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  3. Simplified Outcome Prediction in Patients Undergoing Transcatheter Tricuspid Valve Intervention by Survival Tree-Based Modelling.
 

Simplified Outcome Prediction in Patients Undergoing Transcatheter Tricuspid Valve Intervention by Survival Tree-Based Modelling.

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BORIS DOI
10.48620/85228
Publisher DOI
10.1016/j.jacadv.2024.101575
PubMed ID
39848099
Description
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 cm2, 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.
Date of Publication
2025-02
Publication Type
Article
Subject(s)
600 Technology > 610 Medicine & health
Keyword(s)
machine learning
•
transcatheter tricuspid valve intervention
•
tricuspid regurgitation
Language(s)
en
Contributor(s)
Fortmeier, Vera
Lachmann, Mark
Stolz, Lukas
von Stein, Jennifer
Rommel, Karl-Philipp
Kassar, Mohammadorcid-logo
Clinic of Cardiology
Gerçek, Muhammed
Schöber, Anne R
Stocker, Thomas J
Omran, Hazem
Fett, Michelle
Tervooren, Jule
Körber, Maria I
Hesse, Amelie
Harmsen, Gerhard
Friedrichs, Kai Peter
Yuasa, Shinsuke
Rudolph, Tanja K
Joner, Michael
Pfister, Roman
Baldus, Stephan
Laugwitz, Karl-Ludwig
Windecker, Stephan
Clinic of Cardiology
Clinic of Cardiology
Praz, Fabien
Clinic of Cardiology
Lurz, Philipp
Hausleiter, Jörg
Rudolph, Volker
Additional Credits
Clinic of Cardiology
Series
JACC: Advances
Publisher
Elsevier
ISSN
2772-963X
Access(Rights)
open.access
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