Improvement of Aortic Valve Stenosis Classification in Patients through Computational Fluid Dynamics Model

Aortic valve stenosis (AS) is a common and severe valvular disease where accurate assessment is essential for determining prognosis and treatment. Current echocardiographic methods mostly rely on the simplified Bernoulli equation (SB), which approximate the peak pressure drop (ΔP), risking to poor stratification, especially for low-flow low gradient patients. This study examines the capabilities of combining computational fluid dynamics (CFD) and imaging techniques for better stratification of AS patients. Patient-specific geometries of the aortic valve, ascending aorta, and left ventricular outflow tract were reconstructed from CT and echocardiography data during peak systole. Inlet velocity boundary conditions based on ultrasound data enabled transient flow simulations. Blood flow was modelled as laminar and Newtonian, with the geometry discretized for computational efficiency without loss of accuracy. Validation against echocardiography data showed a 4% deviation in velocity predictions. Results indicated that ΔP and maximum velocity (Vmax) are strongly influenced by AVA size, while leaflet geometry affects flow jet location. The CFD model revealed that SB overestimates ΔP in non-severe AS, potentially leading to misclassification. By combining CFD with precise imaging, detailed hemodynamic insights can be achieved, addressing the limitations of conventional methods and improving patient stratification for treatment.