2.5 Logistic regression analysis
Potential risk factors for non-survived COVID-19 patients (n = 49),
compared to survived severe patients (n = 78), were analyzed by a
multivariate binary logistic model using Forward Stepwise (Wald) model
method. Missing values of laboratory data for the logistic regression
analysis, including affected lobe number(s), CRP, PCT and D-dimer, were
replaced via multiple imputation. Cut-off value of
neutrophil-to-lymphocyte ratio (NLR = 7.726) was calculated via ROC
analysis, with an AUC of 0.6614, and NLR was analyzed as categorical
variables for the logistic regression analysis. All variables were
subject to univariate logistic regression, and odds ratios (ORs) were
calculated between non-survived and survived severe groups, with 95%
confidence intervals generated. Variables were included in binary
logistic regression if corresponding p value was less than 0.05.
The binary logistic regression analysis was employed to conclude a
multivariate model to conclude the risk factors of death among
critically ill patients.
Univariate analysis and multivariate regression analysis were performed
by SPSS software (version 26.0, IBM), and R software (version 3.4.3,
supported by R Foundation for Statistical Computing).