Predicting Bypass Graft Failure
Collaborators: Marsden Lab
Coronary artery bypass grafting (CABG) is widely used to treat advanced coronary artery disease, but long-term success depends on the durability of the grafts. Saphenous vein grafts are commonly used, yet many develop narrowing or occlusion over time. These changes are often silent and difficult to predict using standard clinical tools.
In this work, patient-specific models derived from CT angiography were used to investigate how blood flow contributes to graft failure. Computational fluid dynamics (CFD) simulations were performed to evaluate hemodynamic forces within each graft, with a focus on wall shear stress, a measure of how blood flow interacts with the vessel wall. By comparing diseased and non-diseased segments within the same patients, the study identified localized regions of low wall shear stress that were more likely to develop stenosis.
Publication Link:PubMed Central
Figure A: Workflow showing CTA-based model reconstruction, stenosis removal, and extraction of anatomical and hemodynamic parameters for computational analysis.
The 3DQ Lab contributed by generating the anatomical models required for this analysis. Using post-surgical CTA data, vascular structures were segmented and reconstructed into 3D models using SimVascular. Approximately 75 patient-specific models were created, providing the foundation for CFD simulations and enabling detailed evaluation of graft hemodynamics across the study cohort.
These findings support the role of blood flow patterns in graft disease and highlight the potential of computational modeling to identify high-risk regions before clinical failure occurs. With further validation, this approach may help guide closer monitoring and more targeted treatment strategies for patients following bypass surgery.
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