Simplified Outcome Prediction in Patients Undergoing Transcatheter Tricuspid Valve Intervention by Survival Tree-Based Modelling
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...
Saved in:
| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-02-01
|
| Series: | JACC: Advances |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772963X24008561 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832590871848026112 |
|---|---|
| author | Vera Fortmeier, MD Mark Lachmann, MD Lukas Stolz, MD Jennifer von Stein, MD Karl-Philipp Rommel, MD Mohammad Kassar, MD Muhammed Gerçek, MD Anne R. Schöber, MD Thomas J. Stocker, MD Hazem Omran, MD Michelle Fett Jule Tervooren Maria I. Körber, MD Amelie Hesse Gerhard Harmsen, PhD Kai Peter Friedrichs, MD Shinsuke Yuasa, MD, PhD Tanja K. Rudolph, MD Michael Joner, MD Roman Pfister, MD Stephan Baldus, MD Karl-Ludwig Laugwitz, MD Stephan Windecker, MD Fabien Praz, MD Philipp Lurz, MD Jörg Hausleiter, MD Volker Rudolph, MD |
| author_facet | Vera Fortmeier, MD Mark Lachmann, MD Lukas Stolz, MD Jennifer von Stein, MD Karl-Philipp Rommel, MD Mohammad Kassar, MD Muhammed Gerçek, MD Anne R. Schöber, MD Thomas J. Stocker, MD Hazem Omran, MD Michelle Fett Jule Tervooren Maria I. Körber, MD Amelie Hesse Gerhard Harmsen, PhD Kai Peter Friedrichs, MD Shinsuke Yuasa, MD, PhD Tanja K. Rudolph, MD Michael Joner, MD Roman Pfister, MD Stephan Baldus, MD Karl-Ludwig Laugwitz, MD Stephan Windecker, MD Fabien Praz, MD Philipp Lurz, MD Jörg Hausleiter, MD Volker Rudolph, MD |
| author_sort | Vera Fortmeier, MD |
| collection | DOAJ |
| 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. |
| format | Article |
| id | doaj-art-2f1e5d3fc4694f26902a39bd1b1e9f98 |
| institution | Kabale University |
| issn | 2772-963X |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | JACC: Advances |
| spelling | doaj-art-2f1e5d3fc4694f26902a39bd1b1e9f982025-01-23T05:28:04ZengElsevierJACC: Advances2772-963X2025-02-0142101575Simplified Outcome Prediction in Patients Undergoing Transcatheter Tricuspid Valve Intervention by Survival Tree-Based ModellingVera Fortmeier, MD0Mark Lachmann, MD1Lukas Stolz, MD2Jennifer von Stein, MD3Karl-Philipp Rommel, MD4Mohammad Kassar, MD5Muhammed Gerçek, MD6Anne R. Schöber, MD7Thomas J. Stocker, MD8Hazem Omran, MD9Michelle Fett10Jule Tervooren11Maria I. Körber, MD12Amelie Hesse13Gerhard Harmsen, PhD14Kai Peter Friedrichs, MD15Shinsuke Yuasa, MD, PhD16Tanja K. Rudolph, MD17Michael Joner, MD18Roman Pfister, MD19Stephan Baldus, MD20Karl-Ludwig Laugwitz, MD21Stephan Windecker, MD22Fabien Praz, MD23Philipp Lurz, MD24Jörg Hausleiter, MD25Volker Rudolph, MD26Department of General and Interventional Cardiology, Heart and Diabetes Center North Rhine-Westphalia, Ruhr University Bochum, Bad Oeynhausen, GermanyFirst 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, GermanyDZHK (German Center for Cardiovascular Research), Partner Site Munich Heart Alliance, Munich, Germany; Medizinische Klinik und Poliklinik I, Klinikum der Universität München, Ludwig Maximilians University of Munich, Munich, GermanyDepartment of Cardiology, Heart Center, University of Cologne, Cologne, GermanyDepartment of Cardiology, Heart Center Leipzig, University of Leipzig, Leipzig, GermanyDepartment of Cardiology, Inselspital Bern, Bern University Hospital, Bern, SwitzerlandDepartment of General and Interventional Cardiology, Heart and Diabetes Center North Rhine-Westphalia, Ruhr University Bochum, Bad Oeynhausen, GermanyDepartment of Cardiology, Heart Center Leipzig, University of Leipzig, Leipzig, GermanyDZHK (German Center for Cardiovascular Research), Partner Site Munich Heart Alliance, Munich, Germany; Medizinische Klinik und Poliklinik I, Klinikum der Universität München, Ludwig Maximilians University of Munich, Munich, GermanyDepartment of General and Interventional Cardiology, Heart and Diabetes Center North Rhine-Westphalia, Ruhr University Bochum, Bad Oeynhausen, GermanyDepartment of General and Interventional Cardiology, Heart and Diabetes Center North Rhine-Westphalia, Ruhr University Bochum, Bad Oeynhausen, GermanyDepartment 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, GermanyDepartment of Cardiology, Heart Center, University of Cologne, Cologne, GermanyFirst 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, GermanyDepartment of Physics, University of Johannesburg, Auckland Park, South AfricaDepartment of General and Interventional Cardiology, Heart and Diabetes Center North Rhine-Westphalia, Ruhr University Bochum, Bad Oeynhausen, GermanyDepartment of Cardiovascular Medicine, Okayama University, Okayama, JapanDepartment of General and Interventional Cardiology, Heart and Diabetes Center North Rhine-Westphalia, Ruhr University Bochum, Bad Oeynhausen, GermanyDZHK (German Center for Cardiovascular Research), Partner Site Munich Heart Alliance, Munich, Germany; Department of Cardiology, German Heart Center Munich, Technical University of Munich, Munich, GermanyDepartment of Cardiology, Heart Center, University of Cologne, Cologne, GermanyDepartment of Cardiology, Heart Center, University of Cologne, Cologne, GermanyFirst 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, GermanyDepartment of Cardiology, Inselspital Bern, Bern University Hospital, Bern, SwitzerlandDepartment of Cardiology, Inselspital Bern, Bern University Hospital, Bern, SwitzerlandDepartment of Cardiology, Heart Center Leipzig, University of Leipzig, Leipzig, GermanyDZHK (German Center for Cardiovascular Research), Partner Site Munich Heart Alliance, Munich, Germany; Medizinische Klinik und Poliklinik I, Klinikum der Universität München, Ludwig Maximilians University of Munich, Munich, GermanyDepartment of General and Interventional Cardiology, Heart and Diabetes Center North Rhine-Westphalia, Ruhr University Bochum, Bad Oeynhausen, Germany; Address for correspondence: Dr Volker Rudolph, Department of General and Interventional Cardiology, Heart and Diabetes Center North Rhine-Westfalia, Ruhr University Bochum, Georgstraße 11, 32545 Bad Oeynhausen, Germany.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.http://www.sciencedirect.com/science/article/pii/S2772963X24008561machine learningtranscatheter tricuspid valve interventiontricuspid regurgitation |
| spellingShingle | Vera Fortmeier, MD Mark Lachmann, MD Lukas Stolz, MD Jennifer von Stein, MD Karl-Philipp Rommel, MD Mohammad Kassar, MD Muhammed Gerçek, MD Anne R. Schöber, MD Thomas J. Stocker, MD Hazem Omran, MD Michelle Fett Jule Tervooren Maria I. Körber, MD Amelie Hesse Gerhard Harmsen, PhD Kai Peter Friedrichs, MD Shinsuke Yuasa, MD, PhD Tanja K. Rudolph, MD Michael Joner, MD Roman Pfister, MD Stephan Baldus, MD Karl-Ludwig Laugwitz, MD Stephan Windecker, MD Fabien Praz, MD Philipp Lurz, MD Jörg Hausleiter, MD Volker Rudolph, MD Simplified Outcome Prediction in Patients Undergoing Transcatheter Tricuspid Valve Intervention by Survival Tree-Based Modelling JACC: Advances machine learning transcatheter tricuspid valve intervention tricuspid regurgitation |
| title | Simplified Outcome Prediction in Patients Undergoing Transcatheter Tricuspid Valve Intervention by Survival Tree-Based Modelling |
| title_full | Simplified Outcome Prediction in Patients Undergoing Transcatheter Tricuspid Valve Intervention by Survival Tree-Based Modelling |
| title_fullStr | Simplified Outcome Prediction in Patients Undergoing Transcatheter Tricuspid Valve Intervention by Survival Tree-Based Modelling |
| title_full_unstemmed | Simplified Outcome Prediction in Patients Undergoing Transcatheter Tricuspid Valve Intervention by Survival Tree-Based Modelling |
| title_short | Simplified Outcome Prediction in Patients Undergoing Transcatheter Tricuspid Valve Intervention by Survival Tree-Based Modelling |
| title_sort | simplified outcome prediction in patients undergoing transcatheter tricuspid valve intervention by survival tree based modelling |
| topic | machine learning transcatheter tricuspid valve intervention tricuspid regurgitation |
| url | http://www.sciencedirect.com/science/article/pii/S2772963X24008561 |
| work_keys_str_mv | AT verafortmeiermd simplifiedoutcomepredictioninpatientsundergoingtranscathetertricuspidvalveinterventionbysurvivaltreebasedmodelling AT marklachmannmd simplifiedoutcomepredictioninpatientsundergoingtranscathetertricuspidvalveinterventionbysurvivaltreebasedmodelling AT lukasstolzmd simplifiedoutcomepredictioninpatientsundergoingtranscathetertricuspidvalveinterventionbysurvivaltreebasedmodelling AT jennifervonsteinmd simplifiedoutcomepredictioninpatientsundergoingtranscathetertricuspidvalveinterventionbysurvivaltreebasedmodelling AT karlphilipprommelmd simplifiedoutcomepredictioninpatientsundergoingtranscathetertricuspidvalveinterventionbysurvivaltreebasedmodelling AT mohammadkassarmd simplifiedoutcomepredictioninpatientsundergoingtranscathetertricuspidvalveinterventionbysurvivaltreebasedmodelling AT muhammedgercekmd simplifiedoutcomepredictioninpatientsundergoingtranscathetertricuspidvalveinterventionbysurvivaltreebasedmodelling AT annerschobermd simplifiedoutcomepredictioninpatientsundergoingtranscathetertricuspidvalveinterventionbysurvivaltreebasedmodelling AT thomasjstockermd simplifiedoutcomepredictioninpatientsundergoingtranscathetertricuspidvalveinterventionbysurvivaltreebasedmodelling AT hazemomranmd simplifiedoutcomepredictioninpatientsundergoingtranscathetertricuspidvalveinterventionbysurvivaltreebasedmodelling AT michellefett simplifiedoutcomepredictioninpatientsundergoingtranscathetertricuspidvalveinterventionbysurvivaltreebasedmodelling AT juletervooren simplifiedoutcomepredictioninpatientsundergoingtranscathetertricuspidvalveinterventionbysurvivaltreebasedmodelling AT mariaikorbermd simplifiedoutcomepredictioninpatientsundergoingtranscathetertricuspidvalveinterventionbysurvivaltreebasedmodelling AT ameliehesse simplifiedoutcomepredictioninpatientsundergoingtranscathetertricuspidvalveinterventionbysurvivaltreebasedmodelling AT gerhardharmsenphd simplifiedoutcomepredictioninpatientsundergoingtranscathetertricuspidvalveinterventionbysurvivaltreebasedmodelling AT kaipeterfriedrichsmd simplifiedoutcomepredictioninpatientsundergoingtranscathetertricuspidvalveinterventionbysurvivaltreebasedmodelling AT shinsukeyuasamdphd simplifiedoutcomepredictioninpatientsundergoingtranscathetertricuspidvalveinterventionbysurvivaltreebasedmodelling AT tanjakrudolphmd simplifiedoutcomepredictioninpatientsundergoingtranscathetertricuspidvalveinterventionbysurvivaltreebasedmodelling AT michaeljonermd simplifiedoutcomepredictioninpatientsundergoingtranscathetertricuspidvalveinterventionbysurvivaltreebasedmodelling AT romanpfistermd simplifiedoutcomepredictioninpatientsundergoingtranscathetertricuspidvalveinterventionbysurvivaltreebasedmodelling AT stephanbaldusmd simplifiedoutcomepredictioninpatientsundergoingtranscathetertricuspidvalveinterventionbysurvivaltreebasedmodelling AT karlludwiglaugwitzmd simplifiedoutcomepredictioninpatientsundergoingtranscathetertricuspidvalveinterventionbysurvivaltreebasedmodelling AT stephanwindeckermd simplifiedoutcomepredictioninpatientsundergoingtranscathetertricuspidvalveinterventionbysurvivaltreebasedmodelling AT fabienprazmd simplifiedoutcomepredictioninpatientsundergoingtranscathetertricuspidvalveinterventionbysurvivaltreebasedmodelling AT philipplurzmd simplifiedoutcomepredictioninpatientsundergoingtranscathetertricuspidvalveinterventionbysurvivaltreebasedmodelling AT jorghausleitermd simplifiedoutcomepredictioninpatientsundergoingtranscathetertricuspidvalveinterventionbysurvivaltreebasedmodelling AT volkerrudolphmd simplifiedoutcomepredictioninpatientsundergoingtranscathetertricuspidvalveinterventionbysurvivaltreebasedmodelling |