Fortmeier Vera, Lachmann Mark, Stolz Lukas, von Stein Jennifer, Rommel Karl-Philipp, Kassar Mohammad, 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, Praz Fabien, Lurz Philipp, Hausleiter Jörg, Rudolph Volker
Department of General and Interventional Cardiology, Heart and Diabetes Center North Rhine-Westphalia, Ruhr University Bochum, Bad Oeynhausen, Germany.
First Department of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany; DZHK (German Center for Cardiovascular Research), Partner Site Munich Heart Alliance, Munich, Germany.
JACC Adv. 2025 Feb;4(2):101575. doi: 10.1016/j.jacadv.2024.101575. Epub 2025 Jan 22.
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).
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.
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.
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 cm, 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.
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.
重度三尖瓣反流（TR）患者的心脏功能不全程度和心外合并症存在异质性，这些因素对经导管三尖瓣介入治疗（TTVI）后的生存起着决定性作用。
本研究旨在创建一种基于生存树的模型，以确定与TTVI后2年生存率相关的心脏和心外特征。
该研究纳入了918例因重度TR接受TTVI的患者（推导集，n = 631；验证集，n = 287）。将基于监督式机器学习的生存树建模应用于术前临床、实验室、超声心动图和血流动力学数据。
在对2年死亡率预测的候选变量进行单变量回归分析预筛选后，使用4个关键参数构建了基于生存树的模型。确定了3个不同的与聚类相关的风险类别，它们在TTVI后的生存率上有显著差异。低风险类别（n = 261）的患者定义为平均肺动脉压≤28 mmHg且N末端B型利钠肽原≤2,728 pg/mL，其2年生存率为85.5%。高风险类别（n = 190）的患者定义为平均肺动脉压>28 mmHg、右心房面积>32.5 cm且估计肾小球滤过率≤51 mL/min，其2年生存率明显更差，仅为52.6%（2年死亡率的HR：4.3，P < 0.001）。净重新分类改善分析表明，该模型在识别高风险患者方面与TRI评分相当，且优于欧洲心脏手术风险评估系统II（EuroScore II）。风险表型的预后价值通过外部验证得到证实。
这种简单的基于生存树的模型有效地将重度TR患者分层为不同的风险类别，显示出TTVI后2年生存率的显著差异。
