The Prognostic Value of Tricuspid Annular Dimensions in TAVI Patients: A CT-Based Retrospective Analysis of Risk Stratification and Long-Term Outcomes.

: Transcatheter aortic valve implantation (TAVI) has transformed the treatment of severe aortic stenosis (AS), particularly in high-risk patients. However, comorbidities such as pulmonary hypertension (PH) and secondary tricuspid regurgitation (TR) contribute to adverse outcomes. Tricuspid annulus (TA) dilatation (TAD), a key marker of right ventricular dysfunction, has been associated with PH and TR progression. While echocardiographic assessment of TA has limitations, cardiac computed tomography (CT), routinely performed before TAVI, enables precise TA measurement. This study aimed to determine clinically relevant TA and TA indexed to body surface area (TA/BSA) cut-offs and assess their prognostic significance for long-term mortality. : This retrospective, single-center study included 522 patients who underwent transfemoral TAVI between 2016 and 2022. Pre-procedural CT-derived TA measurements were analyzed to establish cut-off values predictive of right ventricular dysfunction in TAVI. Receiver operating characteristic (ROC) analysis was performed, and Kaplan-Meier survival curves, log-rank tests, and Cox regression were used to assess the impact of TA dimensions on long-term survival. : TAD correlated moderately with right ventricular dysfunction, with optimal cut-offs identified as TA ≥ 44.50 mm and TA/BSA ≥ 23.00 mm/m. However, Kaplan-Meier and Cox regression analyses demonstrated no significant association between TA or TA/BSA and long-term survival, with area under the curve (AUC) values close to 0.50, indicating poor prognostic value. : Despite its relevance regarding right ventricular dysfunction in TAVI patients, TAD does not independently predict long-term mortality following TAVI. These findings challenge prior assumptions and suggest that TA dimensions alone should not guide risk stratification in TAVI patients. Further research is needed to refine prognostic models integrating multiple clinical and imaging parameters.