Machine learning-based prediction of in‐hospital death for patients with takotsubo syndrome : the InterTAK-ML model
Creators: name: De Filippo, Ovidio
name: Cammann, Victoria Lucia
name: Pancotti, Corrado
name: Di Vece, Davide
name: Silverio, Angelo
name: Schweiger, Victor
name: Niederseer, David
name: Szawan, Konrad Andreas
name: Würdinger, Michael
name: Koleva, Iva
name: Dusi, Veronica
name: Bellino, Michele
name: Vecchione, Carmine
name: Parodi, Guido
name: Bossone, Eduardo
name: Gili, Sebastiano
name: Neuhaus, Michael
name: Franke, Jennifer
name: Meder, Benjamin
name: Jaguszewski, Milosz
name: Noutsias, Michel
name: Knorr, Maike Christina
name: Jansen, Thomas
name: Dichtl, Wolfgang
name: Lewinski, Dirk
name: Burgdorf, Christof
name: Kherad, Behrouz
name: Tschöpe, Carsten
name: Sarcon, Annahita
name: Shinbane, Jerold
name: Rajan, Lawrence
name: Michels, Guido
name: Pfister, Roman
name: Cuneo, Alessandro
name: Jacobshagen, Claudius
name: Karakas, Mahir
name: Koenig, Wolfgang
name: Pott, Alexander
name: Meyer, Philippe
name: Roffi, Marco
name: Banning, Adrian
name: Wolfrum, Mathias
name: Cuculi, Florim
name: Kobza, Richard
name: Fischer, Thomas A.
name: Vasankari, Tuija
name: Airaksinen, K. E. Juhani
name: Napp, Christian
name: Dworakowski, Rafal
name: MacCarthy, Philip
name: Kaiser, Christoph A.
name: Osswald, Stefan
name: Galiuto, Leonarda
name: Chan, Christina
name: Bridgman, Paul
name: Beug, Daniel
name: Delmas, Clément
name: Lairez, Olivier
name: Gilyarova, Ekaterina
name: Shilova, Alexandra
name: Gilyarov, Mikhail
name: El-Battrawy, Ibrahim
name: Akın, Ibrahim
name: Poledniková, Karolina
name: Toušek, Petr
name: Winchester, David E.
name: Massoomi, Michael
name: Galuszka, Jan
name: Ukena, Christian
name: Poglajen, Gregor
name: Carrilho-Ferreira, Pedro
name: Hauck, Christian
name: Paolini, Carla
name: Bilato, Claudio
name: Kobayashi, Yoshio
name: Kato, Ken
name: Ishibashi, Iwao
name: Himi, Toshiharu
name: Din, Jehangir
name: Al-Shammari, Ali
name: Prasad, Abhiram
name: Rihal, Charanjit S.
name: Liu, Kan
name: Schulze, Paul Christian
name: Bianco, Matteo
name: Jörg, Lucas
name: Rickli, Hans
name: Pestana, Gonçalo
name: Nguyen, Thanh H.
name: Böhm, Michael
name: Maier, Lars Siegfried
name: Pinto, Fausto J.
name: Widimský, Petr
name: Felix, Stephan
name: Braun-Dullaeus, Ruediger C.
name: Rottbauer, Wolfgang
name: Hasenfuß, Gerd
name: Pieske, Burkert M.
name: Schunkert, Heribert
name: Budnik, Monika
name: Opolski, Grzegorz
name: Thiele, Holger
name: Bauersachs, Johann
name: Horowitz, John D.
name: Di Mario, Carlo
name: Bruno, Francesco
name: Kong, William
name: Dalakoti, Mayank
name: Imori, Yoichi
name: Münzel, Thomas
name: Crea, Filippo
name: Lüscher, Thomas F.
name: Bax, Jeroen J.
name: Ruschitzka, Frank
name: De Ferrari, Gaetano Maria
name: Fariselli, Piero
name: Templin-Ghadri, Jelena-Rima
name: Citro, Rodolfo
name: D'Ascenzo, Fabrizio
name: Templin, Christian
Publisher: name: Universitäts- und Landesbibliothek Sachsen-Anhalt
Year: 2023
Subjects: subject: 610

description: 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.