Relationships between local geometrical features and hemodynamic flow properties.

Area of Science:

• Biomedical Engineering
• Cardiovascular Research
• Medical Imaging

Background:

• Stroke is a leading cause of death and disability globally, with ischemic strokes often caused by carotid artery atherosclerosis.
• Vascular wall shear stress (WSS) is implicated in carotid plaque development, but in vivo measurement and computational fluid dynamics (CFD) analysis are challenging.
• Identifying reliable predictors of WSS is crucial for understanding stroke risk.

Purpose of the Study:

• To identify local geometric parameters correlated with WSS in carotid arteries.
• To develop a predictive regression model for WSS based on these geometric parameters.
• To establish a cost-effective method for identifying individuals at elevated stroke risk.

Main Methods:

• Analysis of six carotid arteries, separating the internal (ICA) and external (ECA) carotid arteries.
• Identification of local geometric parameters relevant to WSS.
• Development and validation of a regression model using metrics like RMSE, adjusted R(2), and AIC.

Main Results:

• The regression model demonstrated strong correlations between geometric parameters and WSS.
• Adjusted R(2) values exceeded 0.8 for 9 out of 12 analyzed arterial branches.
• The proposed geometric parameters are efficiently obtainable.

Conclusions:

• Local geometric parameters can effectively predict WSS in carotid arteries.
• These parameters offer a cost-effective approach to identifying stroke risk phenotypes.
• This method has the potential to significantly improve early detection of individuals susceptible to stroke.