ArticleChaos (Woodbury, N.Y.)2017
Sensitivity of reentrant driver localization to electrophysiological parameter variability in image-based computational models of persistent atrial fibrillation sustained by a fibrotic substrate.
Article in Chaos (Woodbury, N.Y.), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 51 papers.
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Who cites it
51 citing papers in PubMed, 70 citations in OpenAlex.
- The Hodgkin-Huxley-Katz Prize Lecture: Heart digital twins: A new paradigm in arrhythmia medicine.The Journal of physiology · 2026Article
- Atrial fibrosis in atrial fibrillation: Mechanisms, mapping techniques and clinical applications.The Journal of physiology · 2026Review
- 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
- Towards mechanisms-driven strategy for persistent atrial fibrillation ablation: Leveraging digital twins.Journal of precision medicine (Amsterdam, Netherlands) · 2026Article
- The effect of clinically relevant changes in extracellular electrolyte concentrations on human atrial arrhythmias.Communications medicine · 2025Article
- Digital Twin Models in Atrial Fibrillation: Charting the Future of Precision Therapy?Journal of personalized medicine · 2025Review
- Short term hemodynamic effects of atrial fibrillation in a closed-loop human cardiac-baroreflex system.PloS one · 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
- Impact of effective refractory period personalization on arrhythmia vulnerability in patient-specific atrial computer models.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
- Progress of Conductivity and Conduction Velocity Measured in Human and Animal Hearts.Reviews in cardiovascular medicine · 2024Review
- Computational modeling of cardiac electrophysiology and arrhythmogenesis: toward clinical translation.Physiological reviews · 2024Review
- Up digital and personal: How heart digital twins can transform heart patient care.Heart rhythm · 2024Review
- Computational Modeling and Simulation of the Fibrotic Human Atria.Methods in molecular biology (Clifton, N.J.) · 2024Article
- A Review of Personalised Cardiac Computational Modelling Using Electroanatomical Mapping Data.Arrhythmia & electrophysiology review · 2024Review
- Patchy fibrosis promotes trigger-substrate interactions that both generate and maintain atrial fibrillation.Interface focus · 2023Article
- Impact of noise on the instability of spiral waves in stochastic 2D mathematical models of human atrial fibrillation.Journal of biological physics · 2023Article
- Comparing Inducibility of Re-Entrant Arrhythmia in Patient-Specific Computational Models to Clinical Atrial Fibrillation Phenotypes.JACC. Clinical electrophysiology · 2023Article
- Explainable Machine Learning to Predict Anchored Reentry Substrate Created by Persistent Atrial Fibrillation Ablation in Computational Models.Journal of the American Heart Association · 2023Article
- Personalized ablation vs. conventional ablation strategies to terminate atrial fibrillation and prevent recurrence.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 · 2023Article
- High-density mapping of the average complex interval helps localizing atrial fibrillation drivers and predicts catheter ablation outcomes.Frontiers in cardiovascular medicine · 2023Article
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Authors and funding
8 authors at 1 institution in 1 country.
Funding
Abstract
Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia, causing morbidity and mortality in millions worldwide. The atria of patients with persistent AF (PsAF) are characterized by the presence of extensive and distributed atrial fibrosis, which facilitates the formation of persistent reentrant drivers (RDs, i.e., spiral waves), which promote fibrillatory activity. Targeted catheter ablation of RD-harboring tissues has shown promise as a clinical treatment for PsAF, but the outcomes remain sub-par. Personalized computational modeling has been proposed as a means of non-invasively predicting optimal ablation targets in individual PsAF patients, but it remains unclear how RD localization dynamics are influenced by inter-patient variability in the spatial distribution of atrial fibrosis, action potential duration (APD), and conduction velocity (CV). Here, we conduct simulations in computational models of fibrotic atria derived from the clinical imaging of PsAF patients to characterize the sensitivity of RD locations to these three factors. We show that RDs consistently anchor to boundaries between fibrotic and non-fibrotic tissues, as delineated by late gadolinium-enhanced magnetic resonance imaging, but those changes in APD/CV can enhance or attenuate the likelihood that an RD will anchor to a specific site. These findings show that the level of uncertainty present in patient-specific atrial models reconstructed without any invasive measurements (i.e., incorporating each individual's unique distribution of fibrotic tissue from medical imaging alongside an average representation of AF-remodeled electrophysiology) is sufficiently high that a personalized ablation strategy based on targeting simulation-predicted RD trajectories alone may not produce the desired result.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.