominant patterns (DPs) were defined as the HDF pattern with the highest recurrence. Results: DPs were found in all patients. Patients in atrial flutter after ablation had a single DP over the recorded time period. The time interval (median [IQR]) of DP recurrence for the patients in AF after ablation (7 patients) decreased from 21.1 s [11.8 49.7 s] to 15.7 s [6.5 18.2 s]. The DF inside the DPs presented lower temporal standard deviation (0.18 ± 0.06 Hz vs. 0.29 ± 0.12 Hz, p < 0.05) and higher OI (0.35 ± 0.03 vs. 0.31 ± 0.04, p < 0.05). The atrial regions with the highest proportion of HDF region were the septum and the left upper pulmonary vein. Conclusion: Multiple recurrent spatiotemporal HDF patterns exist during persAF. The proposed method can identify and quantify the spatiotemporal repetition of the HDFs, where the high recurrences of DP may suggest a more organized rhythm. DPs presented a more consistent DF and higher organization compared with non-DPs, suggesting that DF with higher OI might be more likely to recur. Recurring patterns offer a more comprehensive dynamic insight of persAF behavior, and ablation targeting such regions may be beneficial.

Epicardial Fibrosis Explains Increased Endo-Epicardial Dissociation and Epicardial Breakthroughs in Human Atrial Fibrillation

Background

Atrial fibrillation (AF) is accompanied by progressive epicardial fibrosis, dissociation of electrical activity between the epicardial layer and the endocardial bundle network, and transmural conduction (breakthroughs). However, causal relationships between these phenomena have not been demonstrated yet. Our goal was to test the hypothesis that epicardial fibrosis suffices to increase endo–epicardial dissociation (EED) and breakthroughs (BT) during AF.

Methods

We simulated the effect of fibrosis in the epicardial layer on EED and BT in a detailed, high-resolution, three-dimensional model of the human atria with realistic electrophysiology. The model results were compared with simultaneous endo–epicardial mapping in human atria. The model geometry, specifically built for this study, was based on MR images and histo-anatomical studies. Clinical data were obtained in four patients with longstanding persistent AF (persAF) and three patients without a history of AF.

Results

The AF cycle length (AFCL), conduction velocity (CV), and EED were comparable in the mapping studies and the simulations. EED increased from 24.1 ± 3.4 to 56.58 ± 6.2% (p < 0.05), and number of BTs per cycle from 0.89 ± 0.55 to 6.74 ± 2.11% (p < 0.05), in different degrees of fibrosis in the epicardial layer. In both mapping data and simulations, EED correlated with prevalence of BTs. Fibrosis also increased the number of fibrillation waves per cycle in the model.

Conclusion

A realistic 3D computer model of AF in which epicardial fibrosis was increased, in the absence of other pathological changes, showed increases in EED and epicardial BT comparable to those in longstanding persAF. Thus, epicardial fibrosis can explain both phenomena.

Computational models in cardiology

The treatment of individual patients in cardiology practice increasingly relies on advanced imaging, genetic screening and devices. As the amount of imaging and other diagnostic data increases, paralleled by the greater capacity to personalize treatment, the difficulty of using the full array of measurements of a patient to determine an optimal treatment seems also to be paradoxically increasing. Computational models are progressively addressing this issue by providing a common framework for integrating multiple data sets from individual patients. These models, which are based on physiology and physics rather than on population statistics, enable computational simulations to reveal diagnostic information that would have otherwise remained concealed and to predict treatment outcomes for individual patients. The inherent need for patient-specific models in cardiology is clear and is driving the rapid development of tools and techniques for creating personalized methods to guide pharmaceutical therapy, deployment of devices and surgical interventions.

How disruption of endo-epicardial electrical connections enhances endo-epicardial conduction during atrial fibrillation

Aims

Loss of side-to-side electrical connections between atrial muscle bundles is thought to underlie conduction disturbances predisposing to atrial fibrillation (AF). Putatively, disruption of electrical connections occurs not only within the epicardial layer but also between the epicardial layer and the endocardial bundle network, thus impeding transmural conductions (‘breakthroughs’). However, both clinical and experimental studies have shown an enhancement of breakthroughs during later stages of AF. We tested the hypothesis that endo-epicardial uncoupling enhances endo-epicardial electrical dyssynchrony, breakthrough rate (BTR), and AF stability.

Methods and Results

In a novel dual-layer computer model of the human atria, 100% connectivity between the two layers served as healthy control. Atrial structural remodelling was simulated by reducing the number of connections between the layers from 96 to 6 randomly chosen locations. With progressive elimination of connections, AF stability increased. Reduction in the number of connections from 96 to 24 resulted in an increase in endo-epicardial dyssynchrony from 6.6 ± 1.9 to 24.6 ± 1.3%, with a concomitant increase in BTR. A further reduction to 12 and 6 resulted in more pronounced endo-epicardial dyssynchrony of 34.4 ± 1.15 and 40.2 ± 0.52% but with BTR reduction. This biphasic relationship between endo-epicardial coupling and BTR was found independently from whether AF was maintained by re-entry or by ectopic focal discharges.

Conclusion

Loss of endo-epicardial coupling increases AF stability. There is a biphasic relation between endo-epicardial coupling and BTR. While at high degrees of endo-epicardial connectivity, the BTR is limited by the endo-epicardial synchronicity, at low degrees of connectivity, it is limited by the number of endo-epicardial connections.

Mechanisms of stochastic onset and termination of atrial fibrillation studied with a cellular automaton model

Mathematical models of cardiac electrical excitation are increasingly complex, with multiscale models seeking to represent and bridge physiological behaviours across temporal and spatial scales. The increasing complexity of these models makes it computationally expensive to both evaluate long term (more than 60 s) behaviour and determine sensitivity of model outputs to inputs. This is particularly relevant in models of atrial fibrillation (AF), where individual episodes last from seconds to days, and interepisode waiting times can be minutes to months. Potential mechanisms of transition between sinus rhythm and AF have been identified but are not well understood, and it is difficult to simulate AF for long periods of time using state-of-the-art models. In this study, we implemented a Moe-type cellular automaton on a novel, topologically equivalent surface geometry of the left atrium. We used the model to simulate stochastic initiation and spontaneous termination of AF, arising from bursts of spontaneous activation near pulmonary veins. The simplified representation of atrial electrical activity reduced computational cost, and so permitted us to investigate AF mechanisms in a probabilistic setting. We computed large numbers (approx. 10⁵) of sample paths of the model, to infer stochastic initiation and termination rates of AF episodes using different model parameters. By generating statistical distributions of model outputs, we demonstrated how to propagate uncertainties of inputs within our microscopic level model up to a macroscopic level. Lastly, we investigated spontaneous termination in the model and found a complex dependence on its past AF trajectory, the mechanism of which merits future investigation.

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.

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

AIMS

Preliminary studies showed that the septum area was the only location allowing local capture of both the atria during rapid pacing of atrial fibrillation (AF) from a single site. The present model-based study investigated the influence of atrial substrate on the ability to capture AF when pacing the septum.

METHODS AND RESULTS

Three biophysical models of AF with an identical anatomy from human atria but with different AF substrates were used: (i) AF based on multiple wavelets, (ii) AF based on heterogeneities in vagal activation, (iii) AF based on heterogeneities in repolarization. A fourth anatomical model without Bachmann's bundle (BB) was also implemented. Rapid pacing was applied from the septum at pacing cycle lengths in the range of 50-100% of AF cycle length. Local capture was automatically assessed with 24 pairs of electrodes evenly distributed on the atrial surface. The results were averaged over 16 AF simulations. In the homogeneous substrate, AF capture could reach 80% of the atrial surface. Heterogeneities degraded the ability to capture during AF. In the vagal substrate, the capture tended to be more regular and the degradation of the capture was not directly related to the spatial extent of the heterogeneities. In the third substrate, heterogeneities induced wave anchorings and wavebreaks even in areas close to the pacing site, with a more dramatic effect on AF capture. Finally, BB did not significantly affect the ability to capture.

CONCLUSION

Atrial fibrillation substrate had a significant effect on rapid pacing outcomes. The response to therapeutic pacing may therefore be specific to each patient.

Mathematical approaches to understanding and imaging atrial fibrillation: significance for mechanisms and management

Atrial fibrillation (AF) is the most common sustained arrhythmia in humans. The mechanisms that govern AF initiation and persistence are highly complex, of dynamic nature, and involve interactions across multiple temporal and spatial scales in the atria. This article aims to review the mathematical modeling and computer simulation approaches to understanding AF mechanisms and aiding in its management. Various atrial modeling approaches are presented, with descriptions of the methodological basis and advancements in both lower-dimensional and realistic geometry models. A review of the most significant mechanistic insights made by atrial simulations is provided. The article showcases the contributions that atrial modeling and simulation have made not only to our understanding of the pathophysiology of atrial arrhythmias, but also to the development of AF management approaches. A summary of the future developments envisioned for the field of atrial simulation and modeling is also presented. The review contends that computational models of the atria assembled with data from clinical imaging modalities that incorporate electrophysiological and structural remodeling could become a first line of screening for new AF therapies and approaches, new diagnostic developments, and new methods for arrhythmia prevention.

A new look at atrial fibrillation: lessons learned from drugs, pacing, and ablation therapies

Atrial fibrillation (AF) is the most common arrhythmia and among the leading causes of stroke and heart failure in Western populations. Despite the increasing size of clinical trials assessing the efficacy and safety of AF therapies, achieved outcomes have not always matched expectations. Considering that AF is a symptom of many possible underlying diseases, clinical research for this arrhythmia should take into account their respective pathophysiology. Accordingly, the definition of the study populations to be included should rely on the established as well as on the new classifications of AF and take advantage from a differentiated look at the AF-electrocardiogram and from increasingly large spectrum of biomarkers. Such an integrated approach could bring researchers and treating physicians one step closer to the ultimate vision of personalized therapy, which, in this case, means an AF therapy based on refined diagnostic elements in accordance with scientific evidence gathered from clinical trials. By applying clear-cut patient inclusion criteria, future studies will be of smaller size and thus of lower cost. In addition, the findings from such studies will be of greater predictive value at the individual patient level, allowing for pinpointed therapeutic decisions in daily practice.