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

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%.

Non-invasive estimation of left ventricle elastance using a multi-compartment lumped parameter model and gradient-based optimization with forward-mode automatic differentiation

“…These 14 dependent variables are defined by the model equations for the four heart chambers, four heart valves and systemic and pulmonary loops as discussed in section 2.2.1. For a more detailed discussion of the model equations the interested reader is referred to [29]. Once the model is solved, the corresponding output has the size M ( θ, Xi nit) = X ∈ R 14×Nt where θ is a vector containing all the parameters mentioned above, X is the model solution matrix and N t is the total number of time steps taken by the ODE integrator over the simulation time T = 1 [s].…”