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Autor
Airaksinen, K E Juhani 1 Akin, Ibrahim 1 Al-Shammari, Ali 1 Banning, Adrian 1 Bauersachs, Johann 1 Bax, Jeroen J 1 Bellino, Michele 1 Beug, Daniel 1 Bianco, Matteo 1 Bilato, Claudio 1 Bossone, Eduardo 1 Braun-Dullaeus, Ruediger C 1 Bridgman, Paul 1 Bruno, Francesco 1 Budnik, Monika 1 Burgdorf, Christof 1 Böhm, Michael 1 Cammann, Victoria L 1 Carrilho-Ferreira, Pedro 1 Chan, Christina 1
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Pracoviště
1st Department of Cardiology Medical ... 1 1st Department of Medicine Faculty of... 1 Advanced Heart Failure and Transplant... 1 Berlin Institute of Health Berlin Ger... 1 CHULN Center of Cardiology of the Uni... 1 Cardiocenter 3rd Faculty of Medicine ... 1 Center for Cardiology Cardiology 1 Un... 1 Center for Molecular Cardiology Schli... 1 Centro Cardiologico Monzino IRCCS Mil... 1 Clinic for Cardiology and Pneumology ... 1 DZHK Partner Site Greifswald Greifswa... 1 DZHK Partner Site Hamburg Kiel Luebec... 1 DZHK Partner Site Heidelberg Mannheim... 1 DZHK Partner Site Munich Heart Allian... 1 Department of Cardio Thoracic Vascula... 1 Department of Cardiology Basil Hetzel... 1 Department of Cardiology Centro Hospi... 1 Department of Cardiology Charité Camp... 1 Department of Cardiology Chiba Emerge... 1 Department of Cardiology Christchurch... 1
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Autor
Airaksinen, K E Juhani 1 Akin, Ibrahim 1 Al-Shammari, Ali 1 Banning, Adrian 1 Bauersachs, Johann 1 Bax, Jeroen J 1 Bellino, Michele 1 Beug, Daniel 1 Bianco, Matteo 1 Bilato, Claudio 1 Bossone, Eduardo 1 Braun-Dullaeus, Ruediger C 1 Bridgman, Paul 1 Bruno, Francesco 1 Budnik, Monika 1 Burgdorf, Christof 1 Böhm, Michael 1 Cammann, Victoria L 1 Carrilho-Ferreira, Pedro 1 Chan, Christina 1
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Pracoviště
1st Department of Cardiology Medical ... 1 1st Department of Medicine Faculty of... 1 Advanced Heart Failure and Transplant... 1 Berlin Institute of Health Berlin Ger... 1 CHULN Center of Cardiology of the Uni... 1 Cardiocenter 3rd Faculty of Medicine ... 1 Center for Cardiology Cardiology 1 Un... 1 Center for Molecular Cardiology Schli... 1 Centro Cardiologico Monzino IRCCS Mil... 1 Clinic for Cardiology and Pneumology ... 1 DZHK Partner Site Greifswald Greifswa... 1 DZHK Partner Site Hamburg Kiel Luebec... 1 DZHK Partner Site Heidelberg Mannheim... 1 DZHK Partner Site Munich Heart Allian... 1 Department of Cardio Thoracic Vascula... 1 Department of Cardiology Basil Hetzel... 1 Department of Cardiology Centro Hospi... 1 Department of Cardiology Charité Camp... 1 Department of Cardiology Chiba Emerge... 1 Department of Cardiology Christchurch... 1
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PubMed
37522520
DOI
10.1002/ejhf.2983
Knihovny.cz E-zdroje
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.
- MeSH
- lidé MeSH
- mortalita v nemocnicích MeSH
- prognóza MeSH
- srdeční selhání * komplikace MeSH
- strojové učení MeSH
- takotsubo kardiomyopatie * diagnóza komplikace MeSH
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- lidé MeSH
- Publikační typ
- časopisecké články MeSH
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