Vertaisarvioitu alkuperäisartikkeli tai data-artikkeli tieteellisessä aikakauslehdessä (A1)
Machine learning-based prediction of in-hospital death for patients with takotsubo syndrome: The InterTAK-ML model
Julkaisun tekijät: 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
Kustantaja: Wiley
Julkaisuvuosi: 2023
Journal: European Journal of Heart Failure
Tietokannassa oleva lehden nimi: European journal of heart failure
Lehden akronyymi: Eur J Heart Fail
ISSN: 1388-9842
eISSN: 1879-0844
DOI: http://dx.doi.org/10.1002/ejhf.2983
Verkko-osoite: https://doi.org/10.1002/ejhf.2983
Rinnakkaistallenteen osoite: https://research.utu.fi/converis/portal/detail/Publication/181102331
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.
Ladattava julkaisu This is an electronic reprint of the original article. |  |
