Development of a Patient-Specific Streamlined Workflow for a Predictive Tool for Coronary Artery Bypass Graft Outcomes

Abstract

Coronary artery disease is one of the leading causes of mortality worldwide and is treatable by only a few procedures, such as coronary artery bypass grafting (CABG). However, predicting the long-term success of CABG, particularly in terms of graft patency and disease progression, remains a challenge. This thesis aims to develop a patient-specific streamlined workflow for a predictive tool that integrates peri-operative Coronary Computed Tomography Angiography (CCTA) with Computational Fluid Dynamics (CFD) simulations to predict post-CABG outcomes. The patient-specific streamlined workflow for a predictive tool for CABG outcomes is designed to simulate hemodynamic conditions within grafts, providing personalized predictions to guide surgical decisions and improve patient outcomes. The methodology includes creating anatomically accurate 3D models, simulating hemodynamic conditions, and validating the results against clinical data. While the patient-specific streamlined workflow for the predictive tool shows promise, significant challenges remain in terms of boundary condition setup, data integration, and discrepancies between simulation results and hospital data. The overestimation of flow rates and high wall shear stress observed in the simulations indicate the need for refinements in model assumptions, including incorporating non-Newtonian blood properties, vessel wall compliance, and pulsatile blood flow. Despite these limitations, this study demonstrates the potential of a patient-specific streamlined workflow for a predictive tool for CABG outcomes and offers a path forward for personalized cardiovascular treatment. Future work will focus on refining the model to enhance its accuracy, and clinical applicability, and commercializing the method.