Patient-Specific Identification of Atrial Flutter Vulnerability–A Computational Approach to Reveal Latent Reentry Pathways

Abstract

Atypical atrial flutter (AFlut) is a reentrant arrhythmia which patients frequently develop after ablation for atrial fibrillation (AF). Indeed, substrate modifications during AF ablation can increase the likelihood to develop AFlut and it is clinically not feasible to reliably and sensitively test if a patient is vulnerable to AFlut. Here, we present a novel method based on personalized computational models to identify pathways along which AFlut can be sustained in an individual patient. We build a personalized model of atrial excitation propagation considering the anatomy as well as the spatial distribution of anisotropic conduction velocity and repolarization characteristics based on a combination of a priori knowledge on the population level and information derived from measurements performed in the individual patient. The fast marching scheme is employed to compute activation times for stimuli from all parts of the atria. Potential flutter pathways are then identified by tracing loops from wave front collision sites and constricting them using a geometric snake approach under consideration of the heterogeneous wavelength condition. In this way, all pathways along which AFlut can be sustained are identified. Flutter pathways can be instantiated by using an eikonal-diffusion phase extrapolation approach and a dynamic multifront fast marching simulation. In these dynamic simulations, the initial pattern eventually turns into the one driven by the dominant pathway, which is the only pathway that can be observed clinically. We assessed the sensitivity of the flutter pathway maps with respect to conduction velocity and its anisotropy. Moreover, we demonstrate the application of tailored models considering disease-specific repolarization properties (healthy, AF-remodeled, potassium channel mutations) as well as applicabiltiy on a clinical dataset. Finally, we tested how AFlut vulnerability of these substrates is modulated by exemplary antiarrhythmic drugs (amiodarone, dronedarone). Our novel method allows to assess the vulnerability of an individual patient to develop AFlut based on the personal anatomical, electrophysiological, and pharmacological characteristics. In contrast to clinical electrophysiological studies, our computational approach provides the means to identify all possible AFlut pathways and not just the currently dominant one. This allows to consider all relevant AFlut pathways when tailoring clinical ablation therapy in order to reduce the development and recurrence of AFlut.

Sensitivity of Ablation Targets Prediction to Electrophysiological Parameter Variability in Image-Based Computational Models of Ventricular Tachycardia in Post-infarction Patients

“…Computational modeling has emerged as a powerful platform for the non-invasive investigation of lethal heart rhythm disorders and their treatment (Behradfar et al, 2014; Pathmanathan and Gray, 2014; Grandi and Maleckar, 2016; Loewe et al, 2018; Roney et al, 2018a,b); it has been used for risk stratification in patients with myocardial infarction (MI) (Vadakkumpadan et al, 2014; Arevalo et al, 2016; Deng et al, 2016) and prediction of reentry location (Ashikaga et al, 2013; Deng et al, 2015; Zahid et al, 2016). Computational technology has also been recently developed to guide patient ablation (the Virtual-heart Arrhythmia Ablation Targeting, or VAAT), and even used prospectively, as a prove of feasibility of the approach, in a small number of patients (Prakosa et al, 2018).…”

Challenges Associated with Interpreting Mechanisms of AF

“…Computational models of atrial arrhythmia have been used to offer important insights into arrhythmia mechanisms. [ 128 – 130 ] A recent pioneering study from the Trayanova laboratory identified patient-specific targets for AF for patients with a fibrotic substrate. [ 131 ] However, patient-specific modelling of AF is challenging due to the anatomical and structural complexity of the atria and the dynamic nature of the electrical substrate.…”

A Reproducible Protocol to Assess Arrhythmia Vulnerability in silico: Pacing at the End of the Effective Refractory Period

“…Computational modeling has been proven to be a useful tool for assessing arrhythmia vulnerability (Arevalo et al, 2016 ; Zahid et al, 2016 ; Azzolin et al, 2020 ) and for supporting ablation planning (Lim et al, 2017 ; Boyle et al, 2019 ; Loewe et al, 2019 ; Roney et al, 2020 ). However, different protocols used to induce arrhythmia in simulations (Krummen et al, 2012 ; Matene and Jacquemet, 2012 ; Bayer et al, 2016 ; Roney et al, 2016 , 2018 ; Zahid et al, 2016 ; Boyle et al, 2019 ) are not only making studies difficult to compare, but are also influencing the decision on whether an atrial model is vulnerable to AF and are therefore crucial for identifying the optimal ablation targets.…”