A Proof of Concept for Computational Fluid Dynamic Analysis of the Left Atrium in Atrial Fibrillation on a Patient-Specific Basis.

Atrial fibrillation (AF) is associated with a fivefold increase in the risk of cerebrovascular events, being responsible of 15-18% of all strokes. The morphological and functional remodeling of the left atrium (LA) caused by AF favors blood stasis and, consequently, stroke risk. In this context, several clinical studies suggest that the stroke risk stratification could be improved by using hemodynamic information on the LA and the left atrial appendage (LAA). The goal of this study was to develop a personalized computational fluid dynamics (CFD) model of the LA which could clarify the hemodynamic implications of AF on a patient-specific basis. In this paper, we present the developed model and its application to two AF patients as a preliminary advancement toward an optimized stroke risk stratification pipeline.

Computational fluid dynamics of blood flow in an idealized left human heart.

We construct an idealized computational model of the left human heart for the study of the blood flow dynamics in the left atrium and ventricle. We solve the Navier-Stokes equations in the ALE formulation and we prescribe the left heart wall displacement based on physiological data; moreover, we consider the presence of both the mitral and aortic valves through the resistive method. We simulate the left heart hemodynamics by means of the finite element method and we consider the variational multiscale large eddy simulation (LES) formulation to account for the transitional and nearly turbulent regimes of the blood flow in physiological conditions. The main contribution of this paper is the characterization of the blood flow in an idealized configuration of the left heart aiming at reproducing function in normal conditions. Our assessment is based on the analysis of instantaneous and phase averaged velocity fields, blood pressure, and other clinically meaningful fluid dynamics indicators. Finally, we show that our idealized computational model can be suitably used to study and critically discuss pathological scenarios like that of a regurgitant mitral valve.