SUMMARY

Aortic valve stenosis (AS) is a disease caused by valve degeneration, most commonly due to calcific aortic valve disease, that affects 3% of all adults over 65 years of age and aortic valve replacement (AVR) is the only treatment option for patients with severe AS. Currently, transcatheter based approaches to aortic valve replacement (TAVR) are being widely adopted, especially in patients who are at increased risk of mortality from conventional open-heart surgery. However, adverse procedural complications such as coronary obstruction and aortic root rupture can severely impact the success of TAVR. Despite low incidence of such events, they can present high mortality rates of up to 40% at 30-day follow-up. Pre-procedural cardiac computed tomography (CT) imaging is often insufficient in visualizing the complex interactions between the transcatheter heart valve (THV) stent and the diseased aortic valve. Therefore, reliable prediction of occurrence of these complications based on CT measurements remains a challenge. The goal of this research is to (1) characterize the effects of device type, positioning and procedural adaptations such as valve fracture and alterations to filling volume of balloon-expanded THVs on the biomechanics of THV deployment using a validated computational framework, (2) create a quantitative predictive model for different modes of coronary obstruction and understand the effects of valve type, deployment on the risk of coronary obstruction, and (3) investigate the hemodynamics of coronary ostial flow after TAVR to better understand mechanisms surrounding delayed coronary obstruction using computational fluid dynamics. This study will improve the understanding of biomechanics of TAVR and its adaptations, thus leading to better patient selection. The integration of computational modeling in the procedural planning for TAVR could be the next major step towards reducing the rates of complications and maximize the success rate of TAVR.