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.