A Clinical Risk Prediction Model for Identifying Patient Candidates for Same-day Discharge After Transcatheter Aortic Valve Replacement

Background: Same-day discharge after transcatheter aortic valve replacement (TAVR) may be feasible for selected patients if a low risk for adverse clinical events can be defined. We aimed to develop a clinical risk prediction model to facilitate same-day discharge planning. Methods: A random forest machine learning algorithm was used to build a prediction model of adverse events occurring in-hospital after TAVR. Patients were categorized into low, moderate, or high-risk groups based on their estimated scores. Results: Overall, 730 patients (median age, 81 years; 58.9% men) who had transfemoral TAVR performed with conscious sedation were examined. The risk score was built utilizing 9 clinical parameters. The prediction model had a median area under the receiver operating characteristic curve of 0.76. For determining the probability of events that would disallow same-day discharge, the model successfully identified 172 patients (23.6% of the population) as low-risk for same-day discharge, or for having an event rate of