Interactions between calcium-induced arrhythmia triggers and the electrophysiological-anatomical substrate underlying the induction of atrial fibrillation.

Atrial fibrillation (AF) is the most common cardiac arrhythmia and is sustained by spontaneous focal excitations and re-entry. Spontaneous electrical firing in the pulmonary vein (PV) sleeves is implicated in AF generation. The aim of this simulation study was to identify the mechanisms determining the localisation of AF triggers in the PVs and their contribution to the genesis of AF.

Patchy fibrosis promotes trigger-substrate interactions that both generate and maintain atrial fibrillation.

Fibrosis has been mechanistically linked to arrhythmogenesis in multiple cardiovascular conditions, including atrial fibrillation (AF). Previous studies have demonstrated that fibrosis can create functional barriers to conduction which may promote excitation wavebreak and the generation of re-entry, while also acting to pin re-entrant excitation in stable rotors during AF. However, few studies have investigated the role of fibrosis in the generation of AF triggers in detail.

Exploring interpretability in deep learning prediction of successful ablation therapy for atrial fibrillation.

Radiofrequency catheter ablation (RFCA) therapy is the first-line treatment for atrial fibrillation (AF), the most common type of cardiac arrhythmia globally. However, the procedure currently has low success rates in dealing with persistent AF, with a reoccurrence rate of ∼50% post-ablation. Therefore, deep learning (DL) has increasingly been applied to improve RFCA treatment for AF.

Imaging and biophysical modelling of thrombogenic mechanisms in atrial fibrillation and stroke.

Atrial fibrillation (AF) underlies almost one third of all ischaemic strokes, with the left atrial appendage (LAA) identified as the primary thromboembolic source. Current stroke risk stratification approaches, such as the CHADS-VASc score, rely mostly on clinical comorbidities, rather than thrombogenic mechanisms such as blood stasis, hypercoagulability and endothelial dysfunction-known as Virchow's triad. While detection of AF-related thrombi is possible using established cardiac imaging techniques, such as transoesophageal echocardiography, there is a growing need to reliably assess AF-patient thrombogenicity prior to thrombus formation.

Corrigendum: Reinforcement Learning to Improve Image-Guidance of Ablation Therapy for Atrial Fibrillation.

[This corrects the article DOI: 10.3389/fphys.2021.

Reinforcement Learning to Improve Image-Guidance of Ablation Therapy for Atrial Fibrillation.

Atrial fibrillation (AF) is the most common cardiac arrhythmia and currently affects more than 650,000 people in the United Kingdom alone. Catheter ablation (CA) is the only AF treatment with a long-term curative effect as it involves destroying arrhythmogenic tissue in the atria. However, its success rate is suboptimal, approximately 50% after a 2-year follow-up, and this high AF recurrence rate warrants significant improvements.

Toward Patient-Specific Prediction of Ablation Strategies for Atrial Fibrillation Using Deep Learning.

Atrial fibrillation (AF) is a common cardiac arrhythmia that affects 1% of the population worldwide and is associated with high levels of morbidity and mortality. Catheter ablation (CA) has become one of the first line treatments for AF, but its success rates are suboptimal, especially in the case of persistent AF. Computational approaches have shown promise in predicting the CA strategy using simulations of atrial models, as well as applying deep learning to atrial images.

Identifying locations of re-entrant drivers from patient-specific distribution of fibrosis in the left atrium.

Clinical evidence suggests a link between fibrosis in the left atrium (LA) and atrial fibrillation (AF), the most common sustained arrhythmia. Image-derived fibrosis is increasingly used for patient stratification and therapy guidance. However, locations of re-entrant drivers (RDs) sustaining AF are unknown and therapy success rates remain suboptimal.

Prolonged ursodeoxycholic acid administration reduces acute ischaemia-induced arrhythmias in adult rat hearts.

Acute myocardial ischaemia and reperfusion (I-R) are major causes of ventricular arrhythmias in patients with a history of coronary artery disease. Ursodeoxycholic acid (UDCA) has previously been shown to be antiarrhythmic in fetal hearts. This study was performed to investigate if UDCA protects against ischaemia-induced and reperfusion-induced arrhythmias in the adult myocardium, and compares the effect of acute (perfusion only) versus prolonged (2 weeks pre-treatment plus perfusion) UDCA administration.

Modeling Left Atrial Flow, Energy, Blood Heating Distribution in Response to Catheter Ablation Therapy.

Atrial fibrillation (AF) is a widespread cardiac arrhythmia that commonly affects the left atrium (LA), causing it to quiver instead of contracting effectively. This behavior is triggered by abnormal electrical impulses at a specific site in the atrial wall. Catheter ablation (CA) treatment consists of isolating this driver site by burning the surrounding tissue to restore sinus rhythm (SR).

Image-Based Computational Evaluation of the Effects of Atrial Wall Thickness and Fibrosis on Re-entrant Drivers for Atrial Fibrillation.

Catheter ablation (CA) is a common treatment for atrial fibrillation (AF), but the knowledge of optimal ablation sites, and hence clinical outcomes, are suboptimal. Increasing evidence suggest that ablation strategies based on patient-specific substrates information, such as distributions of fibrosis and atrial wall thickness (AWT), may be used to improve therapy. We hypothesized that competing influences of large AWT gradients and fibrotic patches on conductive properties of atrial tissue can determine locations of re-entrant drivers (RDs) sustaining AF.

Algorithms for left atrial wall segmentation and thickness - Evaluation on an open-source CT and MRI image database.

Structural changes to the wall of the left atrium are known to occur with conditions that predispose to Atrial fibrillation. Imaging studies have demonstrated that these changes may be detected non-invasively. An important indicator of this structural change is the wall's thickness.

Novel MRI Technique Enables Non-Invasive Measurement of Atrial Wall Thickness.

Knowledge of atrial wall thickness (AWT) has the potential to provide important information for patient stratification and the planning of interventions in atrial arrhythmias. To date, information about AWT has only been acquired in post-mortem or poor-contrast computed tomography (CT) studies, providing limited coverage and highly variable estimates of AWT. We present a novel contrast agent-free MRI sequence for imaging AWT and use it to create personalized AWT maps and a biatrial atlas.

Novel Computational Analysis of Left Atrial Anatomy Improves Prediction of Atrial Fibrillation Recurrence after Ablation.

The left atrium (LA) can change in size and shape due to atrial fibrillation (AF)-induced remodeling. These alterations can be linked to poorer outcomes of AF ablation. In this study, we propose a novel comprehensive computational analysis of LA anatomy to identify what features of LA shape can optimally predict post-ablation AF recurrence.

Towards patient-specific modelling of lesion formation during radiofrequency catheter ablation for atrial fibrillation.

Radiofrequency catheter ablation procedures are a first-line method of clinical treatment for atrial fibrillation. However, they suffer from suboptimal success rates and are also prone to potentially serious adverse effects. These limitations can be at least partially attributed to the inter- and intra- patient variations in atrial wall thickness, and could be mitigated by patient-specific approaches to the procedure.

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.

Slow Conduction in the Border Zones of Patchy Fibrosis Stabilizes the Drivers for Atrial Fibrillation: Insights from Multi-Scale Human Atrial Modeling.

The genesis of atrial fibrillation (AF) and success of AF ablation therapy have been strongly linked with atrial fibrosis. Increasing evidence suggests that patient-specific distributions of fibrosis may determine the locations of electrical drivers (rotors) sustaining AF, but the underlying mechanisms are incompletely understood. This study aims to elucidate a missing mechanistic link between patient-specific fibrosis distributions and AF drivers.

Role of atrial tissue substrate and electrical activation pattern in fractionation of atrial electrograms: a computational study.

Complex fractionated atrial electrograms (CFAEs) are often used as a clinical marker for re-entrant drivers of atrial fibrillation. However, outcomes of clinical ablation procedures based on CFAEs are controversial and the mechanistic links between fractionation, re-entrant activity and the characteristics of the atrial substrate are not completely understood. We explore such links by simulating electrograms arising from both normal and re-entrant electrical activity in atrial tissue models.

Evolution and pharmacological modulation of the arrhythmogenic wave dynamics in canine pulmonary vein model.

Aims: Atrial fibrillation (AF), the commonest cardiac arrhythmia, has been strongly linked with arrhythmogenic sources near the pulmonary veins (PVs), but underlying mechanisms are not fully understood. We aim to study the generation and sustenance of wave sources in a model of the PV tissue.

Methods And Results: A previously developed biophysically detailed three-dimensional canine atrial model is applied.

Heterogeneous and anisotropic integrative model of pulmonary veins: computational study of arrhythmogenic substrate for atrial fibrillation.

Mechanisms underlying the genesis of re-entrant substrate for the most common cardiac arrhythmia, atrial fibrillation (AF), are not well understood. In this study, we develop a multi-scale three-dimensional computational model that integrates cellular electrophysiology of the left atrium (LA) and pulmonary veins (PVs) with the respective tissue geometry and fibre orientation. The latter is reconstructed in unique detail from high-resolution (approx.

A novel computational sheep atria model for the study of atrial fibrillation.

Sheep are often used as animal models for experimental studies into the underlying mechanisms of cardiac arrhythmias. Previous studies have shown that biophysically detailed computer models of the heart provide a powerful alternative to experimental animal models for underpinning such mechanisms. In this study, we have developed a family of mathematical models for the electrical action potentials of various sheep atrial cell types.

Pro-arrhythmogenic effects of atrial fibrillation-induced electrical remodelling: insights from the three-dimensional virtual human atria.

Chronic atrial fibrillation (AF) is associated with structural and electrical remodelling in the atria, which are associated with a high recurrence of AF. Through biophysically detailed computer modelling, this study investigated mechanisms by which AF-induced electrical remodelling promotes and perpetuates AF. A family of Courtemanche-Ramirez-Nattel variant models of human atrial cell action potentials (APs), taking into account of intrinsic atrial electrophysiological properties, was modified to incorporate various experimental data sets on AF-induced changes of major ionic channel currents (ICaL, IKur, Ito, IK1, IKs, INaCa) and on intracellular Ca(2+) handling.