A proof-of-concept study for predicting the region of atherosclerotic plaque development based on plaque growth modeling in carotid arteries

Prediction of Atherosclerotic Plaque Development in an In Vivo Coronary Arterial Segment Based on a Multilevel Modeling Approach

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 multilevel 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-density lipoprotein (LDL) and high-density lipoprotein 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.