Assessment with clinical data of a coupled bio-hemodynamics numerical model to predict leukocyte adhesion in coronary...

Area of Science:

• Cardiovascular Science
• Biomedical Engineering
• Computational Fluid Dynamics

Background:

• Atherosclerosis involves complex interactions between blood flow, white blood cells, and artery walls.
• Understanding these interactions is crucial for predicting disease progression and developing targeted therapies.

Purpose of the Study:

• To develop and validate a computational model simulating hemodynamics and leukocyte adhesion in coronary arteries.
• To investigate the influence of patient-specific artery geometry on leukocyte adhesion patterns.

Main Methods:

• Utilized realistic coronary artery geometries derived from intravascular ultrasound and angiography.
• Developed a coupled numerical model for unsteady 3D blood hemodynamics, leukocyte transport, and wall-shear stress-dependent adhesion using agent-based modeling.
• Validated simulation results against clinical data on plaque progression.

Main Results:

• Simulated leukocyte adhesion patterns showed good correlation with clinical observations of plaque increase in specific artery segments.
• Artery geometry, particularly centerline tortuosity, was identified as a key factor influencing wall shear stress distribution and leukocyte adhesion.
• Model predictions align with documented artery segments exhibiting plaque growth over time.

Conclusions:

• The developed computational model shows promise for predicting atherosclerosis progression by integrating hemodynamics and leukocyte transport.
• Artery geometry plays a critical role in the spatial distribution of wall shear stress and subsequent leukocyte adhesion.
• Further refinement of the model could lead to a predictive methodology for quantifying plaque growth.