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Predicting Outcomes in Patients With Tricuspid Regurgitation Undergoing Transcatheter Edge-to-Edge Repair Using an Artificial Intelligence–Derived Risk Score

  • Jörg Hausleiter
  • , Lukas Stolz
  • , Karl Patrik Kresoja
  • , Jennifer von Stein
  • , Vera Fortmeier
  • , Benedikt Koell
  • , Wolfgang Rottbauer
  • , Mohammad Kassar
  • , Bjoern Goebel
  • , Paolo Denti
  • , Paul Achouh
  • , Tienush Rassaf
  • , Manuel Barreiro-Perez
  • , Peter Boekstegers
  • , Andreas Rück
  • , Monika Zdanyte
  • , Marianna Adamo
  • , Flavien Vincent
  • , Philipp Schlegel
  • , Sebastian Rosch
  • Mirjam G. Wild, Christian Besler, Stefan Toggweiler, Stephanie Brunner, Julia Grapsa, Tiffany Patterson, Holger Thiele, Tobias Kister, Giuseppe Tarantini, Giulia Masiero, Marco De Carlo, Alessandro Sticchi, Fabian Voss, Amin Polzin, Antonio Popolo Rubbio, Francesco Bedogni, Karl Ludwig Laugwitz, Mathias H. Konstandin, Eric Van Belle, Marco Metra, Tobias Geisler, Rodrigo Estévez-Loureiro, Amir Abbas Mahabadi, Nicole Karam, Francesco Maisano, Philipp Lauten, Fabien Praz, Mirjam Kessler, Daniel Kalbacher, Volker Rudolph, Christos Iliadis, Mark Lachmann, Philipp Lurz
  • Ludwig-Maximilians-Universität München
  • Partner Site Munich Heart Alliance
  • University Medical Center
  • Uniklinikum Köln
  • University Hospital of the Ruhr-University Bochum
  • Universitätsklinikum Hamburg-Eppendorf
  • Partner Site Hamburg/Lübeck/Kiel
  • Heart Clinic Ulm
  • Inselspital Universitatsspital
  • University of Bern
  • Zentralklinik Bad Berka
  • Istituto Scientifico San Raffaele
  • AP-HP
  • University Hospital of Essen
  • Hospital Álvaro Cunqueiro
  • HELIOS Klinikum Siegburg
  • Karolinska Institutet
  • Universitätsklinikum Tübingen
  • University of Brescia
  • CHRU Roger Salengro
  • Universitätsklinikum Heidelberg
  • University Heart Center Freiburg
  • Cantonal Hospital of Lucerne
  • Guy's and St. Thomas' National Health Service Foundation Trust
  • Leipzig Heart Institute
  • University of Padova
  • University of Pisa
  • Heinrich-Heine-University
  • San Donato Hospital Arezzo
  • Technical University of Munich

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

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.

Original languageEnglish
Pages (from-to)631-646
Number of pages16
JournalJACC: Cardiovascular Interventions
Volume19
Issue number5
DOIs
StatePublished - 9 Mar 2026

Keywords

  • artificial intelligence
  • edge-to-edge repair
  • machine learning
  • mortality prediction

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