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Lachmann M, Hesse A, Trenkwalder T, Xhepa E, Rheude T, von Scheidt M, Covarrubias HAA, Rippen E, Hramiak O, Pellegrini C, Schuster T, Yuasa S, Schunkert H, Kastrati A, Kupatt C, Laugwitz KL, Joner M. Invasive Assessment of Right Ventricular to Pulmonary Artery Coupling Improves 1-year Mortality Prediction After Transcatheter Aortic Valve Replacement and Anticipates the Persistence of Extra-Aortic Valve Cardiac Damage. STRUCTURAL HEART : THE JOURNAL OF THE HEART TEAM 2024; 8:100282. [PMID: 38799808 PMCID: PMC11121747 DOI: 10.1016/j.shj.2024.100282] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Revised: 12/04/2023] [Accepted: 01/03/2024] [Indexed: 05/29/2024]
Abstract
Background The interplay between the right ventricle and the pulmonary artery, known as right ventricular to pulmonary artery (RV-PA) coupling, is crucial for assessing right ventricular systolic function against the afterload from the pulmonary circulation. Pulmonary artery pressure levels are ideally measured by right heart catheterization. Yet, echocardiography represents the most utilized method for evaluating pulmonary artery pressure levels, albeit with limitations in accuracy. This study therefore aims to evaluate the prognostic significance of right ventricular to pulmonary artery (RV-PA) coupling expressed as tricuspid annular plane systolic excursion (TAPSE) related to systolic pulmonary artery pressure (sPAP) levels measured by right heart catheterization (TAPSE/sPAPinvasive) or estimated by transthoracic echocardiography (TAPSE/sPAPechocardiography) in patients with severe aortic stenosis undergoing transcatheter aortic valve replacement (TAVR). Methods Using data from a bicentric registry, this study compares TAPSE/sPAPinvasive vs. TAPSE/sPAPechocardiography in predicting 1-year all-cause mortality after TAVR. Results Among 333 patients with complete echocardiography and right heart catheterization data obtained before TAVR, their mean age was 79.8 ± 6.74 years, 39.6% were female, and general 1-year survival was 89.8%. sPAPinvasive and sPAPechocardiography showed only moderate correlation (Pearson correlation coefficient R: 0.53, p value: <0.0001). TAPSE/sPAPinvasive was superior to TAPSE/sPAPechocardiography in predicting 1-year all-cause mortality after TAVR (area under the curve: 0.662 vs. 0.569, p value: 0.025). Patients with reduced TAPSE/sPAPinvasive levels (< 0.365 mm/mmHg) evidenced significantly lower 1-year survival rates than patients with preserved TAPSE/sPAPinvasive levels (81.8 vs. 93.6%, p value: 0.001; hazard ratio for 1-year mortality: 3.09 [95% confidence interval: 1.55-6.17]). Echocardiographic follow-up data revealed that patients with reduced RV-PA coupling suffer from persistent right ventricular dysfunction (TAPSE: 16.6 ± 4.05 mm vs. 21.6 ± 4.81 mm in patients with preserved RV-PA coupling) and severe tricuspid regurgitation (diagnosed in 19.7 vs. 6.58% in patients with preserved RV-PA coupling). Conclusions RV-PA coupling expressed as TAPSE/sPAPinvasive can refine stratification of severe aortic stenosis patients into low-risk and high-risk cohorts for mortality after TAVR. Moreover, it can help to anticipate persistent extra-aortic valve cardiac damage, which will demand further treatment.
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Affiliation(s)
- Mark Lachmann
- First Department of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- DZHK (German Centre for Cardiovascular Research), Partner Site Munich Heart Alliance, Munich, Germany
| | - Amelie Hesse
- First Department of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- DZHK (German Centre for Cardiovascular Research), Partner Site Munich Heart Alliance, Munich, Germany
| | - Teresa Trenkwalder
- DZHK (German Centre for Cardiovascular Research), Partner Site Munich Heart Alliance, Munich, Germany
- Department of Cardiology, Deutsches Herzzentrum München, Technical University of Munich, Munich, Germany
| | - Erion Xhepa
- DZHK (German Centre for Cardiovascular Research), Partner Site Munich Heart Alliance, Munich, Germany
- Department of Cardiology, Deutsches Herzzentrum München, Technical University of Munich, Munich, Germany
| | - Tobias Rheude
- Department of Cardiology, Deutsches Herzzentrum München, Technical University of Munich, Munich, Germany
| | - Moritz von Scheidt
- DZHK (German Centre for Cardiovascular Research), Partner Site Munich Heart Alliance, Munich, Germany
- Department of Cardiology, Deutsches Herzzentrum München, Technical University of Munich, Munich, Germany
| | | | - Elena Rippen
- First Department of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- DZHK (German Centre for Cardiovascular Research), Partner Site Munich Heart Alliance, Munich, Germany
| | - Oksana Hramiak
- First Department of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- Specialized Department of Cardiology, Ternopil City Communal Hospital №2, Ternopil National Medical University, Ternopil, Ukraine
| | - Costanza Pellegrini
- Department of Cardiology, Deutsches Herzzentrum München, Technical University of Munich, Munich, Germany
| | - Tibor Schuster
- Department of Family Medicine, McGill University, Montreal, Canada
| | - Shinsuke Yuasa
- Department of Cardiology, Keio University School of Medicine, Tokyo, Japan
| | - Heribert Schunkert
- DZHK (German Centre for Cardiovascular Research), Partner Site Munich Heart Alliance, Munich, Germany
- Department of Cardiology, Deutsches Herzzentrum München, Technical University of Munich, Munich, Germany
| | - Adnan Kastrati
- DZHK (German Centre for Cardiovascular Research), Partner Site Munich Heart Alliance, Munich, Germany
- Department of Cardiology, Deutsches Herzzentrum München, Technical University of Munich, Munich, Germany
| | - Christian Kupatt
- First Department of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- DZHK (German Centre for Cardiovascular Research), Partner Site Munich Heart Alliance, Munich, Germany
| | - Karl-Ludwig Laugwitz
- First Department of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- DZHK (German Centre for Cardiovascular Research), Partner Site Munich Heart Alliance, Munich, Germany
| | - Michael Joner
- DZHK (German Centre for Cardiovascular Research), Partner Site Munich Heart Alliance, Munich, Germany
- Department of Cardiology, Deutsches Herzzentrum München, Technical University of Munich, Munich, Germany
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Fortmeier V, Lachmann M, Stolz L, von Stein J, Unterhuber M, Kassar M, Gerçek M, Schöber AR, Stocker TJ, Omran H, Körber MI, Hesse A, Harmsen G, Friedrichs KP, Yuasa S, Rudolph TK, Joner M, Pfister R, Baldus S, Laugwitz KL, Windecker S, Praz F, Lurz P, Hausleiter J, Rudolph V. Artificial intelligence-enabled assessment of right ventricular to pulmonary artery coupling in patients undergoing transcatheter tricuspid valve intervention. Eur Heart J Cardiovasc Imaging 2024; 25:558-572. [PMID: 37996066 DOI: 10.1093/ehjci/jead324] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/26/2023] [Revised: 10/09/2023] [Accepted: 10/12/2023] [Indexed: 11/25/2023] Open
Abstract
AIMS Right ventricular to pulmonary artery (RV-PA) coupling has been established as a prognostic marker in patients with severe tricuspid regurgitation (TR) undergoing transcatheter tricuspid valve interventions (TTVI). RV-PA coupling assesses right ventricular systolic function related to pulmonary artery pressure levels, which are ideally measured by right heart catheterization. This study aimed to improve the RV-PA coupling concept by relating tricuspid annular plane systolic excursion (TAPSE) to mean pulmonary artery pressure (mPAP) levels. Moreover, instead of right heart catheterization, this study sought to employ an extreme gradient boosting (XGB) algorithm to predict mPAP levels based on standard echocardiographic parameters. METHODS AND RESULTS This multicentre study included 737 patients undergoing TTVI for severe TR; among them, 55 patients from one institution served for external validation. Complete echocardiography and right heart catheterization data were available from all patients. The XGB algorithm trained on 10 echocardiographic parameters could reliably predict mPAP levels as evaluated on right heart catheterization data from external validation (Pearson correlation coefficient R: 0.68; P value: 1.3 × 10-8). Moreover, predicted mPAP (mPAPpredicted) levels were superior to echocardiographic systolic pulmonary artery pressure (sPAPechocardiography) levels in predicting 2-year mortality after TTVI [area under the curve (AUC): 0.607 vs. 0.520; P value: 1.9 × 10-6]. Furthermore, TAPSE/mPAPpredicted was superior to TAPSE/sPAPechocardiography in predicting 2-year mortality after TTVI (AUC: 0.633 vs. 0.586; P value: 0.008). Finally, patients with preserved RV-PA coupling (defined as TAPSE/mPAPpredicted > 0.617 mm/mmHg) showed significantly higher 2-year survival rates after TTVI than patients with reduced RV-PA coupling (81.5% vs. 58.8%, P < 0.001). Moreover, independent association between TAPSE/mPAPpredicted levels and 2-year mortality after TTVI was confirmed by multivariate regression analysis (P value: 6.3 × 10-4). CONCLUSION Artificial intelligence-enabled RV-PA coupling assessment can refine risk stratification prior to TTVI without necessitating invasive right heart catheterization. A comparison with conservatively treated patients is mandatory to quantify the benefit of TTVI in accordance with RV-PA coupling.
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Affiliation(s)
- Vera Fortmeier
- Department of General and Interventional Cardiology, Heart and Diabetes Center North Rhine-Westphalia, Ruhr University Bochum, Georgstraße 11, 32545 Bad Oeynhausen, Germany
| | - Mark Lachmann
- 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
| | - Lukas Stolz
- DZHK (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, Germany
| | - Jennifer von Stein
- Department of Cardiology, Heart Center, University of Cologne, Cologne, Germany
| | - Matthias Unterhuber
- Department of Cardiology, Heart Center Leipzig, University of Leipzig, Leipzig, Germany
| | - Mohammad Kassar
- Department of Cardiology, Inselspital Bern, Bern University Hospital, Bern, Switzerland
| | - Muhammed Gerçek
- Department of General and Interventional Cardiology, Heart and Diabetes Center North Rhine-Westphalia, Ruhr University Bochum, Georgstraße 11, 32545 Bad Oeynhausen, Germany
| | - Anne R Schöber
- Department of Cardiology, Heart Center Leipzig, University of Leipzig, Leipzig, Germany
| | - Thomas J Stocker
- DZHK (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, Germany
| | - Hazem Omran
- Department of General and Interventional Cardiology, Heart and Diabetes Center North Rhine-Westphalia, Ruhr University Bochum, Georgstraße 11, 32545 Bad Oeynhausen, Germany
| | - Maria I Körber
- Department of Cardiology, Heart Center, University of Cologne, Cologne, Germany
| | - Amelie Hesse
- 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
| | - Gerhard Harmsen
- Department of Physics, University of Johannesburg, Auckland Park, South Africa
| | - Kai Peter Friedrichs
- Department of General and Interventional Cardiology, Heart and Diabetes Center North Rhine-Westphalia, Ruhr University Bochum, Georgstraße 11, 32545 Bad Oeynhausen, Germany
| | - Shinsuke Yuasa
- Department of Cardiology, Keio University School of Medicine, Tokyo, Japan
| | - Tanja K Rudolph
- Department of General and Interventional Cardiology, Heart and Diabetes Center North Rhine-Westphalia, Ruhr University Bochum, Georgstraße 11, 32545 Bad Oeynhausen, Germany
| | - Michael Joner
- DZHK (German Center for Cardiovascular Research), partner site Munich Heart Alliance, Munich, Germany
- Department of Cardiology, German Heart Center Munich, Technical University of Munich, Munich, Germany
| | - Roman Pfister
- Department of Cardiology, Heart Center, University of Cologne, Cologne, Germany
| | - Stephan Baldus
- Department of Cardiology, Heart Center, University of Cologne, Cologne, Germany
| | - Karl-Ludwig Laugwitz
- 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
| | - Stephan Windecker
- Department of Cardiology, Inselspital Bern, Bern University Hospital, Bern, Switzerland
| | - Fabien Praz
- Department of Cardiology, Inselspital Bern, Bern University Hospital, Bern, Switzerland
| | - Philipp Lurz
- Department of Cardiology, Heart Center Leipzig, University of Leipzig, Leipzig, Germany
| | - Jörg Hausleiter
- DZHK (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, Germany
| | - Volker Rudolph
- Department of General and Interventional Cardiology, Heart and Diabetes Center North Rhine-Westphalia, Ruhr University Bochum, Georgstraße 11, 32545 Bad Oeynhausen, Germany
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Zhang Y, Wang M, Zhang E, Wu Y. Artificial Intelligence in the Screening, Diagnosis, and Management of Aortic Stenosis. Rev Cardiovasc Med 2024; 25:31. [PMID: 39077660 PMCID: PMC11262349 DOI: 10.31083/j.rcm2501031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 08/30/2023] [Accepted: 09/13/2023] [Indexed: 07/31/2024] Open
Abstract
The integration of artificial intelligence (AI) into clinical management of aortic stenosis (AS) has redefined our approach to the assessment and management of this heterogenous valvular heart disease (VHD). While the large-scale early detection of valvular conditions is limited by socioeconomic constraints, AI offers a cost-effective alternative solution for screening by utilizing conventional tools, including electrocardiograms and community-level auscultations, thereby facilitating early detection, prevention, and treatment of AS. Furthermore, AI sheds light on the varied nature of AS, once considered a uniform condition, allowing for more nuanced, data-driven risk assessments and treatment plans. This presents an opportunity to re-evaluate the complexity of AS and to refine treatment using data-driven risk stratification beyond traditional guidelines. AI can be used to support treatment decisions including device selection, procedural techniques, and follow-up surveillance of transcatheter aortic valve replacement (TAVR) in a reproducible manner. While recognizing notable AI achievements, it is important to remember that AI applications in AS still require collaboration with human expertise due to potential limitations such as its susceptibility to bias, and the critical nature of healthcare. This synergy underpins our optimistic view of AI's promising role in the AS clinical pathway.
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Affiliation(s)
- Yuxuan Zhang
- Department of Cardiology, State Key Laboratory of Cardiovascular Disease,
Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of
Medical Sciences and Peking Union Medical College, 100037 Beijing, China
- Center for Structural Heart Diseases, State Key Laboratory of
Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular
Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College,
100037 Beijing, China
| | - Moyang Wang
- Department of Cardiology, State Key Laboratory of Cardiovascular Disease,
Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of
Medical Sciences and Peking Union Medical College, 100037 Beijing, China
- Center for Structural Heart Diseases, State Key Laboratory of
Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular
Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College,
100037 Beijing, China
| | - Erli Zhang
- Department of Cardiology, State Key Laboratory of Cardiovascular Disease,
Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of
Medical Sciences and Peking Union Medical College, 100037 Beijing, China
- Center for Structural Heart Diseases, State Key Laboratory of
Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular
Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College,
100037 Beijing, China
| | - Yongjian Wu
- Department of Cardiology, State Key Laboratory of Cardiovascular Disease,
Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of
Medical Sciences and Peking Union Medical College, 100037 Beijing, China
- Center for Structural Heart Diseases, State Key Laboratory of
Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular
Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College,
100037 Beijing, China
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Chokesuwattanaskul R, Petchlorlian A, Lertsanguansinchai P, Suttirut P, Prasitlumkum N, Srimahachota S, Buddhari W. Machine Learning-Based Predictive Model of Aortic Valve Replacement Modality Selection in Severe Aortic Stenosis Patients. Med Sci (Basel) 2023; 12:3. [PMID: 38249079 PMCID: PMC10801609 DOI: 10.3390/medsci12010003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2023] [Revised: 12/13/2023] [Accepted: 12/20/2023] [Indexed: 01/23/2024] Open
Abstract
The current recommendation for bioprosthetic valve replacement in severe aortic stenosis (AS) is either surgical aortic valve replacement (SAVR) or transcatheter aortic valve replacement (TAVR). We evaluated the performance of a machine learning-based predictive model using existing periprocedural variables for valve replacement modality selection. We analyzed 415 patients in a retrospective longitudinal cohort of adult patients undergoing aortic valve replacement for aortic stenosis. A total of 72 clinical variables including demographic data, patient comorbidities, and preoperative investigation characteristics were collected on each patient. We fit models using LASSO (least absolute shrinkage and selection operator) and decision tree techniques. The accuracy of the prediction on confusion matrix was used to assess model performance. The most predictive independent variable for valve selection by LASSO regression was frailty score. Variables that predict SAVR consisted of low frailty score (value at or below 2) and complex coronary artery diseases (DVD/TVD). Variables that predicted TAVR consisted of high frailty score (at or greater than 6), history of coronary artery bypass surgery (CABG), calcified aorta, and chronic kidney disease (CKD). The LASSO-generated predictive model achieved 98% accuracy on valve replacement modality selection from testing data. The decision tree model consisted of fewer important parameters, namely frailty score, CKD, STS score, age, and history of PCI. The most predictive factor for valve replacement selection was frailty score. The predictive models using different statistical learning methods achieved an excellent concordance predictive accuracy rate of between 93% and 98%.
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Affiliation(s)
- Ronpichai Chokesuwattanaskul
- Division of Cardiovascular Medicine, Department of Medicine, Faculty of Medicine, Center of Excellence in Arrhythmia Research, Chulalongkorn University, Bangkok 10330, Thailand; (R.C.); (P.L.); (P.S.); (S.S.); (W.B.)
- Cardiac Center, King Chulalongkorn Memorial Hospital, The Thai Red Cross Society, Bangkok 10330, Thailand
| | - Aisawan Petchlorlian
- Division of Geriatric Medicine, Department of Medicine, Faculty of Medicine, Chulalongkorn University, Bangkok 10330, Thailand;
- Geriatric Excellence Center, King Chulalongkorn Memorial Hospital, The Thai Red Cross Society, Bangkok 10330, Thailand
| | - Piyoros Lertsanguansinchai
- Division of Cardiovascular Medicine, Department of Medicine, Faculty of Medicine, Center of Excellence in Arrhythmia Research, Chulalongkorn University, Bangkok 10330, Thailand; (R.C.); (P.L.); (P.S.); (S.S.); (W.B.)
- Cardiac Center, King Chulalongkorn Memorial Hospital, The Thai Red Cross Society, Bangkok 10330, Thailand
| | - Paramaporn Suttirut
- Division of Cardiovascular Medicine, Department of Medicine, Faculty of Medicine, Center of Excellence in Arrhythmia Research, Chulalongkorn University, Bangkok 10330, Thailand; (R.C.); (P.L.); (P.S.); (S.S.); (W.B.)
- Cardiac Center, King Chulalongkorn Memorial Hospital, The Thai Red Cross Society, Bangkok 10330, Thailand
| | - Narut Prasitlumkum
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN 559020, USA
| | - Suphot Srimahachota
- Division of Cardiovascular Medicine, Department of Medicine, Faculty of Medicine, Center of Excellence in Arrhythmia Research, Chulalongkorn University, Bangkok 10330, Thailand; (R.C.); (P.L.); (P.S.); (S.S.); (W.B.)
- Cardiac Center, King Chulalongkorn Memorial Hospital, The Thai Red Cross Society, Bangkok 10330, Thailand
| | - Wacin Buddhari
- Division of Cardiovascular Medicine, Department of Medicine, Faculty of Medicine, Center of Excellence in Arrhythmia Research, Chulalongkorn University, Bangkok 10330, Thailand; (R.C.); (P.L.); (P.S.); (S.S.); (W.B.)
- Cardiac Center, King Chulalongkorn Memorial Hospital, The Thai Red Cross Society, Bangkok 10330, Thailand
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Güldener U, Kessler T, von Scheidt M, Hawe JS, Gerhard B, Maier D, Lachmann M, Laugwitz KL, Cassese S, Schömig AW, Kastrati A, Schunkert H. Machine Learning Identifies New Predictors on Restenosis Risk after Coronary Artery Stenting in 10,004 Patients with Surveillance Angiography. J Clin Med 2023; 12:jcm12082941. [PMID: 37109283 PMCID: PMC10142067 DOI: 10.3390/jcm12082941] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Revised: 03/31/2023] [Accepted: 04/15/2023] [Indexed: 04/29/2023] Open
Abstract
OBJECTIVE Machine learning (ML) approaches have the potential to uncover regular patterns in multi-layered data. Here we applied self-organizing maps (SOMs) to detect such patterns with the aim to better predict in-stent restenosis (ISR) at surveillance angiography 6 to 8 months after percutaneous coronary intervention with stenting. METHODS In prospectively collected data from 10,004 patients receiving percutaneous coronary intervention (PCI) for 15,004 lesions, we applied SOMs to predict ISR angiographically 6-8 months after index procedure. SOM findings were compared with results of conventional uni- and multivariate analyses. The predictive value of both approaches was assessed after random splitting of patients into training and test sets (50:50). RESULTS Conventional multivariate analyses revealed 10, mostly known, predictors for restenosis after coronary stenting: balloon-to-vessel ratio, complex lesion morphology, diabetes mellitus, left main stenting, stent type (bare metal vs. first vs. second generation drug eluting stent), stent length, stenosis severity, vessel size reduction, and prior bypass surgery. The SOM approach identified all these and nine further predictors, including chronic vessel occlusion, lesion length, and prior PCI. Moreover, the SOM-based model performed well in predicting ISR (AUC under ROC: 0.728); however, there was no meaningful advantage in predicting ISR at surveillance angiography in comparison with the conventional multivariable model (0.726, p = 0.3). CONCLUSIONS The agnostic SOM-based approach identified-without clinical knowledge-even more contributors to restenosis risk. In fact, SOMs applied to a large prospectively sampled cohort identified several novel predictors of restenosis after PCI. However, as compared with established covariates, ML technologies did not improve identification of patients at high risk for restenosis after PCI in a clinically relevant fashion.
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Affiliation(s)
- Ulrich Güldener
- Department of Cardiology, Deutsches Herzzentrum München, Technische Universität München, 80636 Munich, Germany
| | - Thorsten Kessler
- Department of Cardiology, Deutsches Herzzentrum München, Technische Universität München, 80636 Munich, Germany
- DZHK (German Center for Cardiovascular Research), Partner Site Munich Heart Alliance, 80802 Munich, Germany
| | - Moritz von Scheidt
- Department of Cardiology, Deutsches Herzzentrum München, Technische Universität München, 80636 Munich, Germany
- DZHK (German Center for Cardiovascular Research), Partner Site Munich Heart Alliance, 80802 Munich, Germany
| | - Johann S Hawe
- Department of Cardiology, Deutsches Herzzentrum München, Technische Universität München, 80636 Munich, Germany
| | | | - Dieter Maier
- Biomax, Robert-Koch-Str. 2, 82152 Planegg, Germany
| | - Mark Lachmann
- Department of Cardiology, Klinikum Rechts der Isar, Technische Universität München, 81675 Munich, Germany
| | - Karl-Ludwig Laugwitz
- DZHK (German Center for Cardiovascular Research), Partner Site Munich Heart Alliance, 80802 Munich, Germany
- Department of Cardiology, Klinikum Rechts der Isar, Technische Universität München, 81675 Munich, Germany
| | - Salvatore Cassese
- Department of Cardiology, Deutsches Herzzentrum München, Technische Universität München, 80636 Munich, Germany
| | - Albert W Schömig
- Department of Cardiology, Deutsches Herzzentrum München, Technische Universität München, 80636 Munich, Germany
| | - Adnan Kastrati
- Department of Cardiology, Deutsches Herzzentrum München, Technische Universität München, 80636 Munich, Germany
- DZHK (German Center for Cardiovascular Research), Partner Site Munich Heart Alliance, 80802 Munich, Germany
| | - Heribert Schunkert
- Department of Cardiology, Deutsches Herzzentrum München, Technische Universität München, 80636 Munich, Germany
- DZHK (German Center for Cardiovascular Research), Partner Site Munich Heart Alliance, 80802 Munich, Germany
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6
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Fortmeier V, Lachmann M, Unterhuber M, Stolz L, Kassar M, Ochs L, Gerçek M, Schöber AR, Stocker TJ, Omran H, Körber MI, Hesse A, Friedrichs KP, Yuasa S, Rudolph TK, Joner M, Pfister R, Baldus S, Laugwitz KL, Praz F, Windecker S, Hausleiter J, Lurz P, Rudolph V. Epiphenomenon or Prognostically Relevant Interventional Target? A Novel Proportionality Framework for Severe Tricuspid Regurgitation. J Am Heart Assoc 2023; 12:e028737. [PMID: 36926925 PMCID: PMC10111550 DOI: 10.1161/jaha.122.028737] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/18/2023]
Abstract
Background Tricuspid regurgitation (TR) frequently develops in patients with long-standing pulmonary hypertension, and both pathologies are associated with increased morbidity and mortality. This study aimed to improve prognostic assessment in patients with severe TR undergoing transcatheter tricuspid valve intervention (TTVI) by relating the extent of TR to pulmonary artery pressures. Methods and Results In this multicenter study, we included 533 patients undergoing TTVI for moderate-to-severe or severe TR. The proportionality framework was based on the ratio of tricuspid valve effective regurgitant orifice area to mean pulmonary artery pressure. An optimal threshold for tricuspid valve effective regurgitant orifice area/mean pulmonary artery pressure ratio was derived on 353 patients with regard to 2-year all-cause mortality and externally validated on 180 patients. Patients with a tricuspid valve effective regurgitant orifice area/mean pulmonary artery pressure ratio ≤1.25 mm2/mm Hg (defining proportionate TR) featured significantly lower 2-year survival rates after TTVI than patients with disproportionate TR (56.6% versus 69.6%; P=0.005). In contrast with patients with disproportionate TR (n=398), patients with proportionate TR (n=135) showed more pronounced mPAP levels (37.9±9.06 mm Hg versus 27.9±8.17 mm Hg; P<2.2×10-16) and more severely impaired right ventricular function (tricuspid annular plane systolic excursion: 16.0±4.11 versus 17.0±4.64 mm; P=0.012). Moreover, tricuspid valve effective regurgitant orifice area was smaller in patients with proportionate TR when compared with disproportionate TR (0.350±0.105 cm2 versus 0.770±0.432 cm2; P<2.2×10-16). Importantly, proportionate TR remained a significant predictor for 2-year mortality after adjusting for demographic and clinical variables (hazard ratio, 1.7; P=0.006). Conclusions The proposed proportionality framework promises to improve future risk stratification and clinical decision-making by identifying patients who benefit the most from TTVI (disproportionate TR). As a next step, randomized controlled studies with a conservative treatment arm are needed to quantify the net benefit of TTVI in patients with proportionate TR.
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Affiliation(s)
- Vera Fortmeier
- Department of General and Interventional Cardiology, Heart and Diabetes Center North Rhine-Westphalia Ruhr University Bochum Bad Oeynhausen Germany
| | - Mark Lachmann
- 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
| | - Matthias Unterhuber
- Department of Cardiology, Heart Center Leipzig University of Leipzig Leipzig Germany
| | - Lukas Stolz
- Medizinische Klinik und Poliklinik I Klinikum der Universität München, Ludwig Maximilians University of Munich Munich Germany
| | - Mohammad Kassar
- Department of Cardiology Inselspital Bern, Bern University Hospital Bern Switzerland
| | - Laurin Ochs
- Department of Cardiology, Heart Center University of Cologne Cologne Germany
| | - Muhammed Gerçek
- Department of General and Interventional Cardiology, Heart and Diabetes Center North Rhine-Westphalia Ruhr University Bochum Bad Oeynhausen Germany
| | - Anne R Schöber
- Department of Cardiology, Heart Center Leipzig University of Leipzig Leipzig Germany
| | - Thomas J Stocker
- DZHK (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 Germany
| | - Hazem Omran
- Department of General and Interventional Cardiology, Heart and Diabetes Center North Rhine-Westphalia Ruhr University Bochum Bad Oeynhausen Germany
| | - Maria I Körber
- Department of Cardiology, Heart Center University of Cologne Cologne Germany
| | - Amelie Hesse
- 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
| | - Kai Peter Friedrichs
- Department of General and Interventional Cardiology, Heart and Diabetes Center North Rhine-Westphalia Ruhr University Bochum Bad Oeynhausen Germany
| | - Shinsuke Yuasa
- Department of Cardiology Keio University School of Medicine Tokyo Japan
| | - Tanja K Rudolph
- Department of General and Interventional Cardiology, Heart and Diabetes Center North Rhine-Westphalia Ruhr University Bochum Bad Oeynhausen Germany
| | - Michael Joner
- DZHK (German Center for Cardiovascular Research), partner site Munich Heart Alliance Munich Germany
- Department of Cardiology, German Heart Center Munich Technical University of Munich Munich Germany
| | - Roman Pfister
- Department of Cardiology, Heart Center University of Cologne Cologne Germany
| | - Stephan Baldus
- Department of Cardiology, Heart Center University of Cologne Cologne Germany
| | - Karl-Ludwig Laugwitz
- 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
| | - Fabien Praz
- Department of Cardiology Inselspital Bern, Bern University Hospital Bern Switzerland
| | - Stephan Windecker
- Department of Cardiology Inselspital Bern, Bern University Hospital Bern Switzerland
| | - Jörg Hausleiter
- DZHK (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 Germany
| | - Philipp Lurz
- Department of Cardiology, Heart Center Leipzig University of Leipzig Leipzig Germany
| | - Volker Rudolph
- Department of General and Interventional Cardiology, Heart and Diabetes Center North Rhine-Westphalia Ruhr University Bochum Bad Oeynhausen Germany
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