Abstract
Transcatheter aortic valve implantation (TAVI) has emerged as the standard treatment option for patients with symptomatic aortic stenosis who are considered intermediate to high surgical risk. Nonetheless, optimal clinical outcomes following the procedure require careful consideration of procedural risk by the Heart Team. While this decision-making could be supported through the development of TAVI-specific clinical prediction models (CPMs), current models remain suboptimal. In this review paper, we aimed to outline the performance of several recently derived TAVI CPMs that predict mortality and present some future research directions. We discuss how the existing risk models have achieved only moderate discrimination but highlight that some of the models are well calibrated across multiple populations, indicating the feasibility of using them to aid benchmarking analyses. Moreover, we suggest that future work should focus on the development of CPMs in cohorts of patients with aortic stenosis that include multiple treatment modalities. Supported by appropriate modelling of 'what if' scenarios, this would allow the Heart Teams to predict and compare outcomes across surgical aortic valve replacement, medical management and TAVI, thereby allowing one to personalise treatment decisions to the individual patient. Such a goal could be facilitated by considering novel risk factors, shifting the focus to endpoints other than mortality, and through collaborative efforts to combine the evidence base and existing models across wider populations.