Titel: |
Predicting lethal courses in critically ill COVID-19 patients using a machine learning model trained on patients with non-COVID-19 viral pneumonia |
Autor(en): |
G. Lichtner, F. Balzer, S. Haufe, N. Giesa, F. Schiefenhövel, M. Schmieding, C. Jurth, W. Kopp, A. Akalin, S. J. Schaller, S. Weber-Carstens, C. Spies and F. von Dincklage |
Journal: |
Scientific Reports |
Jahr: |
2021 |
Band: |
11 |
Ausgabe: |
1 |
Seite(n): |
13205 |
DOI: |
10.1038/s41598-021-92475-7 |
ISSN: |
2045-2322 |
Web URL: |
http://www.nature.com/articles/s41598-021-92475-7 |
Marker: |
8.4, 8.44 |
Zusammenfassung: |
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. |