Functional and Structural Remodeling as Atrial Fibrillation Progresses in a Persistent Atrial Fibrillation Canine Model.

Background: Contractile, electrical, and structural remodeling has been associated with atrial fibrillation (AF), but the progression of functional and structural changes as AF sustains has not been previously evaluated serially.

Objectives: Using a rapid-paced persistent AF canine model, the authors aimed to evaluate the structural and functional changes serially as AF progresses.

Methods: Serial electrophysiological studies in a chronic rapid-paced canine model (n = 19) prior to AF sustaining and repeated at 1, 3, and 6 months of sustained AF were conducted to measure changes in atrial conduction speed and direction.

Effective ablation settings that predict chronic scar after atrial ablation with HELIOSTAR™ multi-electrode radiofrequency balloon catheter.

Background: Radiofrequency balloon (RFB) ablation (HELIOSTAR™, Biosense Webster) has been developed to improve pulmonary vein ablation efficiency over traditional point-by-point RF ablation approaches. We aimed to find effective parameters for RFB ablation that result in chronic scar verified by late gadolinium enhancement cardiac magnetic resonance (LGE-CMR).

Methods: A chronic canine model (n = 8) was used to ablate in the superior vena cava (SVC), the right superior and the left inferior pulmonary vein (RSPV and LIPV), and the left atrial appendage (LAA) with a circumferential ablation approach (RF energy was delivered to all electrodes simultaneously) for 20 s or 60 s.

Are drivers recurring or ephemeral? observations from serial mapping of persistent atrial fibrillation.

Aims: Rotational re-entries and ectopic foci, or 'drivers', are proposed mechanisms for persistent atrial fibrillation (persAF), but driver-based interventions have had mixed success in clinical trials. Selective targeting of drivers with multi-month stability may improve these interventions, but no prior work has investigated whether drivers can be stable on such a long timescale.

Objective: We hypothesized that drivers could recur even several months after initial observation.

Uncertainty Quantification of the Effect of Variable Conductivity in Ventricular Fibrotic Regions on Ventricular Tachycardia.

Ventricular tachycardia (VT) is a life-threatening cardiac arrhythmia for which a common treatment pathway is electroanatomical mapping and ablation. Recent advances in both noninvasive ablation techniques and computational modeling have motivated the development of patient-specific computational models of VT. Such models are parameterized by a wide range of inputs, each of which is associated with an often unknown amount of error and uncertainty.

Effects of Biventricular Pacing Locations on Anti-Tachycardia Pacing Success in a Patient-Specific Model.

Patients with drug-refractory ventricular tachycardia (VT) often undergo implantation of a cardiac defibrillator (ICD). While life-saving, shock from an ICD can be traumatic. To combat the need for defibrillation, ICDs come equipped with low-energy pacing protocols.

Transfer Learning for Improved Classification of Drivers in Atrial Fibrillation.

"Drivers" are theorized mechanisms for persistent atrial fibrillation. Machine learning algorithms have been used to identify drivers, but the small size of current driver datasets limits their performance. We hypothesized that pretraining with unsupervised learning on a large dataset of unlabeled electrograms would improve classifier accuracy on a smaller driver dataset.

Deep Learning Based Prediction of Atrial Fibrillation Disease Progression with Endocardial Electrograms in a Canine Model.

Objective: We sought to determine whether electrical patterns in endocardial wavefronts contained elements specific to atrial fibrillation (AF) disease progression.

Methods: A canine paced model (n=7, female mongrel, 29±2 kg) of persistent AF was endocardially mapped with a 64-electrode basket catheter during periods of AF at 1 month, 3 month, and 6 months post-implant of stimulator. A 50-layer residual network was then trained to map half-second electrogram samples to their source timepoint.