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@Article { lichtner_predicting_2021,
   title = {Predicting lethal courses in critically ill {COVID}-19 patients using a machine learning model trained on patients with non-{COVID}-19 viral pneumonia},
   journal = {Scientific Reports},
   year = {2021},
   volume = {11},
   number = {1},
   pages = {13205},
   abstract = {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.},
   web_url = {http://www.nature.com/articles/s41598-021-92475-7},
   web_url_date = {2021-10-14},
   language = {en},
   ISSN = {2045-2322},
   DOI = {10.1038/s41598-021-92475-7},
   author = {Lichtner, Gregor and Balzer, Felix and Haufe, Stefan and Giesa, Niklas and Schiefenh{\"o}vel, Fridtjof and Schmieding, Malte and Jurth, Carlo and Kopp, Wolfgang and Akalin, Altuna and Schaller, Stefan J. and Weber-Carstens, Steffen and Spies, Claudia and von Dincklage, Falk}
}