Background: Predicting long-term survival after transcatheter edge-to-edge mitral valve repair (M-TEER) remains challenging. Existing risk models often fail to account for potential gender-specific differences that may influence outcomes.
Objective: The study aims to develop and validate a gender-specific multivariable risk model for predicting long-term survival after successful M-TEER in accordance with TRIPOD guidelines.
Methods: Consecutive patients with severe mitral regurgitation undergoing successful M-TEER between August 2010 and August 2025 were included. The primary endpoint was all-cause mortality. Missing data were handled by multiple imputation with 20 datasets. In each imputed dataset, Least Absolute Shrinkage and Selection Operator (LASSO)-penalized logistic regression with cross-validated tuning was applied separately for women and men to identify relevant predictors. Variables consistently selected across imputations were summarized to build sex-specific risk models. Model performance was assesst by receiver operating characteristic (ROC) analysis and validated internally and temporally.
Results: A total of 1,333 patients (mean age 78 ± 8 years; 49% female) were analyzed. Overall mortality during follow-up was 29% (female: 24%; male: 33%). Gender-specific LASSO regression identified distinct predictive profiles. In females, the most frequently selected predictors were tricuspid annular plane systolic excursion (TAPSE), chronic obstructive pulmonary disease (COPD), diabetes mellitus, N-terminal prohormone of brain natriuretic peptide (NT-proBNP), left ventricular stroke work index (LVSWI), and ejection fraction (EF). In males, NT-proBNP, TAPSE, peripheral artery disease, EF, COPD, LVSWI, and right ventricular stroke work index (RVSWI) were most predictive. During internal validation using bootstrap resampling, the final models showed good discrimination (area under the curve (AUC) = 0.731 for females; AUC = 0.69 for males). Temporal validation confirmed superior performance of the sex-specific models (AUC 0.66) compared with existing risk scores (AUC range 0.54–0.64).
Conclusions: Gender-specific risk models incorporating hemodynamic, demographic and echocardiographic parameters may provide improved predictive accuracy over traditional, non-sex-specific risk scores and may facilitate individualized risk assessment in M-TEER patients.
