Development and validation of prognosis model of mortality risk in patients with COVID-19.
Epidemiol Infect
; 148: e168, 2020 08 04.
Artigo
em Inglês
| 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.
Palavras-chave
Texto completo:
Disponível
Coleções:
Bases de dados internacionais
Base de dados:
MEDLINE
Assunto principal:
Pneumonia Viral
/
Modelos Logísticos
/
Infecções por Coronavirus
/
Aprendizado de Máquina
Tipo de estudo:
Estudo observacional
/
Estudo prognóstico
/
Ensaios controlados aleatorizados
Limite:
Adolescente
/
Adulto
/
Idoso
/
Feminino
/
Humanos
/
Masculino
/
Meia-Idade
/
Jovem adulto
País/Região como assunto:
Ásia
Idioma:
Inglês
Revista:
Epidemiol Infect
Assunto da revista:
Doenças Transmissíveis
/
Epidemiologia
Ano de publicação:
2020
Tipo de documento:
Artigo
País de afiliação:
S0950268820001727
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