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Machine learning-based prediction of in-hospital death for patients with takotsubo syndrome: The InterTAK-ML model
O. De Filippo, VL. Cammann, C. Pancotti, D. Di Vece, A. Silverio, V. Schweiger, D. Niederseer, KA. Szawan, M. Würdinger, I. Koleva, V. Dusi, M. Bellino, C. Vecchione, G. Parodi, E. Bossone, S. Gili, M. Neuhaus, J. Franke, B. Meder, M....
Jazyk angličtina Země Anglie, Velká Británie
Typ dokumentu časopisecké články
NLK
Medline Complete (EBSCOhost)
od 2000-03-01 do Před 1 rokem
Wiley Free Content
od 1999 do Před 1 rokem
PubMed
37522520
DOI
10.1002/ejhf.2983
Knihovny.cz E-zdroje
- MeSH
- lidé MeSH
- mortalita v nemocnicích MeSH
- prognóza MeSH
- srdeční selhání * komplikace MeSH
- strojové učení MeSH
- takotsubo kardiomyopatie * diagnóza komplikace MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
AIMS: Takotsubo syndrome (TTS) is associated with a substantial rate of adverse events. We sought to design a machine learning (ML)-based model to predict the risk of in-hospital death and to perform a clustering of TTS patients to identify different risk profiles. METHODS AND RESULTS: A ridge logistic regression-based ML model for predicting in-hospital death was developed on 3482 TTS patients from the International Takotsubo (InterTAK) Registry, randomly split in a train and an internal validation cohort (75% and 25% of the sample size, respectively) and evaluated in an external validation cohort (1037 patients). Thirty-one clinically relevant variables were included in the prediction model. Model performance represented the primary endpoint and was assessed according to area under the curve (AUC), sensitivity and specificity. As secondary endpoint, a K-medoids clustering algorithm was designed to stratify patients into phenotypic groups based on the 10 most relevant features emerging from the main model. The overall incidence of in-hospital death was 5.2%. The InterTAK-ML model showed an AUC of 0.89 (0.85-0.92), a sensitivity of 0.85 (0.78-0.95) and a specificity of 0.76 (0.74-0.79) in the internal validation cohort and an AUC of 0.82 (0.73-0.91), a sensitivity of 0.74 (0.61-0.87) and a specificity of 0.79 (0.77-0.81) in the external cohort for in-hospital death prediction. By exploiting the 10 variables showing the highest feature importance, TTS patients were clustered into six groups associated with different risks of in-hospital death (28.8% vs. 15.5% vs. 5.4% vs. 1.0.8% vs. 0.5%) which were consistent also in the external cohort. CONCLUSION: A ML-based approach for the identification of TTS patients at risk of adverse short-term prognosis is feasible and effective. The InterTAK-ML model showed unprecedented discriminative capability for the prediction of in-hospital death.
1st Department of Cardiology Medical University of Gdansk Gdansk Poland
Berlin Institute of Health Berlin Germany
Center for Cardiology Cardiology 1 University Medical Center Mainz Mainz Germany
Center for Molecular Cardiology Schlieren Campus University of Zurich Zurich Switzerland
Centro Cardiologico Monzino IRCCS Milan Italy
Clinic for Cardiology and Pneumology Georg August University Goettingen Goettingen Germany
Department of Cardiology and Angiology Hannover Medical School Hannover Germany
Department of Cardiology and Cardiac Imaging Center University Hospital of Rangueil Toulouse France
Department of Cardiology and Internal Medicine B University Medicine Greifswald Greifswald Germany
Department of Cardiology Centro Hospitalar Universitário de São João Porto Portugal
Department of Cardiology Charité Campus Rudolf Virchow Berlin Germany
Department of Cardiology Chiba Emergency Medical Center Chiba Japan
Department of Cardiology Christchurch Hospital Christchurch New Zealand
Department of Cardiology Heidelberg University Hospital Heidelberg Germany
Department of Cardiology John Radcliffe Hospital Oxford University Hospitals Oxford UK
Department of Cardiology Kantonsspital Frauenfeld Frauenfeld Switzerland
Department of Cardiology Kantonsspital Lucerne Lucerne Switzerland
Department of Cardiology Kantonsspital St Gallen St Gallen Switzerland
Department of Cardiology Kantonsspital Winterthur Winterthur Switzerland
Department of Cardiology King's College Hospital London UK
Department of Cardiology Leiden University Medical Centre Leiden The Netherlands
Department of Cardiology Medical University of Warsaw Warsaw Poland
Department of Cardiology National University Heart Centre Singapore Singapore
Department of Cardiology University Hospital Basel Basel Switzerland
Department of Cardiovascular Diseases Mayo Clinic Rochester MN USA
Department of Cardiovascular Medicine Chiba University Graduate School of Medicine Chiba Japan
Department of Cardiovascular Medicine Nippon Medical School Tokyo Japan
Department of Intensive Care Medicine University Medical Center Hamburg Eppendorf Hamburg Germany
Department of Internal Medicine 1 Cardiology University Hospital Olomouc Olomouc Czech Republic
Department of Internal Medicine 2 Cardiology Medical Center University of Ulm Ulm Germany
Department of Internal Medicine 3 Heart Center University of Cologne Cologne Germany
Department of Internal Medicine Cardiology and Angiology Magdeburg University Magdeburg Germany
Department of Internal Medicine Cardiology Heart Center Leipzig University Hospital Leipzig Germany
Department of Medical Sciences University of Turin Turin Italy
Department of Medicine Surgery and Dentistry University of Salerno Baronissi Italy
Department of Medicine Surgery and Pharmacy University of Sassari Sassari Italy
Department of Vascular Physiopathology IRCCS Neuromed Pozzilli Italy
Deutsches Herzzentrum München Technische Universität München Munich Germany
Division of Cardiology 'Antonio Cardarelli' Hospital Naples Italy
Division of Cardiology A O U San Luigi Gonzaga Turin Italy
Division of Cardiology Heart and Vascular Center University of Iowa Iowa City IA USA
Division of Cardiology Kimitsu Central Hospital Kisarazu Japan
Division of Cardiology Medical University of Graz Graz Austria
Dorset Heart Centre Royal Bournemouth Hospital Bournemouth UK
DZHK Partner Site Greifswald Greifswald Germany
DZHK Partner Site Hamburg Kiel Luebeck Hamburg Germany
DZHK Partner Site Heidelberg Mannheim Mannheim Germany
DZHK Partner Site Munich Heart Alliance Munich Germany
Heart and Vascular Centre Bad Bevensen Bad Bevensen Germany
Heart Center Turku University Hospital University of Turku Turku Finland
Intensive Coronary Care Unit Moscow City Hospital No 1 named after N Pirogov Moscow Russia
Keck School of Medicine University of Southern California Los Angeles CA USA
Klinik für Innere Medizin 3 Universitätsklinikum des Saarlandes Homburg Saar Germany
Klinik und Poliklinik für Innere Medizin 2 Universitätsklinikum Regensburg Regensburg Germany
Krankenhaus 'Maria Hilf' Medizinische Klinik Stadtlohn Germany
Local Health Unit n 8 Cardiology Unit Vicenza Italy
Royal Brompton and Harefield Hospitals Trust and Imperial College and Kings College London UK
Service de Cardiologie Hôpitaux Universitaires de Genève Geneva Switzerland
Structural Interventional Cardiology Careggi University Hospital Florence Italy
TJ Health Partners Heart and Vascular Glasgow KY USA
University Hospital for Internal Medicine 3 Medical University Innsbruck Innsbruck Austria
Citace poskytuje Crossref.org
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- $a De Filippo, Ovidio $u Division of Cardiology, Department of Medical Sciences, AOU Città della Salute e della Scienza, University of Turin, Turin, Italy
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- $a Machine learning-based prediction of in-hospital death for patients with takotsubo syndrome: The InterTAK-ML model / $c O. De Filippo, VL. Cammann, C. Pancotti, D. Di Vece, A. Silverio, V. Schweiger, D. Niederseer, KA. Szawan, M. Würdinger, I. Koleva, V. Dusi, M. Bellino, C. Vecchione, G. Parodi, E. Bossone, S. Gili, M. Neuhaus, J. Franke, B. Meder, M. Jaguszewski, M. Noutsias, M. Knorr, T. Jansen, W. Dichtl, D. von Lewinski, C. Burgdorf, B. Kherad, C. Tschöpe, A. Sarcon, J. Shinbane, L. Rajan, G. Michels, R. Pfister, A. Cuneo, C. Jacobshagen, M. Karakas, W. Koenig, A. Pott, P. Meyer, M. Roffi, A. Banning, M. Wolfrum, F. Cuculi, R. Kobza, TA. Fischer, T. Vasankari, KEJ. Airaksinen, LC. Napp, R. Dworakowski, P. MacCarthy, C. Kaiser, S. Osswald, L. Galiuto, C. Chan, P. Bridgman, D. Beug, C. Delmas, O. Lairez, E. Gilyarova, A. Shilova, M. Gilyarov, I. El-Battrawy, I. Akin, K. Poledniková, P. Toušek, DE. Winchester, M. Massoomi, J. Galuszka, C. Ukena, G. Poglajen, P. Carrilho-Ferreira, C. Hauck, C. Paolini, C. Bilato, Y. Kobayashi, K. Kato, I. Ishibashi, T. Himi, J. Din, A. Al-Shammari, A. Prasad, CS. Rihal, K. Liu, PC. Schulze, M. Bianco, L. Jörg, H. Rickli, G. Pestana, TH. Nguyen, M. Böhm, LS. Maier, FJ. Pinto, P. Widimský, SB. Felix, RC. Braun-Dullaeus, W. Rottbauer, G. Hasenfuß, BM. Pieske, H. Schunkert, M. Budnik, G. Opolski, H. Thiele, J. Bauersachs, JD. Horowitz, C. Di Mario, F. Bruno, W. Kong, M. Dalakoti, Y. Imori, T. Münzel, F. Crea, TF. Lüscher, JJ. Bax, F. Ruschitzka, GM. De Ferrari, P. Fariselli, JR. Ghadri, R. Citro, F. D'Ascenzo, C. Templin
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- $a AIMS: Takotsubo syndrome (TTS) is associated with a substantial rate of adverse events. We sought to design a machine learning (ML)-based model to predict the risk of in-hospital death and to perform a clustering of TTS patients to identify different risk profiles. METHODS AND RESULTS: A ridge logistic regression-based ML model for predicting in-hospital death was developed on 3482 TTS patients from the International Takotsubo (InterTAK) Registry, randomly split in a train and an internal validation cohort (75% and 25% of the sample size, respectively) and evaluated in an external validation cohort (1037 patients). Thirty-one clinically relevant variables were included in the prediction model. Model performance represented the primary endpoint and was assessed according to area under the curve (AUC), sensitivity and specificity. As secondary endpoint, a K-medoids clustering algorithm was designed to stratify patients into phenotypic groups based on the 10 most relevant features emerging from the main model. The overall incidence of in-hospital death was 5.2%. The InterTAK-ML model showed an AUC of 0.89 (0.85-0.92), a sensitivity of 0.85 (0.78-0.95) and a specificity of 0.76 (0.74-0.79) in the internal validation cohort and an AUC of 0.82 (0.73-0.91), a sensitivity of 0.74 (0.61-0.87) and a specificity of 0.79 (0.77-0.81) in the external cohort for in-hospital death prediction. By exploiting the 10 variables showing the highest feature importance, TTS patients were clustered into six groups associated with different risks of in-hospital death (28.8% vs. 15.5% vs. 5.4% vs. 1.0.8% vs. 0.5%) which were consistent also in the external cohort. CONCLUSION: A ML-based approach for the identification of TTS patients at risk of adverse short-term prognosis is feasible and effective. The InterTAK-ML model showed unprecedented discriminative capability for the prediction of in-hospital death.
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