Machine-learning based prediction of in-hospital death for patients with takotsubo syndrome: the InterTAK-ML model
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Machine-learning based prediction of in-hospital death for patients with takotsubo syndrome: the InterTAK-ML model. / De Filippo, Ovidio; Cammann, Victoria L; Pancotti, Corrado; Di Vece, Davide; Silverio, Angelo; Schweiger, Victor; Niederseer, David; Szawan, Konrad A; Würdinger, Michael; Koleva, Iva; Dusi, Veronica; Bellino, Michele; Vecchione, Carmine; Parodi, Guido; Bossone, Eduardo; Gili, Sebastiano; Neuhaus, Michael; Franke, Jennifer; Meder, Benjamin; Jaguszewski, Miłosz; Noutsias, Michel; Knorr, Maike; Jansen, Thomas; Dichtl, Wolfgang; von Lewinski, Dirk; Burgdorf, Christof; Kherad, Behrouz; Tschöpe, Carsten; Sarcon, Annahita; Shinbane, Jerold; Rajan, Lawrence; Michels, Guido; Pfister, Roman; Cuneo, Alessandro; Jacobshagen, Claudius; Karakas, Mahir; Koenig, Wolfgang; Pott, Alexander; Meyer, Philippe; Roffi, Marco; Banning, Adrian; Wolfrum, Mathias; Cuculi, Florim; Kobza, Richard; Fischer, Thomas A; Vasankari, Tuija; Airaksinen, K E Juhani; Napp, L Christian; Dworakowski, Rafal; MacCarthy, Philip; Kaiser, Christoph; Osswald, Stefan; Galiuto, Leonarda; Chan, Christina; Bridgman, Paul; Beug, Daniel; Delmas, Clément; Lairez, Olivier; Gilyarova, Ekaterina; Shilova, Alexandra; Gilyarov, Mikhail; El-Battrawy, Ibrahim; Akin, Ibrahim; Poledniková, Karolina; Toušek, Petr; Winchester, David E; Massoomi, Michael; Galuszka, Jan; Ukena, Christian; Poglajen, Gregor; Carrilho-Ferreira, Pedro; Hauck, Christian; Paolini, Carla; Bilato, Claudio; Kobayashi, Yoshio; Kato, Ken; Ishibashi, Iwao; Himi, Toshiharu; Din, Jehangir; Al-Shammari, Ali; Prasad, Abhiram; Rihal, Charanjit S; Liu, Kan; Schulze, P Christian; Bianco, Matteo; Jörg, Lucas; Rickli, Hans; Pestana, Gonçalo; Nguyen, Thanh H; Böhm, Michael; Maier, Lars S; Pinto, Fausto J; Widimský, Petr; Felix, Stephan B; Braun-Dullaeus, Ruediger C; Rottbauer, Wolfgang; Hasenfuß, Gerd; Pieske, Burkert M; Schunkert, Heribert; Budnik, Monika; Opolski, Grzegorz; Thiele, Holger; Bauersachs, Johann; Horowitz, John D; Di Mario, Carlo; Bruno, Francesco; Kong, William; Dalakoti, Mayank; Imori, Yoichi; Münzel, Thomas; Crea, Filippo; Lüscher, Thomas F; Bax, Jeroen J; Ruschitzka, Frank; de Ferrari, Gaetano Maria; Fariselli, Piero; Ghadri, Jelena R; Citro, Rodolfo; D'Ascenzo, Fabrizio; Templin, Christian; InterTAK-ML.
in: EUR J HEART FAIL, Jahrgang 25, Nr. 12, 12.2023, S. 2299-2311.Publikationen: SCORING: Beitrag in Fachzeitschrift/Zeitung › SCORING: Zeitschriftenaufsatz › Forschung › Begutachtung
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TY - JOUR
T1 - Machine-learning based prediction of in-hospital death for patients with takotsubo syndrome: the InterTAK-ML model
AU - De Filippo, Ovidio
AU - Cammann, Victoria L
AU - Pancotti, Corrado
AU - Di Vece, Davide
AU - Silverio, Angelo
AU - Schweiger, Victor
AU - Niederseer, David
AU - Szawan, Konrad A
AU - Würdinger, Michael
AU - Koleva, Iva
AU - Dusi, Veronica
AU - Bellino, Michele
AU - Vecchione, Carmine
AU - Parodi, Guido
AU - Bossone, Eduardo
AU - Gili, Sebastiano
AU - Neuhaus, Michael
AU - Franke, Jennifer
AU - Meder, Benjamin
AU - Jaguszewski, Miłosz
AU - Noutsias, Michel
AU - Knorr, Maike
AU - Jansen, Thomas
AU - Dichtl, Wolfgang
AU - von Lewinski, Dirk
AU - Burgdorf, Christof
AU - Kherad, Behrouz
AU - Tschöpe, Carsten
AU - Sarcon, Annahita
AU - Shinbane, Jerold
AU - Rajan, Lawrence
AU - Michels, Guido
AU - Pfister, Roman
AU - Cuneo, Alessandro
AU - Jacobshagen, Claudius
AU - Karakas, Mahir
AU - Koenig, Wolfgang
AU - Pott, Alexander
AU - Meyer, Philippe
AU - Roffi, Marco
AU - Banning, Adrian
AU - Wolfrum, Mathias
AU - Cuculi, Florim
AU - Kobza, Richard
AU - Fischer, Thomas A
AU - Vasankari, Tuija
AU - Airaksinen, K E Juhani
AU - Napp, L Christian
AU - Dworakowski, Rafal
AU - MacCarthy, Philip
AU - Kaiser, Christoph
AU - Osswald, Stefan
AU - Galiuto, Leonarda
AU - Chan, Christina
AU - Bridgman, Paul
AU - Beug, Daniel
AU - Delmas, Clément
AU - Lairez, Olivier
AU - Gilyarova, Ekaterina
AU - Shilova, Alexandra
AU - Gilyarov, Mikhail
AU - El-Battrawy, Ibrahim
AU - Akin, Ibrahim
AU - Poledniková, Karolina
AU - Toušek, Petr
AU - Winchester, David E
AU - Massoomi, Michael
AU - Galuszka, Jan
AU - Ukena, Christian
AU - Poglajen, Gregor
AU - Carrilho-Ferreira, Pedro
AU - Hauck, Christian
AU - Paolini, Carla
AU - Bilato, Claudio
AU - Kobayashi, Yoshio
AU - Kato, Ken
AU - Ishibashi, Iwao
AU - Himi, Toshiharu
AU - Din, Jehangir
AU - Al-Shammari, Ali
AU - Prasad, Abhiram
AU - Rihal, Charanjit S
AU - Liu, Kan
AU - Schulze, P Christian
AU - Bianco, Matteo
AU - Jörg, Lucas
AU - Rickli, Hans
AU - Pestana, Gonçalo
AU - Nguyen, Thanh H
AU - Böhm, Michael
AU - Maier, Lars S
AU - Pinto, Fausto J
AU - Widimský, Petr
AU - Felix, Stephan B
AU - Braun-Dullaeus, Ruediger C
AU - Rottbauer, Wolfgang
AU - Hasenfuß, Gerd
AU - Pieske, Burkert M
AU - Schunkert, Heribert
AU - Budnik, Monika
AU - Opolski, Grzegorz
AU - Thiele, Holger
AU - Bauersachs, Johann
AU - Horowitz, John D
AU - Di Mario, Carlo
AU - Bruno, Francesco
AU - Kong, William
AU - Dalakoti, Mayank
AU - Imori, Yoichi
AU - Münzel, Thomas
AU - Crea, Filippo
AU - Lüscher, Thomas F
AU - Bax, Jeroen J
AU - Ruschitzka, Frank
AU - de Ferrari, Gaetano Maria
AU - Fariselli, Piero
AU - Ghadri, Jelena R
AU - Citro, Rodolfo
AU - D'Ascenzo, Fabrizio
AU - Templin, Christian
AU - InterTAK-ML
N1 - This article is protected by copyright. All rights reserved.
PY - 2023/12
Y1 - 2023/12
N2 - 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.
AB - 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.
U2 - 10.1002/ejhf.2983
DO - 10.1002/ejhf.2983
M3 - SCORING: Journal article
C2 - 37522520
VL - 25
SP - 2299
EP - 2311
JO - EUR J HEART FAIL
JF - EUR J HEART FAIL
SN - 1388-9842
IS - 12
ER -