What is the clinical evidence from this study?

Study design: Other. Population: Atrial fibrillation (n=10). Intervention: Anatomical variability and patient-specific fibrosis vs. Non-fibrotic models / across different anatomies. Primary outcome: Atrial fibrillation initiation rates (p=0.37).

What does this research mean for the field?

Patient-specific atrial anatomy significantly influences atrial fibrillation reentry dynamics, whereas fibrosis plays a limited modifying role in patients with low fibrotic burden. Novelty: ClaimNovelty.INCREMENTAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

The study aims to explore how anatomical differences impact atrial fibrillation inducibility and dynamics through personalized models.

July 11, 2026Open Access

Analysis of the Effect of Anatomical Variability on Atrial Fibrillation Dynamics Through a Novel Framework for Personalised Atrial Model Generation

VMVictor Gonçalves MarquesMaastricht University AGAli GharaviriHeart Rhythm Society SPSimone PezzutoUniversity of Trento

Key Result

In 10 patient-specific in silico models, anatomical variability significantly influenced AF reentry dynamics (p<0.04), while fibrosis had a limited modifying role on AF initiation (53.5% vs 54.0%).

Key Points

The study aims to explore how anatomical differences impact atrial fibrillation inducibility and dynamics through personalized models.
Generated personalized atrial models from late gadolinium-enhanced MRI data.
Mapped intra- and inter-atrial structures onto patient-specific anatomies using universal coordinates.
Compared AF initiation rates and reentry dynamics in models with and without fibrosis.
Personalized models of 10 patients showed no significant effect of anatomical variability on AF initiation rates (p=0.43 non-fibrotic, p=0.37 fibrotic).
AF and macro-reentrant atrial tachycardia initiation rates varied significantly among fibrotic models (p=0.04).
Reentry formation and destruction rates significantly varied with anatomical differences in both fibrotic and non-fibrotic scenarios (p<0.04 and p<0.02 respectively).

Structured PICO

How do differences in atrial anatomy and fibrosis affect AF inducibility and dynamics in patient-specific in silico models?

Population

Personalised in silico atrial models were generated from 10 patients with predominantly low-fibrotic content to study the effects of anatomical variability and fibrosis on atrial fibrillation dynamics.

Exposure

In silico modeling of atrial fibrillation incorporating patient-specific endomysial fibrosis based on LGE-MRI.

Comparator

In silico models without fibrosis.

Outcome

AF and macro-reentrant atrial tachycardia (MRAT) initiation rates, and reentry dynamics (formation rates, destruction rates, and number of simultaneous reentries).surrogate

Patient-specific atrial anatomy significantly influences AF reentry dynamics, whereas fibrosis plays a limited modifying role in cohorts with low fibrotic burden.

Main Result

Absolute Event Rate: 53.5% vs 54%

p-value: p=0.37

Limitations

The cohort consisted predominantly of patients with low-fibrotic content (Utah stages ≤ 2), limiting generalizability to high-fibrosis populations.

Abstract

Abstract Purpose Atrial fibrillation (AF) is both influenced by and contributes to atrial structural remodelling, including atrial enlargement and fibrosis. In this work, we aimed to understand how differences in atrial anatomy affect AF inducibility and dynamics, both without and with fibrosis, through in silico models. Methods Atrial wall anatomies from late gadolinium-enhanced magnetic resonance imaging (LGE-MRI) were used to generate personalised models. Detailed intra- and inter-atrial structures (pectinate muscles, Bachmann’s bundle, fossa ovalis, coronary sinus, and fibre orientations) were mapped onto patient-specific atrial anatomies through a novel strategy using universal atrial coordinates and a highly detailed reference model. Patient-specific endomysial fibrosis was incorporated based on LGE-MRI. We quantified AF and macro-reentrant atrial tachycardia (MRAT) initiation rates in models with and without fibrosis, comparing reentry dynamics using renewal theory. Results We generated personalised models of 10 patients, all but one with low-fibrotic content (Utah stages ≤ 2). Anatomical variability did not significantly affect AF initiation rates in both non-fibrotic (54.0 ± 13.7%, p = 0.43) and fibrotic models (53.5 ± 15.3%, p = 0.37). For AF + MRAT, initiation rates varied significantly among fibrotic models ( p = 0.04) but not among non-fibrotic ones ( p = 0.14). Reentry formation rates varied significantly across anatomies both with and without fibrosis ( p < 0.04). With fibrosis, reentry destruction rates also varied significantly ( p < 0.02). The number of simultaneous reentries varied significantly across patient anatomies ( p < 0.001 without fibrosis, p < 0.05 with fibrosis), with fibrosis altering reentry counts in 2 patients ( p < 0.02). Conclusion Atrial anatomy influenced patient-specific variability in AF reentry dynamics, while fibrosis played a limited modifying role in this low-fibrotic cohort.

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