ArticleeLife2021
Computational modeling identifies embolic stroke of undetermined source patients with potential arrhythmic substrate.
Article in eLife, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it, 27 citations in OpenAlex.
- Assessment of Left Atrial Fibrosis by Cardiac Magnetic Resonance Imaging in Ischemic Stroke Patients Without Atrial Fibrillation: A Systematic Review and Meta-Analysis.Journal of the American Heart Association · 2024Pooled it
- Systematic computational assessment of atrial function impairment due to fibrotic remodeling in electromechanical properties.PLoS computational biology · 2025Article
- Predicting arrhythmia recurrence post-ablation in atrial fibrillation using explainable machine learning.Communications medicine · 2025Article
- Population-based computational simulations elucidate mechanisms of focal arrhythmia following stem cell injection.Journal of molecular and cellular cardiology · 2025Article
- Multiphysics simulations reveal haemodynamic impacts of patient-derived fibrosis-related changes in left atrial tissue mechanics.The Journal of physiology · 2024Article
- Computational Medicine: What Electrophysiologists Should Know to Stay Ahead of the Curve.Current cardiology reports · 2024Review
- Computational modeling of cardiac electrophysiology and arrhythmogenesis: toward clinical translation.Physiological reviews · 2024Review
- 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
- Evaluation of an open-source pipeline to create patient-specific left atrial models: A reproducibility study.Computers in biology and medicine · 2023Article
- Graft-host coupling changes can lead to engraftment arrhythmia: a computational study.The Journal of physiology · 2023Article
- Elevated fibrosis burden as assessed by MRI predicts cryoballoon ablation failure.Journal of cardiovascular electrophysiology · 2023Article
- Machine Learning and the Conundrum of Stroke Risk Prediction.Arrhythmia & electrophysiology review · 2023Review
- The age of computational cardiology and future of long-term ablation target prediction for ventricular tachycardia.Frontiers in cardiovascular medicine · 2023Article
- Article
- Article
- Personalized biomechanical insights in atrial fibrillation: opportunities & challenges.Expert review of cardiovascular therapyReview
Corrections and comments
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Authors and funding
13 authors at 3 institutions in 3 countries.
Funding
Abstract
Cardiac magnetic resonance imaging (MRI) has revealed fibrosis in embolic stroke of undetermined source (ESUS) patients comparable to levels seen in atrial fibrillation (AFib). We used computational modeling to understand the absence of arrhythmia in ESUS despite the presence of putatively pro-arrhythmic fibrosis. MRI-based atrial models were reconstructed for 45 ESUS and 45 AFib patients. The fibrotic substrate's arrhythmogenic capacity in each patient was assessed computationally. Reentrant drivers were induced in 24/45 (53%) ESUS and 22/45 (49%) AFib models. Inducible models had more fibrosis (16.7 ± 5.45%) than non-inducible models (11.07 ± 3.61%; p<0.0001); however, inducible subsets of ESUS and AFib models had similar fibrosis levels (p=0.90), meaning that the
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Registered trials
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.