Why the study?
Can a multi-level computational model accurately predict regions of atherosclerotic plaque growth in carotid arteries compared to 12-month follow-up MRI?
Can a multi-level computational model accurately predict regions of atherosclerotic plaque growth in carotid arteries compared to 12-month follow-up MRI?
A multi-level computational model incorporating biological cellular processes significantly improves the prediction of atherosclerotic plaque growth regions in carotid arteries.
Multi-level cellular models may improve carotid plaque prediction; hypothesis-generating and should not yet change practice.
In this work, we present a computational model for plaque growth utilizing magnetic resonance data of a patient's carotid artery. More specifically, we model blood flow utilizing the Navier-Stokes equations, as well as LDL and HDL transport using the convection-diffusion equation in the arterial lumen. The accumulated LDL in the arterial wall is oxidized considering the protective effect of HDL. Macrophages recruitment and foam cells formation are the final step of the proposed multi-level modeling approach of the plaque growth. The simulated results of our model are compared with the follow-up MRI findings in 12 months regarding the change to the arterial wall thickness. WSS and LDL may indicate potential regions of plaque growth (R(2)=0.35), but the contribution of foam cells formation, macrophages and oxidized LDL increased the prediction significantly (R(2)=0.75).
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Sakellarios et al. (2015) studied this question.
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