Impact of Aortic Stenosis on Myofiber Stress: Translational Application of Left Ventricle-Aortic Coupling Simulation

Abstract

The severity of aortic stenosis (AS) has traditionally been graded by measuring hemodynamic parameters of transvalvular pressure gradient, ejection jet velocity, or estimating valve orifice area. Recent research has highlighted limitations of these criteria at effectively grading AS in presence of left ventricle (LV) dysfunction. We hypothesized that simulations coupling the aorta and LV could provide meaningful insight into myocardial biomechanical derangements that accompany AS. A realistic finite element model of the human heart with a coupled lumped-parameter circulatory system was used to simulate AS. Finite element analysis was performed with Abaqus FEA. An anisotropic hyperelastic model was assigned to LV passive properties, and a time-varying elastance function governed the LV active response. Global LV myofiber peak systolic stress (mean ± standard deviation) was 9.31 ± 10.33 kPa at baseline, 13.13 ± 10.29 kPa for moderate AS, and 16.18 ± 10.59 kPa for severe AS. Mean LV myofiber peak systolic strains were −22.40 ± 8.73%, −22.24 ± 8.91%, and −21.97 ± 9.18%, respectively. Stress was significantly elevated compared to baseline for moderate (p < 0.01) and severe AS (p < 0.001), and when compared to each other (p < 0.01). Ventricular regions that experienced the greatest systolic stress were (severe AS vs. baseline) basal inferior (39.87 vs. 30.02 kPa; p < 0.01), mid-anteroseptal (32.29 vs. 24.79 kPa; p < 0.001), and apex (27.99 vs. 23.52 kPa; p < 0.001). This data serves as a reference for future studies that will incorporate patient-specific ventricular geometries and material parameters, aiming to correlate LV biomechanics to AS severity.

A Multi-Domain Simulation Study of a Pulsatile-Flow Pump Device for Heart Failure With Preserved Ejection Fraction

“…End-systolic and end-diastolic wall stress are generally estimated by measuring the LV wall thickness from cardiac magnetic resonance images. These estimates are based on Laplace’s Law, which assumes uniform chamber geometry and neglects the biomechanical properties of the myocardium ( Wisneski et al, 2020 ). A population-based LV FEA study by Wang et al (2016) highlighted that the end-diastolic wall stress at the mid ventricular region is greater in HFpEF patients (1.3 ± 0.2 kPa, p < 0.05) compared to the healthy control group (1.0 ± 0.2 kPa, p < 0.001).…”

Patient-Specific Analysis of Ascending Thoracic Aortic Aneurysm with the Living Heart Human Model

“…They demonstrated the role of kinematic heart behavior on the leaflet coaptation and stress distribution using an electromechanical model with more realistic boundary conditions. In a previous study, we adapted the left ventricle of LHHM using patient-specific CT scans and clinical data to calibrate the material constitutive behavior [ 39 ] and then quantify the intraventricular pressure and volume response under different degrees of aortic stenosis. The methodology here proposed to modify the aortic root and calibrate the left heart mechanics can be here integrated to simulate the ATAA in the setting of patients with bicuspid aortic valve or the dilated aortic root phenotype.…”

“…The active stress in the cardiac fiber direction is generated by a time-varying elastance model, where the active force is a function of the current sarcomere length, peak intracellular calcium concentration, and fiber activation time [ 37 ]. In a different way, the passive behavior of heart chambers is modeled with the anisotropic hyperelastic constitutive model proposed by Ogden and Holzapfel [ 38 ], which has been widely used in many cardiac simulation studies [ 37 , 39 ]. The strain energy function is represented by the following invariant-based formulation: …”

Precision medicine in human heart modeling

“…This suggests that we can confidently and robustly model the physiology of the healthy beating human heart. In a next step, we can use this simulation tool to probe pathological conditions and guide device design and treatment planning in cardiac diseases such as stenosis ( Wisneski et al 2020 ), regurgitation, ischemia ( Sack et al 2020 ), or heart failure ( Peirlinck et al 2019 ). With a view towards precision medicine, it would be relatively straightforward, although probably time intensive, to personalize the heart geometry from individual magnetic resonance images.…”