BACKGROUND: Patients with severe tricuspid regurgitation (TR) typically present with heterogeneity in the extent of cardiac dysfunction and extra-cardiac comorbidities, which play a decisive role for survival after transcatheter tricuspid valve intervention (TTVI). OBJECTIVES: This aim of this study was to create a survival tree-based model to determine the cardiac and extra-cardiac features associated with 2-year survival after TTVI. METHODS: The study included 918 patients (derivation set, n = 631
validation set, n = 287) undergoing TTVI for severe TR. Supervised machine learning-derived survival tree-based modelling was applied to preprocedural clinical, laboratory, echocardiographic, and hemodynamic data. RESULTS: Following univariate regression analysis to pre-select candidate variables for 2-year mortality prediction, a survival tree-based model was constructed using 4 key parameters. Three distinct cluster-related risk categories were identified, which differed significantly in survival after TTVI. Patients from the low-risk category (n = 261) were defined by mean pulmonary artery pressure ≤28 mm Hg and N-terminal pro-B-type natriuretic peptide ≤2,728 pg/mL, and they exhibited a 2-year survival rate of 85.5%. Patients from the high-risk category (n = 190) were defined by mean pulmonary artery pressure >
28 mm Hg, right atrial area >
32.5 cm CONCLUSIONS: This simple survival tree-based model effectively stratifies patients with severe TR into distinct risk categories, demonstrating significant differences in 2-year survival after TTVI.