ArticleAnnals of biomedical engineering2026
Influence of Anatomical Definition and Substrate Condition on Simulations of Atrial Arrhythmias.
Article in Annals of biomedical engineering, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Catheter configuration for mapping micro-anatomic reentries sustaining atrial fibrillation: A simulation study.PLoS computational biology · 2026Article
- Virtual population-based analysis of anatomical and electrophysiological risk markers for atrial fibrillation.JRSM cardiovascular diseaseArticle
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Authors and funding
5 authors.
Funding
Abstract
introductionPersonalized in silico models offer great potential for improving our understanding of atrial arrhythmia mechanisms and for testing therapeutic strategies. However, the level of complexity that can be achieved in these models is often limited by the availability of clinical data. Therefore, evaluating how variations in model detail affect electrophysiological simulation outcomes is essential for evaluating how accurate these models need to be. PURPOSE: To evaluate how substrate conditions (electrical remodeling and diffusion) and the inclusion of fiber orientation and ionic heterogeneities provide significant differences in simulation outcomes.
methodsN = 320 simulations were performed in a realistic 3D model of the human atria considering different conditions: the presence and absence of fiber orientation, regional ionic heterogeneities and different levels of electrical remodeling and electrical diffusion, simulating disease progression. Their arrhythmic behavior was evaluated as the percentage of cases that sustained atrial arrhythmias and the classification of these according to their type as either functional or anatomical.
resultsFiber orientation and electrical diffusion were the parameters that most strongly influenced the simulation outcomes. Including fiber orientation significantly altered both the incidence (44% vs. 67%, p = 0.02) and type of reentries (31% vs. 57% functional reentries, p = 0.055). In terms of electrical diffusion, reduced conduction velocity led to a significantly higher arrhythmogenicity (83% vs. 33%, p = 0.008) and an increased proportion of functional reentries (59% vs. 29%, p = 0.08). Electrical remodeling and ionic heterogeneities did not provide statistically significant differences. However, ionic heterogeneities proved to play an important role in generating different proarrhythmic patterns.
conclusionFiber orientation and electrical diffusion play a relevant role in personalized simulations, since these parameters produced significant effects in simulation behavior. Therefore, they should be considered for personalized simulations. Electrical remodeling and tissue heterogeneity have a minor impact in terms of arrhythmia inducibility, but they should be considered to evaluate specific mechanisms and/or target regions in personalized simulations.
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