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Predicting lethal courses in critically ill COVID-19 patients using a machine learning model trained on patients with non-COVID-19 viral pneumonia

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Item Type:Article
Title:Predicting lethal courses in critically ill COVID-19 patients using a machine learning model trained on patients with non-COVID-19 viral pneumonia
Creators Name:Lichtner, G., Balzer, F., Haufe, S., Giesa, N., Schiefenhövel, F., Schmieding, M., Jurth, C., Kopp, W., Akalin, A., Schaller, S.J., Weber-Carstens, S., Spies, C. and von Dincklage, F.
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.
Keywords:COVID-19, Critical Illness, Machine Learning, Prognosis, Retrospective Studies, Risk Factors, Theoretical Models
Source:Scientific Reports
ISSN:2045-2322
Publisher:Nature Publishing Group
Volume:11
Number:1
Page Range:13205
Date:24 June 2021
Official Publication:https://doi.org/10.1038/s41598-021-92475-7
PubMed:View item in PubMed

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