Statistical analysis

The study was statistically analyzed in its entirety using R software. The confirmed cases were divided into 2 groups, the training set and the validation set, in a ratio of 7:3 to make sure the outcome event (dead or alive) was randomly distributed between the 2 data groups. A chi-square test was used for comparing the baseline data between the 2 groups (in Table ​Table11 for details). For the training group data, univariate analysis was employed, and factors with P < 0.05 were further included in multifactor Cox regression analysis to screen out the factor with P < 0.05 as predictors. Based on the predictors, R called the ‘rms’ package to construct the COX proportional risk model function and applied the Nomogram tool to build the survival prediction model. Internal and external validation were performed using C-index, calibration curve, receiver operating characteristic curve and decision curve analysis (DCA) in the training set and the validation set, separately, to test the distinction and consistency of this model. All statistical tests were bilateral, and P-value (<0.05) was statistically significant.

Characteristics of the training set and validation set