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IEEE Trans Biomed Eng. 2016 Oct 19. doi: 10.1109/TBME.2016.2619489. [Epub ahead of print]

Prediction of Atherosclerotic Plaque Development in an in Vivo Coronary Arterial Segment Based on a Multi-level Modeling Approach.

Abstract

OBJECTIVE:

The aim of this study is to explore major mechanisms of atherosclerotic plaque growth, presenting a proof-of-concept numerical model.

METHODS:

To this aim, a human reconstructed left circumflex coronary artery is utilized for a multi-level modeling approach. More specifically, the first level consists of the modeling of blood flow and endothelial shear stress (ESS) computation. The second level includes the modeling of low and high density lipoprotein (LDL, HDL) and monocytes transport through the endothelial membrane to vessel wall. The third level comprises of the modeling of LDL oxidation, macrophages differentiation and foam cells formation. All modeling levels integrate experimental findings to describe the major mechanisms that occur in the arterial physiology. In order to validate the proposed approach, we utilize a patient specific scenario by comparing the baseline computational results with the changes in arterial wall thickness, lumen diameter and plaque components using follow-up data.

RESULTS:

The results of this model show that ESS and LDL concentration have a good correlation with the changes in plaque area [R2=0.365 (P=0.029, adjusted R2=0.307) and R2=0.368 (P=0.015, adjusted R2=0.342), respectively] whereas the introduction of the variables of oxidized LDL, macrophages and foam cells as independent predictors improves the accuracy in predicting regions potential for atherosclerotic plaque development [R2=0.847 (P=0.009, adjusted R2=0.738)].

CONCLUSION:

Advanced computational models can be used to increase the accuracy to predict regions which are prone to plaque development.

SIGNIFICANCE:

Atherosclerosis is one of leading causes of death worldwide. For this purpose computational models have to be implemented to predict disease progression.

PMID:
28113248
DOI:
10.1109/TBME.2016.2619489
[PubMed - as supplied by publisher]
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