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ID 69057
フルテキストURL
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suppl.docx 1.91 MB
著者
Fortmeier, Vera Department of General and Interventional Cardiology, Heart and Diabetes Center North Rhine-Westphalia, Ruhr University Bochum
Lachmann, Mark First Department of Medicine, Klinikum rechts der Isar, Technical University of Munich
Stolz, Lukas DZHK (German Center for Cardiovascular Research), Partner Site Munich Heart Alliance
von Stein, Jennifer Department of Cardiology, Heart Center, University of Cologne
Rommel, Karl-Philipp Department of Cardiology, Heart Center Leipzig, University of Leipzig
Kassar, Mohammad Department of Cardiology, Inselspital Bern, Bern University Hospital
Gerçek, Muhammed Department of General and Interventional Cardiology, Heart and Diabetes Center North Rhine-Westphalia, Ruhr University Bochum
Schöber, Anne R. Department of Cardiology, Heart Center Leipzig, University of Leipzig
Stocker, Thomas J. DZHK (German Center for Cardiovascular Research), Partner Site Munich Heart Alliance
Omran, Hazem Department of General and Interventional Cardiology, Heart and Diabetes Center North Rhine-Westphalia, Ruhr University Bochum
Fett, Michelle Department of General and Interventional Cardiology, Heart and Diabetes Center North Rhine-Westphalia, Ruhr University Bochum
Tervooren, Jule Department of General and Interventional Cardiology, Heart and Diabetes Center North Rhine-Westphalia, Ruhr University Bochum
Körber, Maria I. Department of Cardiology, Heart Center, University of Cologne
Hesse, Amelie First Department of Medicine, Klinikum rechts der Isar, Technical University of Munich
Harmsen, Gerhard Department of Physics, University of Johannesburg
Friedrichs, Kai Peter Department of General and Interventional Cardiology, Heart and Diabetes Center North Rhine-Westphalia, Ruhr University Bochum
Yuasa, Shinsuke Department of Cardiovascular Medicine, Okayama University
Rudolph, Tanja K. Department of General and Interventional Cardiology, Heart and Diabetes Center North Rhine-Westphalia, Ruhr University Bochum
Joner, Michael DZHK (German Center for Cardiovascular Research), Partner Site Munich Heart Alliance
Pfister, Roman Department of Cardiology, Heart Center, University of Cologne
Baldus, Stephan Department of Cardiology, Heart Center, University of Cologne
Laugwitz, Karl-Ludwig First Department of Medicine, Klinikum rechts der Isar, Technical University of Munich
Windecker, Stephan Department of Cardiology, Inselspital Bern, Bern University Hospital
Praz, Fabien Department of Cardiology, Inselspital Bern, Bern University Hospital
Lurz, Philipp Department of Cardiology, Heart Center Leipzig, University of Leipzig
Hausleiter, Jörg DZHK (German Center for Cardiovascular Research), Partner Site Munich Heart Alliance
Rudolph, Volker Department of General and Interventional Cardiology, Heart and Diabetes Center North Rhine-Westphalia, Ruhr University Bochum
抄録
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.
キーワード
machine learning
transcatheter tricuspid valve intervention
tricuspid regurgitation
発行日
2025-02
出版物タイトル
JACC: Advances
4巻
2号
出版者
Elsevier BV
開始ページ
101575
ISSN
2772-963X
資料タイプ
学術雑誌論文
言語
英語
OAI-PMH Set
岡山大学
著作権者
© 2025 The Authors.
論文のバージョン
publisher
PubMed ID
DOI
Web of Science KeyUT
関連URL
isVersionOf https://doi.org/10.1016/j.jacadv.2024.101575
ライセンス
http://creativecommons.org/licenses/by/4.0/
助成情報
( Ruhr University Bochum )
( Technical University of Munich )
( German Center for Cardiovascular Research )
( German Heart Foundation )
( German Cardiac Society )