Data analysis
According to the NHC definition, the severity of symptoms of COVID-19 was divided into mild-group and severe-group. Univariate analysis was used to select potential risk factors, and variables were significant atP < 0.1 in the univariate analyses. Continuous variables were presented as mean ± standard deviation (SD) (normal distribution) or median and interquartile range (IQR) (non-normal distribution), respectively. The independent t -test (normal distribution) or Mann-Whitney U test (non-normal distribution) were employed to compare the mild-group and the severe-group. Categorical variables were described as number (percentage) and analyzed using the χ2 test or Fisher’s exact test.
The significant risk factors (P < 0.1) were used to construct the risk model using multivariable logistic regression analysis. The area under the receiver operating characteristic (ROC) curve was used to measure the discriminatory power of the multivariable logistic regression model. The Hosmer-Lemeshow goodness-of-fit test was used to evaluate whether the number of expected events from the logistic regression model reflects the number of observed events in the data. To facilitate clinical usage, a risk score was developed according to the coefficients of the multivariable logistic regression model (Pan et al., 2020). All data analyses were performed using SPSS version 17.0 (IBM, NY, USA). A two-sided P < 0.05 was considered significant.