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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. and Balzer, F. and Haufe, S. and Giesa, N. and Schiefenhövel, F. and Schmieding, M. and Jurth, C. and Kopp, W. and Akalin, A. and Schaller, S.J. and Weber-Carstens, S. and 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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