Diffusion Reaction Eikonal Alternant Model: Towards Fast Simulations of Complex Cardiac Arrhythmias

Reaction-diffusion (RD) computer models are suitable to investigate the mechanisms of cardiac arrthymias but not directly applicable in clinical settings due to their computational cost. On the other hand, alternative faster eikonal models are incapable of reproducing reentrant activation when solved by iterative methods. The diffusion reaction eikonal alternant model (DREAM) is a new method in which eikonal and RD models are alternated to allow for reactivation. To solve the eikonal equation, the fast iterative method was modified and embedded into DREAM. Obtained activation times control transmembrane voltage courses in the RD model computing, while repolarization times are provided back to the eikonal model. For a planar wave-front in the center of a 2D patch, DREAM action potentials (APs) have a small overshoot in the upstroke compared to pure RD simulations (monodomain) but similar AP duration. DREAM conduction velocity does not increase near boundaries or stimulated areas as it occurs in RD. Anatomical reentry was reproduced with the S1-S2 protocol. This is the first time that an iterative method is used to solve the eikonal model in a version that admits reactivation. This method can facilitate uptake of computer models in clinical settings. Further improvements will allow to accurately represent even more complex patterns of arrhythmi

Local Electrical Impedance Mapping of the Atria: Conclusions on Substrate Properties and Confounding Factors

The treatment of atrial fibrillation and other cardiac arrhythmias as a major cause of cardiovascular hospitalization has remained a challenge predominantly for patients with severely remodeled substrate. Individualized ablation strategies are extremely important both for pulmonary vein isolation and subsequent ablations. Current approaches to identifying arrhythmogenic regions rely on electrogram-based features such as activation time and voltage. Novel technologies now enable clinical assessment of the local impedance as tissue property. Previous studies demonstrated its use for ablation monitoring and indicated its potential to differentiate healthy substrate, scar, and pathological tissue. This study investigates the potential of local electrical impedance-based substrate mapping of the atria for human in-vivo data. The presented pipeline for impedance mapping particularly contains options for dealing with undesirable effects originating from cardiac motion, catheter motion, or proximity to other intracardiac devices. Bloodpool impedance was automatically determined as a patient-specific reference. Full-chamber, left atrial impedance maps were drawn up from interpolating the measured impedances to the atrial endocardium. Finally, the origin and magnitude of oscillations of the raw impedance recording were probed into. The most dominant reason for exclusion of impedance samples was the loss of endocardial contact. With median elevations above the bloodpool impedance between 29 and 46 Ω, the impedance within the pulmonary veins significantly exceeded the remaining atrial walls presenting median elevations above the bloodpool impedance between 16 and 20 Ω. Previous ablation lesions were distinguished from their surroundings by a significant drop in local impedance while the corresponding regions did not differ for the control group. The raw impedance was found to oscillate with median amplitudes between 6 and 17 Ω depending on the patient. Oscillations were traced back to an interplay of atrial, ventricular, and respiratory motion. In summary, local impedance measurements demonstrated their capability to distinguish pathological atrial tissue from physiological substrate. Methods to limit the influence of confounding factors that still hinder impedance mapping were presented. Measurements at different frequencies or the combination of multiple electrodes could lead to further improvement. The presented examples indicate that electrogram- and impedance-based substrate mapping have the potential to complement each other toward better patient outcomes in future

Non-Invasive Identification of Atrial Fibrillation Driver Location Using the 12-lead ECG: Pulmonary Vein Rotors vs. other Locations

Atrial fibrillation (AF) is an irregular heart rhythm due to disorganized atrial electrical activity, often sustained by rotational drivers called rotors. In the present work, we sought to characterize and discriminate whether simulated single stable rotors are located in the pulmonary veins (PVs) or not, only by using non-invasive signals (i.e., the 12-lead ECG). Several features have been extracted from the signals, such as Hjort descriptors, recurrence quantification analysis (RQA), and principal component analysis. All the extracted features have shown significant discriminatory power, with particular emphasis to the RQA parameters. A decision tree classifier achieved 98.48% accuracy, 83.33% sensitivity, and 100% specificity on simulated data.Clinical Relevance-This study might guide ablation procedures, suggesting doctors to proceed directly in some patients with a pulmonary veins isolation, and avoiding the prior use of an invasive atrial mapping system

Machine Learning to Find Areas of Rotors Sustaining Atrial Fibrillation from the ECG

Atrial fibrillation (AF) is the most frequent irregular heart rhythm due to disorganized atrial electrical activity, often sustained by rotational drivers called rotors. The non-invasive localization of AF drivers can lead to improved personalized ablation strategy, suggesting pulmonary vein (PV) isolation or more complex extra-PV ablation procedures in case the driver is on other atrial regions. We used a Machine Learning approach to characterize and discriminate simulated single stable rotors (1R) location: PVs, left atrium (LA) excluding the PVs, and right atrium (RA), utilizing solely non-invasive signals (i.e., the 12-lead ECG). 1R episodes sustaining AF were simulated. 128 features were extracted from the signals. Greedy forward algorithm was implemented to select the best feature set which was fed to a decision tree classifier with hold-out cross-validation technique. All tested features showed significant discriminatory power, especially those based on recurrence quantification analysis (up to 80.9% accuracy with single feature classification). The decision tree classifier achieved 89.4% test accuracy with 18 features on simulated data, with sensitivities of 93.0%, 82.4%, and 83.3% for RA, LA, and PV classes, respectively. Our results show that a machine learning approach can potentially identify the location of 1R sustaining AF using the 12-lead ECG

Atrial Heterogeneity Generates Re-entrant Substrate during Atrial Fibrillation and Anti-arrhythmic Drug Action: Mechanistic Insights from Canine Atrial Models

Anti-arrhythmic drug therapy is a frontline treatment for atrial fibrillation (AF), but its success rates are highly variable. This is due to incomplete understanding of the mechanisms of action of specific drugs on the atrial substrate at different stages of AF progression. We aimed to elucidate the role of cellular, tissue and organ level atrial heterogeneities in the generation of a re-entrant substrate during AF progression, and their modulation by the acute action of selected anti-arrhythmic drugs. To explore the complex cell-to-organ mechanisms, a detailed biophysical models of the entire 3D canine atria was developed. The model incorporated atrial geometry and fibre orientation from high-resolution micro-computed tomography, region-specific atrial cell electrophysiology and the effects of progressive AF-induced remodelling. The actions of multi-channel class III anti-arrhythmic agents vernakalant and amiodarone were introduced in the model by inhibiting appropriate ionic channel currents according to experimentally reported concentration-response relationships. AF was initiated by applied ectopic pacing in the pulmonary veins, which led to the generation of localized sustained re-entrant waves (rotors), followed by progressive wave breakdown and rotor multiplication in both atria. The simulated AF scenarios were in agreement with observations in canine models and patients. The 3D atrial simulations revealed that a re-entrant substrate was typically provided by tissue regions of high heterogeneity of action potential duration (APD). Amiodarone increased atrial APD and reduced APD heterogeneity and was more effective in terminating AF than vernakalant, which increased both APD and APD dispersion. In summary, the initiation and sustenance of rotors in AF is linked to atrial APD heterogeneity and APD reduction due to progressive remodelling. Our results suggest that anti-arrhythmic strategies that increase atrial APD without increasing its dispersion are effective in terminating AF

Left and Right Atrial Contribution to the P-wave in Realistic Computational Models

ECG markers derived from the P-wave are used frequently to assess atrial function and anatomy, e.g. left atrial enlargement. While having the advantage of being routinely acquired, the processes underlying the genesis of the P-wave are not understood in their entirety. Particularly the distinct contributions of the two atria have not been analyzed mechanistically. We used an in silico approach to simulate P-waves originating from the left atrium (LA) and the right atrium (RA) separately in two realistic models. LA contribution to the P-wave integral was limited to 30% or less. Around 20% could be attributed to the first third of the P-wave which reflected almost only RA depolarization. Both atria contributed to the second and last third with RA contribution being about twice as large as LA contribution. Our results foster the comprehension of the difficulties related to ECG-based LA assessment

Alterations of atrial electrophysiology related to hemodialysis session: insights from a multiscale computer model

Background: The prevalence of atrial fibrillation is increased in patients with end-stage renal disease. Previous studies suggested that extracellular electrolyte alterations caused by hemodialysis (HD) therapy could be proarrhythmic. Methods: Multiscale models were used for a consequent analysis of the effects of extracellular ion concentration changes on atrial electrophysiology. Simulations were based on measured electrolyte concentrations from patients with end-stage renal disease. Results: Simulated conduction velocity and effective refractory period are decreased at the end of an HD session, with potassium having the strongest influence. P-wave is prolonged in patients undergoing HD therapy in the simulation as in measurements. Conclusions: Electrolyte concentration alterations impact atrial electrophysiology from the action potential level to the P-wave and can be proarrhythmic, especially because of induced hypokalemia. Analysis of blood electrolytes enables patient-specific electrophysiology modeling. We are providing a tool to investigate atrial arrhythmias associated with HD therapy, which, in the future, can be used to prevent such complications