Development and validation of prognosis model of mortality risk in patients with COVID-19.
Epidemiol Infect
; 148: e168, 2020 08 04.
Article
in English
| MEDLINE | ID: covidwho-1537262
ABSTRACT
This study aimed to identify clinical features for prognosing mortality risk using machine-learning methods in patients with coronavirus disease 2019 (COVID-19). A retrospective study of the inpatients with COVID-19 admitted from 15 January to 15 March 2020 in Wuhan is reported. The data of symptoms, comorbidity, demographic, vital sign, CT scans results and laboratory test results on admission were collected. Machine-learning methods (Random Forest and XGboost) were used to rank clinical features for mortality risk. Multivariate logistic regression models were applied to identify clinical features with statistical significance. The predictors of mortality were lactate dehydrogenase (LDH), C-reactive protein (CRP) and age based on 500 bootstrapped samples. A multivariate logistic regression model was formed to predict mortality 292 in-sample patients with area under the receiver operating characteristics (AUROC) of 0.9521, which was better than CURB-65 (AUROC of 0.8501) and the machine-learning-based model (AUROC of 0.4530). An out-sample data set of 13 patients was further tested to show our model (AUROC of 0.6061) was also better than CURB-65 (AUROC of 0.4608) and the machine-learning-based model (AUROC of 0.2292). LDH, CRP and age can be used to identify severe patients with COVID-19 on hospital admission.
Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Pneumonia, Viral
/
Logistic Models
/
Coronavirus Infections
/
Machine Learning
Type of study:
Observational study
/
Prognostic study
/
Randomized controlled trials
Limits:
Adolescent
/
Adult
/
Aged
/
Female
/
Humans
/
Male
/
Middle aged
/
Young adult
Country/Region as subject:
Asia
Language:
English
Journal:
Epidemiol Infect
Journal subject:
Communicable Diseases
/
Epidemiology
Year:
2020
Document Type:
Article
Affiliation country:
S0950268820001727
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