Elevated atrial blood stasis in paroxysmal atrial fibrillation during sinus rhythm: a patient-specific computational fluid dynamics study

Front Cardiovasc Med. 2023 Aug 15:10:1219021. doi: 10.3389/fcvm.2023.1219021. eCollection 2023.

Abstract

Introduction: Atrial fibrillation (AF) is associated with an increased risk of stroke, often caused by thrombi that form in the left atrium (LA), and especially in the left atrial appendage (LAA). The underlying mechanism is not fully understood but is thought to be related to stagnant blood flow, which might be present despite sinus rhythm. However, measuring blood flow and stasis in the LAA is challenging due to its small size and low velocities. We aimed to compare the blood flow and stasis in the left atrium of paroxysmal AF patients with controls using computational fluid dynamics (CFD) simulations.

Methods: The CFD simulations were based on time-resolved computed tomography including the patient-specific cardiac motion. The pipeline allowed for analysis of 21 patients with paroxysmal AF and 8 controls. Stasis was estimated by computing the blood residence time.

Results and discussion: Residence time was elevated in the AF group (p < 0.001). Linear regression analysis revealed that stasis was strongest associated with LA ejection ratio (p < 0.001, R2 = 0.68) and the ratio of LA volume and left ventricular stroke volume (p < 0.001, R2 = 0.81). Stroke risk due to LA thrombi could already be elevated in AF patients during sinus rhythm. In the future, patient specific CFD simulations may add to the assessment of this risk and support diagnosis and treatment.

Keywords: atrial cardiomyopathy; atrial fibrillation; computational fluid dynamics; computed tomography; left atrial appendage; stroke.

Grants and funding

This project was funded by the Swedish Medical Research Council (2018-02779); the Swedish research council (2018-04454), ALF Grants, Region Östergötland (974839 and 969563); Swedish Heart and Lung Foundation (20200220 and 20210441), VINNOVA (2019-02261), and the Henry och Ella Ståhls foundation. The computations were enabled by resources provided by the Swedish National Infrastructure for Computing (SNIC), partially funded by the Swedish Research Council through grant agreement no. 2021/3-35.