TY - JOUR
T1 - Predicting Outcomes in Patients With Tricuspid Regurgitation Undergoing Transcatheter Edge-to-Edge Repair Using an Artificial Intelligence–Derived Risk Score
AU - Hausleiter, Jörg
AU - Stolz, Lukas
AU - Kresoja, Karl Patrik
AU - von Stein, Jennifer
AU - Fortmeier, Vera
AU - Koell, Benedikt
AU - Rottbauer, Wolfgang
AU - Kassar, Mohammad
AU - Goebel, Bjoern
AU - Denti, Paolo
AU - Achouh, Paul
AU - Rassaf, Tienush
AU - Barreiro-Perez, Manuel
AU - Boekstegers, Peter
AU - Rück, Andreas
AU - Zdanyte, Monika
AU - Adamo, Marianna
AU - Vincent, Flavien
AU - Schlegel, Philipp
AU - Rosch, Sebastian
AU - Wild, Mirjam G.
AU - Besler, Christian
AU - Toggweiler, Stefan
AU - Brunner, Stephanie
AU - Grapsa, Julia
AU - Patterson, Tiffany
AU - Thiele, Holger
AU - Kister, Tobias
AU - Tarantini, Giuseppe
AU - Masiero, Giulia
AU - De Carlo, Marco
AU - Sticchi, Alessandro
AU - Voss, Fabian
AU - Polzin, Amin
AU - Rubbio, Antonio Popolo
AU - Bedogni, Francesco
AU - Laugwitz, Karl Ludwig
AU - Konstandin, Mathias H.
AU - Van Belle, Eric
AU - Metra, Marco
AU - Geisler, Tobias
AU - Estévez-Loureiro, Rodrigo
AU - Mahabadi, Amir Abbas
AU - Karam, Nicole
AU - Maisano, Francesco
AU - Lauten, Philipp
AU - Praz, Fabien
AU - Kessler, Mirjam
AU - Kalbacher, Daniel
AU - Rudolph, Volker
AU - Iliadis, Christos
AU - Lachmann, Mark
AU - Lurz, Philipp
N1 - Publisher Copyright:
© 2026 The Authors.
PY - 2026/3/9
Y1 - 2026/3/9
N2 - Background Risk stratification for tricuspid valve transcatheter edge-to-edge repair (T-TEER) is paramount in the decision-making process to appropriately select patients with severe tricuspid regurgitation. Objectives The aim of this study was to develop and validate an artificial intelligence–driven risk score, the EuroTR (European Registry of Transcatheter Repair for Tricuspid Regurgitation) score, to predict 1-year mortality in patients undergoing T-TEER. Methods The EuroTR score was developed using data from the EuroTR registry, comprising 1,225 patients in the derivation cohort and 601 patients in the validation cohort. On the basis of 18 clinical, laboratory, echocardiographic, and hemodynamic parameters, an extreme gradient boosting algorithm was trained and independently validated against established risk models. Results Among the entire study cohort (N = 1,826), the overall 1-year survival rate was 82.1% (95% CI: 80.1%-84.2%), with no significant differences between the derivation and validation cohorts. The EuroTR score successfully stratified patients into low-risk and high-risk groups for 1-year mortality after T-TEER (HR: 4.26; 95% CI: 2.71-6.67; P < 0.001), and it significantly outperformed established risk scores such as the EuroScore and the TRI-SCORE in the validation cohort. Beyond mortality prediction (Harrell’s C index [validation cohort] = 0.741; 95% CI: 0.699-0.783), increasing EuroTR score values were associated with a higher likelihood of a clinically relevant combined endpoint of 1-year mortality, need for heart failure hospitalization, or persistent dyspnea corresponding to NYHA functional class ≥III. The likelihood of poor outcomes increased from 30.6% in patients with the lowest EuroTR scores (EuroTR risk rank <5%) to 85.5% in the highest risk group (EuroTR risk rank ≥95%). The EuroTR score’s performance was confirmed in several subgroups (atrial vs nonatrial tricuspid regurgitation, TRILUMINATE-eligible vs TRILUMINATE-noneligible patients, and patients with vs without cardiac implantable electronic device leads). Conclusions The EuroTR score offers an easy-to-use, externally validated, accurate risk stratification tool for patients undergoing T-TEER. It supports personalized treatment strategies and the design of future clinical trials, helping optimize patient selection and enhance shared decision-making within multidisciplinary heart teams.
AB - Background Risk stratification for tricuspid valve transcatheter edge-to-edge repair (T-TEER) is paramount in the decision-making process to appropriately select patients with severe tricuspid regurgitation. Objectives The aim of this study was to develop and validate an artificial intelligence–driven risk score, the EuroTR (European Registry of Transcatheter Repair for Tricuspid Regurgitation) score, to predict 1-year mortality in patients undergoing T-TEER. Methods The EuroTR score was developed using data from the EuroTR registry, comprising 1,225 patients in the derivation cohort and 601 patients in the validation cohort. On the basis of 18 clinical, laboratory, echocardiographic, and hemodynamic parameters, an extreme gradient boosting algorithm was trained and independently validated against established risk models. Results Among the entire study cohort (N = 1,826), the overall 1-year survival rate was 82.1% (95% CI: 80.1%-84.2%), with no significant differences between the derivation and validation cohorts. The EuroTR score successfully stratified patients into low-risk and high-risk groups for 1-year mortality after T-TEER (HR: 4.26; 95% CI: 2.71-6.67; P < 0.001), and it significantly outperformed established risk scores such as the EuroScore and the TRI-SCORE in the validation cohort. Beyond mortality prediction (Harrell’s C index [validation cohort] = 0.741; 95% CI: 0.699-0.783), increasing EuroTR score values were associated with a higher likelihood of a clinically relevant combined endpoint of 1-year mortality, need for heart failure hospitalization, or persistent dyspnea corresponding to NYHA functional class ≥III. The likelihood of poor outcomes increased from 30.6% in patients with the lowest EuroTR scores (EuroTR risk rank <5%) to 85.5% in the highest risk group (EuroTR risk rank ≥95%). The EuroTR score’s performance was confirmed in several subgroups (atrial vs nonatrial tricuspid regurgitation, TRILUMINATE-eligible vs TRILUMINATE-noneligible patients, and patients with vs without cardiac implantable electronic device leads). Conclusions The EuroTR score offers an easy-to-use, externally validated, accurate risk stratification tool for patients undergoing T-TEER. It supports personalized treatment strategies and the design of future clinical trials, helping optimize patient selection and enhance shared decision-making within multidisciplinary heart teams.
KW - artificial intelligence
KW - edge-to-edge repair
KW - machine learning
KW - mortality prediction
UR - https://www.scopus.com/pages/publications/105031218604
U2 - 10.1016/j.jcin.2025.11.034
DO - 10.1016/j.jcin.2025.11.034
M3 - Article
AN - SCOPUS:105031218604
SN - 1936-8798
VL - 19
SP - 631
EP - 646
JO - JACC: Cardiovascular Interventions
JF - JACC: Cardiovascular Interventions
IS - 5
ER -