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Atrial fibrillation (AF) is one of the most common cardiac diseases. However, a complete understanding of how to treat patients suffering from AF is still not achieved. As the isolation of the pulmonary veins in the left atrium (LA) is the standard treatment for AF, the role of the right atrium (RA) in AF is rarely considered. We investigated the impact of including the RA on arrhythmia vulnerability in silico. We generated a dataset of five mono-atrial (LA) and five bi-atrial models with three different electrophysiological (EP) setups each, regarding different states of AF-induced remodelling. For every model, a pacing protocol was run to induce reentries from a set of stimulation points. The average share of inducing points across all EP setups was 0.0, 0.8 and 6.7% for the mono-atrial scenario, 0.5, 27.3 and 37.9% for the biatrial scenario. The increase in inducibility of LA stimulation points from mono- to bi-atrial scenario was 0.91 ± 2.03%, 34.55 ± 14.9% and 44.2 ± 14.9 %, respectively. In this study, the RA had a marked impact on the results of the vulnerability assessment that needs to be further investigated.

Atrial fibrillation (AF) is the most common sustained arrhythmia posing a significant burden to patients and leading to an increased risk of stroke and heart failure. Additional ablation of areas of arrhythmogenic substrate in the atrial body detected by either late gadolinium enhancement magnetic resonance imaging (LGE-MRI) or electroanatomical mapping (EAM) may increase the success rate of restoring and maintaining sinus rhythm compared to the standard treatment procedure of pulmonary vein isolation (PVI). To evaluate if LGE-MRI and EAM identify equivalent substrate as potential ablation targets, we divided the left atrium (LA) into six clinically important regions in ten patients. Then, we computed the correlation between both modalities by analyzing the regional extents of identified pathological tissue. In this regional analysis, we observed no correlation between late gadolinium enhancement (LGE) and low voltage areas (LVA), neither in any region nor with regard to the entire atrial surface (-0.3

Atrial fibrillation (AF) is one of the most common cardiac diseases. However, a complete understanding of how to treat patients suffering from AF is still not achieved. As the isolation of the pulmonary veins in the left atrium (LA) is the standard treatment for AF, the role of the right atrium (RA) in AF is rarely considered. We investigated the impact of including the RA on arrhythmia vulnerability in silico. We generated a dataset of five mono-atrial (LA) and five bi-atrial models with three different electrophysiological (EP) setups each, regarding different states of AF-induced remodelling. For every model, a pacing protocol was run to induce reentries from a set of stimulation points. The average share of inducing points across all EP setups was 0.0, 0.8 and 6.7% for the mono-atrial scenario, 0.5, 27.3 and 37.9% for the biatrial scenario. The increase in inducibility of LA stimulation points from mono- to bi-atrial scenario was 0.91 ± 2.03%, 34.55 ± 14.9% and 44.2 ± 14.9 %, respectively. In this study, the RA had a marked impact on the results of the vulnerability assessment that needs to be further investigated. [ABSTRACT FROM AUTHOR]

Background and Aims: The effective refractory period (ERP) is one of the main electrophysiological properties governing arrhythmia, yet ERP personalisation is rarely performed when creating patient-specific computer models of the atria to inform clinical decision-making. This study evaluates the impact of integrating clinical ERP measurements into personalised in silico models on arrhythmia vulnerability., Methods: Clinical ERP measurements were obtained in seven patients from multiple locations in the atria. Atrial geometries from the electroanatomical mapping system were used to generate personalised anatomical atrial models. The Courtemanche cellular model was adjusted to reproduce patientspecific ERP. Four modelling approaches were compared: homogeneous (A), heterogeneous (B), regional (C), and continuous (D) ERP distributions. Non-personalised approaches (A, B) were based on literature data, while personalised approaches (C, D) were based on patient measurements. Modelling effects were assessed on arrhythmia vulnerability and tachycardia cycle length, with sensitivity analysis on ERP measurement uncertainty., Results: Mean vulnerability was 3.4±4.0%, 7.7±3.4%, 9.0±5.1%, 7.0±3.6% for scenarios A to D, respectively. Mean tachycardia cycle length was 167.1±12.6 ms, 158.4±27.5 ms, 265.2±39.9 ms, and 285.9±77.3 ms for scenarios A to D, respectively. Incorporating perturbations to the measured ERP in the range of 2, 5, 10, 20, and 50ms changed the vulnerability of the model to 5.8±2.7%, 6.1±3.5%, 6.9±3.7%, 5.2±3.5%, 9.7±10.0% respectively., Conclusion: Increased ERP dispersion had a greater effect on reentry dynamics than on vulnerability. Inducibility was higher in personalised scenarios compared to scenarios with uniformly reduced ERP; however, this effect was reversed when incorporating fibrosis informed by low voltage areas.ERP measurement uncertainty up to 20 ms slightly influenced vulnerability. Electrophysiological personalisation of atrial in silico models appears essential and requires confirmation in larger cohorts., (© The Author(s) 2024. Published by Oxford University Press on behalf of the European Society of Cardiology.)