Mapping and Ablation of Rotational and Focal Drivers in Atrial Fibrillation

Non-stationary components in Electrograms localize arrhythmogenic substrates in a 3D model of human atria

Catheter ablation, as a treatment for atrial fibrillation (AF), often yields low success rates in the advanced stages of the arrhythmia. Ablation procedures are guided by atrial mapping using electrogram (EGM) signals, which reflect local electrical activations. The primary goal is to identify arrhythmogenic mechanisms, such as rotors, to serve as ablation targets. Given the chaotic nature of AF propagation, these electrical activations occur at variable rates. This work introduces a novel signal processing approach based on the fractional Fourier transform (FrFT) to characterize the non-stationary content in EGM signals. A 3D biophysical and anatomical model of human atria was used to simulate AF, and unipolar EGMs were calculated. The FrFT-based algorithm was applied to all EGM signals, estimating the optimal FrFT order to capture linear frequency modulations. Electroanatomical maps of these optimal FrFT orders were generated. Results revealed that the AF EGMs exhibit non-stationarity, which can be characterized using the FrFT. Rotors displayed a distinct pattern of non-stationarity, allowing for dynamic tracking, while transient mechanisms were identifiable through variations in the FrFT order, showing different patterns than those of rotors. As a generalization of the classical Fourier analysis, FrFT mapping offers clinically interpretable insights into the rate of change in EGM frequency content over time. This method proves valuable for characterizing AF spatiotemporal dynamics by leveraging the non-stationary information inherent in fibrillatory propagation.

Artificial intelligence-adjudicated spatiotemporal dispersion: A patient-unique fingerprint of persistent atrial fibrillation

BACKGROUND

Spatiotemporal dispersion-guided ablation is a tailored approach for patients in persistent atrial fibrillation (PsAF). The characterization of dispersion extent and distribution and its association with common clinical descriptors of PsAF patients has not been studied.

OBJECTIVES

Artificial intelligence-adjudicated dispersion extent and distribution (AI-DED) was obtained with a machine/deep learning classifier (VX1 Software, Volta Medical) in PsAF patients undergoing ablation. The purpose of this study was to test the hypothesis that AI-DED is unique to each patient and independent of common procedural and clinical parameters.

METHODS

In a subanalysis of the Ev-AIFib study (NCT03434964), spatiotemporal dispersion maps were built with VX1 software in 78 consecutive persistent and long-standing PsAF patients. AI-DED was quantified using 2 distinct approaches (visual regional characterization or automated global quantification of AI-DED).

RESULTS

AI-DED paired-subregion Euclidean distance measurements between 78 patients (average distance 5.07 ± 0.60; min 2.23; max 9.75) demonstrate that AI-DED is a patient-unique characteristic of PsAF. Importantly, both AF type and AF history do not correlate with AI-DED levels (R2 = 0.006, P = .53; and R2 = 0.03, P = .25, respectively). The most extensive AI-DED levels are not associated with poorer procedural (83%, 81%, and 83% of AF termination in low, medium, and high dispersion groups, respectively; P = .954) and long-term (88%, 75%, and 91% of freedom from AF/atrial tachycardia after multiple procedures; P = .517) outcomes.

CONCLUSION

The atrial distribution and extent of multipolar electrogram spatiotemporal dispersion follow a nonrandom, albeit patient-unique, distribution in PsAF patients. AI-DED may represent a procedure-implementable fingerprint of the PsAF substrate.

Recent advances in the tools available for atrial fibrillation ablation

INTRODUCTION

Atrial fibrillation (AF) is the commonest arrhythmia in clinical practice with significant detrimental health impacts. Much effort has been spent in mapping AF, determine its triggers and drivers, and how to develop tools to eliminate these triggers.

AREAS COVERED

In this state of-the-art review article, we aim to highlight the recent techniques in catheter-based management of Atrial Fibrillation; including new advancements either in the catheter design or the software used. This includes a comprehensive summary of the most recent tools used in AF mapping and subsequent ablation.

EXPERT OPINION

Electrical isolation of the pulmonary veins has been developed and established as the cornerstone in AF ablation with good results in patients with paroxysmal AF (PAF) whilst new ablation tools are aimed at streamlining the procedure. However, the quest for persistent AF (PeAF) remains. The future of AF ablation, we believe, lies in identifying AF drivers by means of the new developing mapping tools and altering their electrical properties in a safe, reproducible, and effective manner.

E/E′ Is a New Independent Predictor of Recovered Ejection Fraction in Patients With Systolic Heart Failure Undergoing Ablation for Atrial Fibrillation

Aims: Catheter ablation should be considered in patients with atrial fibrillation (AF) and with heart failure (HF) with reduced ejection fraction (EF; HFrEF) to improve survival and reduce heart failure hospitalization. Careful patient selection for AF ablation is key to achieving similar outcome benefits. However, limited data exist regarding predictors of recovered ejection fraction. We aimed to evaluate the predictors of recovered ejection fraction in consecutive patients with HF undergoing AF ablation.

Methods and Results: A total of 156 patients [67.3% men, median age 63 (11)] with AF and HF underwent initial catheter ablation between September 2017 and October 2019 in the First Affiliated Hospital of Dalian Medical University. Overall, the percentage of recovered ejection fractions was 72.3%. Recovered EFs were associated with a 39% reduction in all-cause hospitalization compared to non-recovered EFs at the 1-year follow-up [23.8 vs. 62.8 (odds ratio) OR 2.09 (1.40–3.12), P < 0.001]. Univariate analysis for recovered EFs showed that diabetes (P = 0.083), prevalent HF (P = 0.014), prevalent AF (P = 0.051), LVEF (P = 0.022), and E/E′ (P = 0.001) were associated with EF improvement. Multivariate analysis showed that the only independent predictor of EF recovery was E/E′ [OR 1.13 (1.03–1.24); P = 0.011]. A receiver operating characteristic analysis determined that the suitable cut-off value for E/E′ was 15 (sensitivity 38.7%, specificity 89.2%, the area under curve 0.704).

Conclusions: Ejection fraction (EF) recovery occurred in 72.3% of patients, associated with a 39% reduction in all-cause hospitalization compared to the non-recovered EFs in our cohort. The only independent predictor of recovered EF was E/E′ < 15 in our series.

A novel noncontact high-resolution charge density mapping system to guide ablation of complex atrial arrhythmias: overview of device technology and application

Introduction: Despite advances in imaging technology to guide ablative therapies, catheter ablation of more complex arrhythmias continues to be a challenge in part due to suboptimal mechanistic understanding of these arrhythmias by conventional mapping systems. A novel noncontact charge density mapping system has been designed to overcome some of these limitations.Areas covered: Hereby, we present an overview of this novel, charge density-based global chamber mapping approach. We initially highlight the concept of charge density, describe further the device technology and finally summarize the clinical application of this system.Expert opinion: Noncontact charge density mapping provides a more localized, high-resolution global panoramic assessment of cardiac activation. This might contribute to uncover the mechanisms of more complex arrhythmias such as persistent atrial fibrillation or unstable atrial tachycardias with the ultimate goal to guide the ablation therapy.

Translational applications of computational modelling for patients with cardiac arrhythmias

Cardiac arrhythmia is associated with high morbidity, and its underlying mechanisms are poorly understood. Computational modelling and simulation approaches have the potential to improve standard-of-care therapy for these disorders, offering deeper understanding of complex disease processes and sophisticated translational tools for planning clinical procedures. This review provides a clinician-friendly summary of recent advancements in computational cardiology. Organ-scale models automatically generated from clinical-grade imaging data are used to custom tailor our understanding of arrhythmia drivers, estimate future arrhythmogenic risk and personalise treatment plans. Recent mechanistic insights derived from atrial and ventricular arrhythmia simulations are highlighted, and the potential avenues to patient care (eg, by revealing new antiarrhythmic drug targets) are covered. Computational approaches geared towards improving outcomes in resynchronisation therapy have used simulations to elucidate optimal patient selection and lead location. Technology to personalise catheter ablation procedures are also covered, specifically preliminary outcomes form early-stage or pilot clinical studies. To conclude, future developments in computational cardiology are discussed, including improving the representation of patient-specific fibre orientations and fibrotic remodelling characterisation and how these might improve understanding of arrhythmia mechanisms and provide transformative tools for patient-specific therapy.