Dynamic simulation of aortic valve stenosis using a lumped parameter cardiovascular system model with flow regime dependent valve pressure loss characteristics

TL;DR

This study develops a detailed lumped parameter cardiovascular model with a novel valve pressure loss approach that accounts for flow regime and geometry, improving predictions of aortic stenosis effects compared to previous models.

Contribution

The paper introduces a new valve modeling method that incorporates flow regime and geometry effects, enhancing the accuracy of simulating aortic stenosis in cardiovascular models.

Findings

The new model predicts peak pressure drops within 20% of physiological data.

Previous models underpredict peak pressure drops by about 47%.

The new model overpredicts mean pressure drops by 7%.

Abstract

Valvular heart diseases are growing concern in impoverished parts of the world, such as Southern-Africa, claiming more than 31 % of total deaths related to cardiovascular diseases. The ability to model the effects of regurgitant and obstructive lesions on the valve body can assist clinicians in preparing personalised treatments. In the present work, a multi-compartment lumped parameter model of the human cardiovascular system is developed, with a newly proposed valve modelling approach which accounts for geometry and flow regime dependent pressure drops along with the valve cusp motion. The model is applied to study various degrees of aortic stenosis using typical human cardiovascular parameters. The results generated with the proposed model, are compared to predictions using previously published valve modelling approaches and both sets of results are compared to typical local and global physiological parameters found in literature such left-ventricular systolic pressures, peak and mean aortic valve pressure drops and vena contracta velocities. The results show that the previously published valve models under predicts expected severely stenosed peak and mean transvalvular pressure drops by approximately 47% and 30% respectively, whereas the newly proposed model under predicts the peak pressure drop by 20% and over predicts mean pressure drop by 7%.