Computational fluid dynamics model utilizing proper orthogonal decomposition to assess coronary physiology and wall shear stress

Abstract

Background

Percutaneous coronary intervention (PCI) to alleviate symptoms and improve outcomes in patients with symptomatic coronary artery disease. However, conventional assessments like coronary angiography may not fully capture the hemodynamic significance of coronary lesions. This study explores the utility of Proper Orthogonal Decomposition (POD) in elucidating coronary flow dynamics pre- and post-stent placement.

Objectives

Through the utilization of POD modes, we aim to analyze the intricate geometries of individual patients, extracting dominant POD modes both pre- and post-PCI. By engaging these modes, our objective is to discern changes in velocity patterns and wall shear stress, offering insight into the physiological outcomes of stent interventions in coronary arteries.

Methods

The POD method with QR-decomposition was employed to generate POD modes, decomposing the vector field of interest into spatial functions modulated by time coefficients. Patients with prior coronary artery bypass surgery, myocardial bridging, collateral arteries, or recent myocardial infarction within 48 h were excluded from the study.

Results

Results demonstrated improved hemodynamic parameters post-PCI, with significant enhancements in coronary flow reserve and reduced wall shear stress. POD analysis revealed that the first five modes effectively characterized flow features, highlighting stenosis, stent deployment, and branch dynamics.

Conclusion

This exploratory study demonstrates POD's potential for real-time assessment of coronary lesion significance and post-intervention outcomes. Its efficiency in capturing key flow characteristics offers a promising tool for personalized decision-making in interventional cardiology, enhancing our understanding of coronary hemodynamics and optimizing treatment strategies.