Development and Validation of a Practical Model to Identify Patients at Risk of Bleeding After TAVR.

Abstract Objectives No standardized algorithm exists to identify patients at risk of bleeding after transcatheter aortic valve replacement (TAVR). The aim of this study was to generate and validate a useful predictive model. Background Bleeding events after TAVR influence prognosis and quality of life and may be preventable. Methods Using machine learning and multivariate regression, more than 100 clinical variables from 5,185 consecutive patients undergoing TAVR in the prospective multicenter RISPEVA (Registro Italiano GISE sull’Impianto di Valvola Aortica Percutanea; NCT02713932 ) registry were analyzed in relation to Valve Academic Research Consortium-2 bleeding episodes at 1 month. The model’s performance was externally validated in 5,043 TAVR patients from the prospective multicenter POL-TAVI (Polish Registry of Transcatheter Aortic Valve Implantation) database. Results Derivation analyses generated a 6-item score (PREDICT-TAVR) comprising blood hemoglobin and serum iron concentrations, oral anticoagulation and dual antiplatelet therapy, common femoral artery diameter, and creatinine clearance. The 30-day area under the receiver-operating characteristic curve (AUC) was 0.80 (95% confidence interval [CI]: 0.75–0.83). Internal validation by optimism bootstrap-corrected AUC was 0.79 (95% CI: 0.75–0.83). Score quartiles were in graded relation to 30-day events (0.8%, 1.1%, 2.5%, and 8.5%; overall p  Conclusions PREDICT-TAVR is a practical, validated, 6-item tool to identify patients at risk of bleeding post-TAVR that can assist in decision making and event prevention.