Left main coronary artery morphological phenotypes and its hemodynamic properties

Area of Science:

• Cardiovascular Imaging and Hemodynamics
• Biomedical Engineering
• Medical Informatics

Background:

• Atherosclerosis pathogenesis may involve coronary artery morphological defects influencing hemodynamics.
• Objective methods for classifying coronary artery morphology based on hemodynamics are limited.
• Unsupervised clustering (UC) offers a novel approach to phenotype coronary artery morphology.

Purpose of the Study:

• To classify left main coronary artery (LM) morphology using unsupervised clustering.
• To investigate hemodynamic differences among distinct LM morphological phenotypes.
• To establish a link between LM structure and coronary artery disease risk.

Main Methods:

• Reconstruction of 76 left main coronary arteries (LMs) using coronary computed tomography angiography.
• Extraction of geometric characteristics and application of unsupervised clustering for phenotype identification.
• Computational fluid dynamics (CFD) analysis to assess time-averaged wall shear stress (TAWSS) for each phenotype.

Main Results:

• Four distinct LM phenotypes were identified based on morphology (stem length, branch thickness, bifurcation angle, ostium angulation).
• Significant variations in TAWSS distribution were observed across the identified phenotypes.
• Low TAWSS regions were predominantly located near the left anterior descending artery branching points, especially in Cluster 2.

Conclusions:

• Unsupervised clustering is effective for classifying LM morphology.
• Distinct LM phenotypes exhibit unique hemodynamic profiles.
• This classification approach may facilitate early identification of individuals at high risk for coronary atherosclerosis.