Your browser doesn't support javascript.
Predicting lethal courses in critically ill COVID-19 patients using a machine learning model trained on patients with non-COVID-19 viral pneumonia.
Lichtner, Gregor; Balzer, Felix; Haufe, Stefan; Giesa, Niklas; Schiefenhövel, Fridtjof; Schmieding, Malte; Jurth, Carlo; Kopp, Wolfgang; Akalin, Altuna; Schaller, Stefan J; Weber-Carstens, Steffen; Spies, Claudia; von Dincklage, Falk.
  • Lichtner G; Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Department of Anesthesiology and Operative Intensive Care Medicine (CCM, CVK), Charitéplatz 1, 10117, Berlin, Germany.
  • Balzer F; Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Institute of Medical Informatics, Berlin, Germany.
  • Haufe S; Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Department of Anesthesiology and Operative Intensive Care Medicine (CCM, CVK), Charitéplatz 1, 10117, Berlin, Germany.
  • Giesa N; Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Institute of Medical Informatics, Berlin, Germany.
  • Schiefenhövel F; Einstein Center Digital Future, Berlin, Germany.
  • Schmieding M; Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Klinik für Neurologie mit Experimenteller Neurologie, Berlin, Germany.
  • Jurth C; Physikalisch-Technische Bundesanstalt Braunschweig und Berlin, Department of Mathematical Modelling and Data Analysis, Berlin, Germany.
  • Kopp W; Technische Universität Berlin, Uncertainty, Inverse Modeling and Machine Learning Group, Berlin, Germany.
  • Akalin A; Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Institute of Medical Informatics, Berlin, Germany.
  • Schaller SJ; Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Department of Anesthesiology and Operative Intensive Care Medicine (CCM, CVK), Charitéplatz 1, 10117, Berlin, Germany.
  • Weber-Carstens S; Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Institute of Medical Informatics, Berlin, Germany.
  • Spies C; Einstein Center Digital Future, Berlin, Germany.
  • von Dincklage F; Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Department of Anesthesiology and Operative Intensive Care Medicine (CCM, CVK), Charitéplatz 1, 10117, Berlin, Germany.
Sci Rep ; 11(1): 13205, 2021 06 24.
Article in English | MEDLINE | ID: covidwho-1281734
ABSTRACT
In a pandemic with a novel disease, disease-specific prognosis models are available only with a delay. To bridge the critical early phase, models built for similar diseases might be applied. To test the accuracy of such a knowledge transfer, we investigated how precise lethal courses in critically ill COVID-19 patients can be predicted by a model trained on critically ill non-COVID-19 viral pneumonia patients. We trained gradient boosted decision tree models on 718 (245 deceased) non-COVID-19 viral pneumonia patients to predict individual ICU mortality and applied it to 1054 (369 deceased) COVID-19 patients. Our model showed a significantly better predictive performance (AUROC 0.86 [95% CI 0.86-0.87]) than the clinical scores APACHE2 (0.63 [95% CI 0.61-0.65]), SAPS2 (0.72 [95% CI 0.71-0.74]) and SOFA (0.76 [95% CI 0.75-0.77]), the COVID-19-specific mortality prediction models of Zhou (0.76 [95% CI 0.73-0.78]) and Wang (laboratory 0.62 [95% CI 0.59-0.65]; clinical 0.56 [95% CI 0.55-0.58]) and the 4C COVID-19 Mortality score (0.71 [95% CI 0.70-0.72]). We conclude that lethal courses in critically ill COVID-19 patients can be predicted by a machine learning model trained on non-COVID-19 patients. Our results suggest that in a pandemic with a novel disease, prognosis models built for similar diseases can be applied, even when the diseases differ in time courses and in rates of critical and lethal courses.
Subject(s)

Full text: Available Collection: International databases Database: MEDLINE Main subject: Machine Learning / COVID-19 / Models, Theoretical Type of study: Diagnostic study / Observational study / Prognostic study Limits: Aged / Female / Humans / Male / Middle aged Language: English Journal: Sci Rep Year: 2021 Document Type: Article Affiliation country: S41598-021-92475-7

Similar

MEDLINE

...
LILACS

LIS


Full text: Available Collection: International databases Database: MEDLINE Main subject: Machine Learning / COVID-19 / Models, Theoretical Type of study: Diagnostic study / Observational study / Prognostic study Limits: Aged / Female / Humans / Male / Middle aged Language: English Journal: Sci Rep Year: 2021 Document Type: Article Affiliation country: S41598-021-92475-7