CT-derived computational modelling in the lifetime management of aortic stenosis

Lifetime management of aortic stenosis represents a growing procedural and clinical challenge. With recent clinical trials indicating that transcatheter aortic valve replacement (TAVR) is at least on par with surgical aortic valve replacement (SAVR) in treating lower risk patients, there has been a rise in TAVR uptake in younger, lower risk patients, leading to an increased likelihood of bioprosthetic valve degradation within a patient's lifetime. This shift in treatment has changed the landscape of interventional cardiology, incentivising the Heart Team to now plan for the initial procedure with subsequent interventions in mind. While traditional multi-slice computed tomography image-based risk assessments are sufficient for initial valve placement, they fall short in their ability to accurately predict post-procedural outcomes and future interventions. Therefore, the need to balance competing risks to optimise patient outcomes over multiple interventions requires innovation. CT-derived computational techniques are being developed to incorporate biomechanics and fluid dynamics into the risk assessment process to allow more comprehensive analysis of the risks associated with different procedures. The goal of this review is to provide an overview of computational techniques that are being developed for the purposes of optimising outcomes in both the index and valve-in-valve interventions and to give cardiologists an understanding of how they may use computational modelling as an additional tool in the lifetime management of aortic stenosis.