ID | 69057 |
フルテキストURL |
suppl.docx
1.91 MB
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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
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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
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発行日 | 2025-02
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出版物タイトル |
JACC: Advances
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巻 | 4巻
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号 | 2号
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出版者 | Elsevier BV
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開始ページ | 101575
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ISSN | 2772-963X
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資料タイプ |
学術雑誌論文
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言語 |
英語
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OAI-PMH Set |
岡山大学
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著作権者 | © 2025 The Authors.
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論文のバージョン | publisher
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PubMed ID | |
DOI | |
Web of Science KeyUT | |
関連URL | isVersionOf https://doi.org/10.1016/j.jacadv.2024.101575
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ライセンス | http://creativecommons.org/licenses/by/4.0/
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助成情報 |
( Ruhr University Bochum )
( Technical University of Munich )
( German Center for Cardiovascular Research )
( German Heart Foundation )
( German Cardiac Society )
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