Predicting lethal courses in critically ill COVID-19 patients using a machine learning model trained on patients with non-COVID-19 viral pneumonia
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 cri...
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2021
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oai:doaj.org-article:9f0925fd5b874cefbef161561d71441b2021-12-02T18:02:44ZPredicting lethal courses in critically ill COVID-19 patients using a machine learning model trained on patients with non-COVID-19 viral pneumonia10.1038/s41598-021-92475-72045-2322https://doaj.org/article/9f0925fd5b874cefbef161561d71441b2021-06-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-92475-7https://doaj.org/toc/2045-2322Abstract 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.Gregor LichtnerFelix BalzerStefan HaufeNiklas GiesaFridtjof SchiefenhövelMalte SchmiedingCarlo JurthWolfgang KoppAltuna AkalinStefan J. SchallerSteffen Weber-CarstensClaudia SpiesFalk von DincklageNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-10 (2021) |
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DOAJ |
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| topic |
Medicine R Science Q |
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Medicine R Science Q Gregor Lichtner Felix Balzer Stefan Haufe Niklas Giesa Fridtjof Schiefenhövel Malte Schmieding Carlo Jurth Wolfgang Kopp Altuna Akalin Stefan J. Schaller Steffen Weber-Carstens Claudia Spies Falk von Dincklage Predicting lethal courses in critically ill COVID-19 patients using a machine learning model trained on patients with non-COVID-19 viral pneumonia |
| description |
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. |
| format |
article |
| author |
Gregor Lichtner Felix Balzer Stefan Haufe Niklas Giesa Fridtjof Schiefenhövel Malte Schmieding Carlo Jurth Wolfgang Kopp Altuna Akalin Stefan J. Schaller Steffen Weber-Carstens Claudia Spies Falk von Dincklage |
| author_facet |
Gregor Lichtner Felix Balzer Stefan Haufe Niklas Giesa Fridtjof Schiefenhövel Malte Schmieding Carlo Jurth Wolfgang Kopp Altuna Akalin Stefan J. Schaller Steffen Weber-Carstens Claudia Spies Falk von Dincklage |
| author_sort |
Gregor Lichtner |
| title |
Predicting lethal courses in critically ill COVID-19 patients using a machine learning model trained on patients with non-COVID-19 viral pneumonia |
| title_short |
Predicting lethal courses in critically ill COVID-19 patients using a machine learning model trained on patients with non-COVID-19 viral pneumonia |
| title_full |
Predicting lethal courses in critically ill COVID-19 patients using a machine learning model trained on patients with non-COVID-19 viral pneumonia |
| title_fullStr |
Predicting lethal courses in critically ill COVID-19 patients using a machine learning model trained on patients with non-COVID-19 viral pneumonia |
| title_full_unstemmed |
Predicting lethal courses in critically ill COVID-19 patients using a machine learning model trained on patients with non-COVID-19 viral pneumonia |
| title_sort |
predicting lethal courses in critically ill covid-19 patients using a machine learning model trained on patients with non-covid-19 viral pneumonia |
| publisher |
Nature Portfolio |
| publishDate |
2021 |
| url |
https://doaj.org/article/9f0925fd5b874cefbef161561d71441b |
| work_keys_str_mv |
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