Effects of age-associated regional changes in aortic stiffness on human hemodynamics revealed by computational modeling

Abstract

Although considered by many as the gold standard clinical measure of arterial stiffness, carotid-to-femoral pulse wave velocity (cf-PWV) averages material and geometric properties over a large portion of the central arterial tree. Given that such properties may evolve differentially as a function of region in cases of hypertension and aging, among other conditions, there is a need to evaluate the potential utility of cf-PWV as an early diagnostic of progressive vascular stiffening. In this paper, we introduce a data-driven fluid-solid-interaction computational model of the human aorta to simulate effects of aging-related changes in regional wall properties (e.g., biaxial material stiffness and wall thickness) and conduit geometry (e.g., vessel caliber, length, and tortuosity) on several metrics of arterial stiffness, including distensibility, augmented pulse pressure, and cyclic changes in stored elastic energy. Using the best available biomechanical data, our results for PWV compare well to findings reported for large population studies while rendering a higher resolution description of evolving local and global metrics of aortic stiffening. Our results reveal similar spatio-temporal trends between stiffness and its surrogate metrics, except PWV, thus indicating a complex dependency of the latter on geometry. Lastly, our analysis highlights the importance of the tethering exerted by external tissues, which was iteratively estimated until hemodynamic simulations recovered typical values of tissue properties, pulse pressure, and PWV for each age group.

A nonlinear rotation-free shell formulation with prestressing for vascular biomechanics

“…The lack of rotational degrees of freedom in the presented nonlinear shell formulation makes it well-suited to serve as the structural model for a strongly coupled, monolithic, computationally efficient, nonlinear fluid-structure interaction framework via an adaption of the CMM to large displacements. Although prior simulations, including our own 12 , 13 , have proven useful in studying fluid–solid interactions in many regions of the vasculature, the present formulation promises to be better suited to study ventricular-vascular interactions, particularly since the ascending aorta experiences dramatic finite distension, extension, and bending during the cardiac cycle. The presented shell formulation is also suitable for including growth and remodeling by using a constrained mixture theory (CMT) 57 .…”

“…Specifically, the CMM employs a linear membrane formulation, resulting in minimal additional computational costs compared to methods with rigid wall approximation. The CMM has been implemented within open-source cardiovascular modeling environments, SimVascular 10 and CRIMSON 11 , and has been utilized in numerous cardiovascular studies [12][13][14][15] . Nevertheless, the CMM is limited due to the use of fixed computational grids and the assumption of geometric and material linearity for the vessel wall behavior.…”

Association of arterial stiffness with aortic calcification and tortuosity

“…Aging was the most important contributor to increasing aortic tortuosity in this study. Cuomo et al [29] reported the association of age-related changes in regional wall properties and geometry with computational modeling of the human aorta. They showed that tortuosity was increased with age by comparing the 30-year-old baseline model with the 40-, 60-, 75-year-old models.…”

A Two-Stage Purkinje Network for More Accurate ECG Representations

“…Monitoring changes in the smaller levels could reveal how they affect the heart's structure and function and give us the ability to better quantify how heart disease impacts its functionality (8). Thus, with the aim to improve cardiac healthcare, multiscale modeling could be pursued to gain insight into the causes and consequences of CVDs (7,9,10), monitor drug-induced effects on cardiac electromechanics (3,11), optimize treatment options and simulate surgeries to nd the best course of operation (12)(13)(14).…”