Analysis of factors affecting the prognosis of COVID-19 patients and viral shedding duration

The clinical characteristics of patients with COVID-19 were analysed to determine the factors influencing the prognosis and virus shedding time to facilitate early detection of disease progression. Logistic regression analysis was used to explore the relationships among prognosis, clinical character...

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Bibliographic Details
Published in:Epidemiology & Infection 2020-01, Vol.148, p.e125, Article e125
Main Authors: Han, Jing, Shi, Li-xia, Xie, Yi, Zhang, Yong-jin, Huang, Shu-ping, Li, Jian-guo, Wang, He-rong, Shao, Shi-feng
Format: Article
Language:English
Subjects:
Adult
Analysis of Variance
Betacoronavirus - physiology
Calibration
Cardiovascular disease
China
Coronavirus Infections - diagnosis
Coronavirus Infections - mortality
Coronavirus Infections - therapy
Coronavirus Infections - virology
Coronaviruses
COVID-19
Diabetes mellitus
Disease Progression
Female
Fever
Health services
Humans
Hypoproteinemia
Kaplan-Meier Estimate
Logistic Models
Male
Medical prognosis
Medical treatment
Middle Aged
Mortality
Original Paper
Pandemics
Patients
Pneumonia, Viral - diagnosis
Pneumonia, Viral - mortality
Pneumonia, Viral - therapy
Pneumonia, Viral - virology
Prognosis
Proportional Hazards Models
Regression analysis
Retrospective Studies
Risk Factors
ROC Curve
SARS-CoV-2
Statistical models
Time Factors
Viral diseases
Virus Shedding - physiology
Viruses
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Summary:The clinical characteristics of patients with COVID-19 were analysed to determine the factors influencing the prognosis and virus shedding time to facilitate early detection of disease progression. Logistic regression analysis was used to explore the relationships among prognosis, clinical characteristics and laboratory indexes. The predictive value of this model was assessed with receiver operating characteristic curve analysis, calibration and internal validation. The viral shedding duration was calculated using the Kaplan–Meier method, and the prognostic factors were analysed by univariate log-rank analysis and the Cox proportional hazards model. A retrospective study was carried out with patients with COVID-19 in Tianjin, China. A total of 185 patients were included, 27 (14.59%) of whom were severely ill at the time of discharge and three (1.6%) of whom died. Our findings demonstrate that patients with an advanced age, diabetes, a low PaO2/FiO2 value and delayed treatment should be carefully monitored for disease progression to reduce the incidence of severe disease. Hypoproteinaemia and the fever duration warrant special attention. Timely interventions in symptomatic patients and a time from symptom onset to treatment
ISSN:0950-2688
1469-4409
1469-4409
DOI:10.1017/S0950268820001399