Simplified Outcome Prediction in Patients Undergoing Transcatheter Tricuspid Valve Intervention by Survival Tree-Based Modelling
Vera Fortmeier, Mark Lachmann, Lukas Stolz, Jennifer von Stein, Karl‐Philipp Rommel, Mohammad Kassar, Muhammed Gerçek, Anne Rebecca Schöber, Thomas J. Stocker, Hazem Omran, Michelle Fett, Jule Tervooren, Maria Isabel Körber, Amelie Hesse, Gerhard Harmsen, Kai Friedrichs, Shinsuke Yuasa, Tanja K. Rudolph, Michael Joner, Roman Pfister, Stephan Baldus, Karl‐Ludwig Laugwitz, Stephan Windecker, Fabien Praz, Philipp Lurz, Jörg Hausleiter, Volker Rudolph
Abstract
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: , and estimated glomerular filtration rate ≤51 mL/min, and they showed a significantly worse 2-year survival of only 52.6% (HR for 2-year mortality: 4.3, P < 0.001). Net re-classification improvement analysis demonstrated that this model was comparable to the TRI-Score and outperformed the EuroScore II in identifying high-risk patients. The prognostic value of risk phenotypes was confirmed by external validation. 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.