Predicting Outcomes in Patients With Tricuspid Regurgitation Undergoing Transcatheter Edge-to-Edge Repair Using an Artificial Intelligence-Derived Risk Score: The EuroTR Risk Score - PubMed
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- #tricuspid regurgitation
- #risk stratification
- #artificial intelligence
- Development of the EuroTR score, an AI-driven risk score for predicting 1-year mortality in patients undergoing transcatheter edge-to-edge repair (T-TEER) for tricuspid regurgitation.
- The EuroTR score was developed using data from 1,225 patients in the derivation cohort and validated in 601 patients, showing significant stratification between low-risk and high-risk groups (HR: 4.26).
- Outperformed established risk scores like EuroScore and TRI-SCORE in predicting mortality (Harrell's C index = 0.741) and combined endpoints including heart failure hospitalization and persistent dyspnea.
- Risk stratification ranges from 30.6% poor outcomes in the lowest risk group to 85.5% in the highest risk group, aiding personalized treatment strategies and clinical trial design.
- Validated across subgroups including different types of tricuspid regurgitation and patients with or without cardiac implantable electronic devices, supporting its broad applicability.