Predicting lethal courses in critically ill COVID-19 patients using a machine learning model trained on patients with non-COVID-19 viral pneumonia.
Sci Rep
; 11(1): 13205, 2021 06 24.
Article
in English
| MEDLINE | ID: covidwho-1281734
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.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Machine Learning
/
COVID-19
/
Models, Theoretical
Type of study:
Diagnostic study
/
Observational study
/
Prognostic study
Limits:
Aged
/
Female
/
Humans
/
Male
/
Middle aged
Language:
English
Journal:
Sci Rep
Year:
2021
Document Type:
Article
Affiliation country:
S41598-021-92475-7
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