非均一石灰化大動脈弁の血行動態解析：区分化流体構造連成解析フレームワークを用いて

Calcific Aortic Valve Disease (CAVD) is a common cause for aortic stenosis characterized by the progressive calcification and stiffening of the aortic valve, often leading to significant hemodynamic changes and impaired cardiac function. Several Computational Fluid Dynamics (CFD) simulations have been conducted in the literature to provide more detailed analysis of CAVD, but are mainly reliant on uniform calcification. Also, outcomes from current clinical diagnostic techniques do not account for the effect of non-uniform calcification. The purpose of this study is to extend previous CFD simulations to non-uniformly calcified aortic valves and to evaluate the accuracy of clinical diagnostic methods under these conditions.

High-fidelity simulations of non-uniformly calcified aortic valves are performed by coupling fluid and solid solvers using a partitioned fluid-structure interaction (FSI) method for a patient-specific valve model extracted from computed tomography (CT) images. Non-uniform calcification is modelled by varying the elasticity along the leaflet, with several levels of calcification ranging from mild to severe.

Non-uniform calcification alters flow physics, leading in up to 35-50% increase in maximum jet velocity and up to 150-170% rise in TPG compared to the normal valve, significant vortex shedding, extended flow separation regions, and intensified wall shear stress (WSS) fluctuations, especially on the ventricular side of the leaflet. The results indicate that the severity of calcification cannot be accurately predicted by several clinical diagnostic methods, with only effective orifice area (EOA) and maximum opening ratio (MOR) emerging as the reliable predictors.

The progression of non-uniform calcification on the aortic valve leaflets significantly impacts both hemodynamics and leaflet mechanics, with clear alterations in flow patterns and biomechanical responses as calcification severity increases. These findings highlight the need for more accurate diagnostic techniques and may drive the development of improved clinical strategies for the management and treatment of CAVD.