Ablation of Atrial Fibrillation Drivers

The Use of Artificial Intelligence for Detecting and Predicting Atrial Arrhythmias Post Catheter Ablation

Catheter ablation (CA) is considered as one of the most effective methods technique for eradicating persistent and abnormal cardiac arrhythmias. Nevertheless, in some cases, these arrhythmias are not treated properly, resulting in their recurrences. If left untreated, they may result in complications such as strokes, heart failure, or death. Until recently, the primary techniques for diagnosing recurrent arrhythmias following CA were the findings predisposing to the changes caused by the arrhythmias on cardiac imaging and electrocardiograms during follow-up visits, or if patients reported having palpitations or chest discomfort after the ablation. However, these follow-ups may be time-consuming and costly, and they may not always determine the root cause of the recurrences. With the introduction of artificial intelligence (AI), these follow-up visits can be effectively shortened, and improved methods for predicting the likelihood of recurring arrhythmias after their ablation procedures can be developed. AI can be divided into two categories: machine learning (ML) and deep learning (DL), the latter of which is a subset of ML. ML and DL models have been used in several studies to demonstrate their ability to predict and identify cardiac arrhythmias using clinical variables, electrophysiological characteristics, and trends extracted from imaging data. AI has proven to be a valuable aid for cardiologists due to its ability to compute massive amounts of data and detect subtle changes in electric signals and cardiac images, which may potentially increase the risk of recurrent arrhythmias after CA. Despite the fact that these studies involving AI have generated promising outcomes comparable to or superior to human intervention, they have primarily focused on atrial fibrillation while atrial flutter (AFL) and atrial tachycardia (AT) were the subjects of relatively few AI studies. Therefore, the aim of this review is to investigate the interaction of AI algorithms, electrophysiological characteristics, imaging data, risk score calculators, and clinical variables in predicting cardiac arrhythmias following an ablation procedure. This review will also discuss the implementation of these algorithms to enable the detection and prediction of AFL and AT recurrences following CA.

Driver characteristics associated with structurally and electrically remodeled atria in persistent atrial fibrillation

Background

Recent studies suggest persistent atrial fibrillation (AF) is maintained by localized focal or rotational electrical activations termed drivers.

Objective

The purpose of this study was to evaluate how left atrial (LA) dilation and time in AF impact persistent AF mechanisms.

Methods

Patients with persistent AF <2 years underwent electrocardiographic image mapping. Potential drivers (PDs) were defined as rotational wavefront activity ≥1.5 revolutions or focal activations. Distribution of PDs was recorded using an 18-segment model.

Results

One hundred patients were enrolled (age 61.3 ± 12.1 years). Of these patients, 47 were hypertensive, 14 had diabetes mellitus, and 10 had ischemic heart disease. AF duration was 8 [5-15] months. Median LA diameter was 39 [33-43] mm. Although LA dimensions did not correlate with overall PD burden or distribution, there was a modest correlation between increasing LA area (r = 0.235; P = .024) and LA volume (r = 0.216; P = .039) with proportion of PDs that were rotational. Although time in AF did not correlate with overall PD burden or distribution, there was a correlation between time in AF and the number of focal PDs (r = 0.203; P = .044). Female gender, increasing age, and hypertension also were associated with an increase in focal PDs.

Conclusion

This is the first study to demonstrate different AF mechanisms in patient subgroups. Greater understanding of patient-specific AF mechanisms may facilitate a tailored approach to AF mapping and ablation.

Impact of pulmonary vein isolation on mechanisms sustaining persistent atrial fibrillation: Predicting the acute response

BACKGROUND

Noninvasive mapping identifies potential drivers (PDs) in atrial fibrillation (AF). We analyzed the impact of pulmonary vein isolation (PVI) on PDs and whether baseline PD pattern predicted termination of AF.

METHODS

Patients with persistent AF less than 2 years underwent electrocardiographic imaging mapping before and after cryoballoon PVI. We recorded the number of PD occurrences, characteristics (rotational wavefronts ≥ 1.5 revolutions or focal activations), and distribution using an 18-segment atrial model.

RESULTS

Of 100 patients recruited, PVI terminated AF in 15 patients; 21.3% ± 9.1% (8.7 ± 4.8) of PDs occurred at the pulmonary veins (PVs) and posterior wall. PVI had no impact on PD occurrences outside the PVs and posterior wall (33.2 ± 12.9 vs 31.6 ± 12.5; P = .164), distribution over the remaining 13 segments (9 [8-11] vs 9 [8-10]; P = .634), the proportion of PDs that was rotational (82.9% ± 9.7% vs 83.6% ± 10.1%; P = .496), or temporal stability (2.4 ± 0.4 vs 2.4 ± 0.5 rotations; P = .541). Fewer focal PDs (area under the curve, 0.683; 95% CI, 0.528-0.839; P = .024) but not rotational PDs (P = .626) predicted AF termination with PVI.

CONCLUSIONS

PVI did not have a global impact on PDs outside the PVs and posterior wall. Although fewer focal PDs predicted termination of AF with PVI, the burden of rotational PDs did not. It is accepted though not all PDs are necessarily real or important. Outcome data are needed to confirm whether noninvasive mapping can predict patients likely to respond to PVI.

Paroxysmal Atrial Fibrillation With Both Triggers and Rotational Drivers Within the Right Atrial Appendage

A 48-year-old woman with paroxysmal atrial fibrillation (AF) underwent an electrophysiology study after 2 previous failed ablations. Noninvasive mapping suggested AF initiation from the right atrial appendage (RAA) with rotational drivers of AF in the RAA. Invasive mapping confirmed these findings. The patient was successfully treated with cryoballoon RAA isolation. (Level of Difficulty: Intermediate.).

Addressing challenges of quantitative methodologies and event interpretation in the study of atrial fibrillation

Atrial fibrillation (AF) is the commonest arrhythmia, yet the mechanisms of its onset and persistence are incompletely known. Although techniques for quantitative assessment have been investigated, there have been few attempts to integrate this information to advance disease treatment protocols. In this review, key quantitative methods for AF analysis are described, and suggestions are provided for the coordination of the available information, and to develop foci and directions for future research efforts. Quantitative biologists may have an interest in this topic in order to develop machine learning and tools for arrhythmia characterization, but they may perhaps have a minimal background in the clinical methodology and in the types of observed events and mechanistic hypotheses that have thus far been developed. We attempt to address these issues via exploration of the published literature. Although no new data is presented in this review, examples are shown of current lines of investigation, and in particular, how electrogram analysis and whole-chamber quantitative modeling of the left atrium may be useful to characterize fibrillatory patterns of activity, so as to propose avenues for more efficacious acquisition and interpretation of AF data.

Focal activation patterns: breaking new grounds in the pathophysiology of atrial fibrillation

INTRODUCTION

High-resolution atrial mapping studies have provided novel insights in the pathophysiology of atrial fibrillation (AF) in the last few years. Increasing attention is being drawn to the so-called focal activation patterns (FAPs); however, there is no consensus on criteria to identify and characterize these patterns. Areas covered: In this expert review, an overview of definitions and criteria used to examine FAPs obtained from atrial mapping studies is provided and studies reporting on the underlying mechanisms are discussed. Expert commentary: High-resolution cardiac mapping has revealed the importance of FAPs in the pathophysiology of AF. There is increasing evidence supporting the concept of endo-epicardial (E-E) asynchrony enabling transmural conduction of electrical waves resulting in FAPs. Uniform reports of FAPs in future studies are needed to provide more knowledge on its clinical importance.