ArticlePLoS computational biology2020
Identifying locations of re-entrant drivers from patient-specific distribution of fibrosis in the left atrium.
Article in PLoS computational biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Structural determinants of re-entrant drivers in atrial fibrillation: insights from digital twins derived from 3D micrometre-resolution imaging of human heart.The Journal of physiology · 2026Article
- Short term hemodynamic effects of atrial fibrillation in a closed-loop human cardiac-baroreflex system.PloS one · 2025Article
- DGM-TOP: automatic identification of the critical boundaries in atrial tachycardia.Frontiers in physiology · 2025Article
- Evolution in electrophysiology 100 years after Einthoven: translational and computational innovations in rhythm control of atrial fibrillation.Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology · 2024Review
- Are drivers recurring or ephemeral? observations from serial mapping of persistent atrial fibrillation.Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology · 2024Article
- Systematic in-silico evaluation of fibrosis effects on re-entrant wave dynamics in atrial tissue.Scientific reports · 2024Article
- Patchy fibrosis promotes trigger-substrate interactions that both generate and maintain atrial fibrillation.Interface focus · 2023Article
- Multi-modal characterization of the left atrium by a fully automated integration of pre-procedural cardiac imaging and electro-anatomical mapping.International journal of cardiology. Heart & vasculature · 2023Article
- Mechanistic insight into the functional role of human sinoatrial node conduction pathways and pacemaker compartments heterogeneity: A computer model analysis.PLoS computational biology · 2023Article
- A simple approach for image-based modelling of the heart that enables robust simulation of highly heterogeneous electrical excitation.Scientific reports · 2023Article
- Exploring interpretability in deep learning prediction of successful ablation therapy for atrial fibrillation.Frontiers in physiology · 2023Article
- Dynamic electrophysiological mechanism in patients with long-standing persistent atrial fibrillation.Frontiers in cardiovascular medicine · 2022Article
- Identifying locations susceptible to micro-anatomical reentry using a spatial network representation of atrial fibre maps.PloS one · 2022Article
- Left atrial evaluation by cardiovascular magnetic resonance: sensitive and unique biomarkers.European heart journal. Cardiovascular Imaging · 2021Article
- Current progress of computational modeling for guiding clinical atrial fibrillation ablation.Journal of Zhejiang University. Science. B · 2021Review
- Computational models of atrial fibrillation: achievements, challenges, and perspectives for improving clinical care.Cardiovascular research · 2021Review
- EP-PINNs: Cardiac Electrophysiology Characterisation Using Physics-Informed Neural Networks.Frontiers in cardiovascular medicine · 2021Article
- Reinforcement Learning to Improve Image-Guidance of Ablation Therapy for Atrial Fibrillation.Frontiers in physiology · 2021Article
- Machine Learning Identification of Pro-arrhythmic Structures in Cardiac Fibrosis.Frontiers in physiology · 2021Article
- A Computational Study of the Electrophysiological Substrate in Patients Suffering From Atrial Fibrillation.Frontiers in physiology · 2021Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
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. This study used image-derived LA models to explore the dynamics of RD stabilization in fibrotic regions and generate maps of RD locations. LA models with patient-specific geometry and fibrosis distribution were derived from late gadolinium enhanced magnetic resonance imaging of 6 AF patients. In each model, RDs were initiated at multiple locations, and their trajectories were tracked and overlaid on the LA fibrosis distributions to identify the most likely regions where the RDs stabilized. The simulations showed that the RD dynamics were strongly influenced by the amount and spatial distribution of fibrosis. In patients with fibrosis burden greater than 25%, RDs anchored to specific locations near large fibrotic patches. In patients with fibrosis burden below 25%, RDs either moved near small fibrotic patches or anchored to anatomical features. The patient-specific maps of RD locations showed that areas that harboured the RDs were much smaller than the entire fibrotic areas, indicating potential targets for ablation therapy. Ablating the predicted locations and connecting them to the existing pulmonary vein ablation lesions was the most effective in-silico ablation strategy.
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