Multiscale modeling for vein graft failure risk stratification in CABG patients

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Coronary artery bypass graft (CABG) surgery is a gold standard treatment for patients with advanced coronary artery disease, with over 400,000 cases performed each year in the US. While arterial grafts have greater long- term patency compared to vein grafts, their use is limited by availability, and saphenous vein grafts (SVGs) are used in the majority of patients. Following CABG surgery, SVG failure occurs at alarmingly high rates, with 5- 10% of SVGs occluding within the first month after surgery, and 40-50% of SVGs failing within 10 years. The risk of SVG disease and the complex mechanobiology of graft failure are known to be associated with mechanical stimuli, including hemodynamics and vessel wall mechanics. However, standard computed tomography (CT) imaging provides no direct means to characterize these stimuli. Recent advances in multiscale modeling now permit physiologic closed-loop simulations with realistic material properties, avoiding prior limitations of idealized anatomy, rigid walls, and incomplete coronary models. We propose a novel coronary simulation framework that can comprehensively characterize bypass graft hemodynamics and wall mechanics using only non-invasive clinical data. We propose that validated simulations with realistic hemodynamics and wall motion, in concert with modern imaging techniques will enable post-CABG risk stratification and early identification of patients at high risk for saphenous graft failure. To accomplish these goals, we propose three specific aims: 1) design and validate a novel closed-loop multiscale CABG simulation framework that can predict local hemodynamics and wall mechanics using only non-invasive clinical data, 2) quantify and compare the mechanical stimuli acting on arterial and vein grafts in patient- specific models, and 3) develop a pilot risk stratification scoring system for post-CABG patients by correlating mechanical stimuli with clinical outcomes in vessels with and without SVG disease. The proposed work is significant and innovative because it will (1) use patient-specific simulations to virtually reverse SVG disease thus using patients as their own control (2) enable early identification of patients at increased risk of SVG obstruction whose outcomes may be improved by more intensive treatment and monitoring, (3) enable future vessel wall growth and remodeling simulations which rely on mechanical stimuli data, (4) combine high resolution imaging with sophisticated multiscale modeling of the complete coronary circulation, and (5) directly validate model predictions against clinical data and report confidence intervals on simulation results. This project assembles a unique team including an adult cardiologist and imaging specialist with a background in physics, and an engineering team with established expertise in cardiovascular biomechanics. We will build upon our extensive experience with patient-specific blood flow simulations, and a successful track record of clinical translation and multi-disciplinary collaboration. Our translational goal is to provide clinicians with new tools to improve management decisions for CABG patients at risk for graft failure and improve outcomes.