Prediction Model of Post-TAVR Complication Using a Medical Twin Web Navigator

Abstract

Transcatheter aortic valve replacement (TAVR) has been introduced as an alternative to surgical aortic valve replacement for patients with severe aortic valve disease and is expanding into a universal treatment. However, complications after TAVR can have devastating consequences for patients and must be predicted. By designing a TAVR medical twin architecture based on real-world data (RWD), we can minimize complications and achieve optimal clinical outcomes through analysis and simulation results in a virtual environment that can predict complications. The simulation phase utilizes machine learning algorithms for complication prediction to predict patients with conduction abnormalities, a complication of TAVR, and provides the prediction results through a web-based monitoring system. We also conduct research to identify factors that influence complications, so that complication prediction in a virtualized environment on a medical twin architecture can serve as a guide for personalized care design for patients undergoing TAVR.