Critical role of arterial constitutive model in predicting blood pressure from pulse wave velocity.

A promising approach to cuff-less, continuous blood pressure monitoring is to estimate blood pressure (BP) from Pulse Wave Velocity (PWV). However, most existing PWV-based methods rely on empirical BP-PWV relations and have large prediction errors, which may be caused by the implicit assumption of thin-walled, linear elastic arteries undergoing small deformations. Our objective is to understand the BP-PWV relationship in the absence of such limiting assumptions. We performed Fluid-Structure Interaction (FSI) simulations of the radial artery and the common carotid artery under physiological flow conditions. In these dynamic simulations, we employed two constitutive models for the arterial wall: the linear elastic model, implying a thin-walled linear elastic artery undergoing small deformations, and the Holzapfel-Gasser-Ogden (HGO) model, accounting for the nonlinear effects of collagen fibers and their orientations on the large arterial deformation. Despite the changing BP, the linear elastic model predicts a constant PWV throughout a cardiac cycle, which is not physiological. The HGO model correctly predicts a positive BP-PWV correlation by capturing the nonlinear deformation of the artery, showing up to 50 % variations of PWV in a cardiac cycle. Dynamic FSI simulations reveal that the BP-PWV relationship strongly depends on the arterial constitutive model, especially in the radial artery. To infer BP from PWV, one must account for the varying PWV, a consequence of the nonlinear arterial response due to collagen fibers. Future efforts should be directed towards robust measurement of time-varying PWV if it is to be used to predict BP. Copyright © 2024 Elsevier Ltd. All rights reserved.

Citation

Aditya Satishkumar Bantwal, Amit Kumar Bhayadia, Hui Meng. Critical role of arterial constitutive model in predicting blood pressure from pulse wave velocity. Computers in biology and medicine. 2024 Aug;178:108730

Mesh Tags

PMID: 38917535

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