Statistical analysis
REGRESSION, EVENTS
The ‘‘createDataPartition’’ function of the caret software package was used to group patients into the training and validation cohorts at a ratio of 7:3, so that the outcome events were randomly distributed in the two cohorts. In order to prevent over-fitting of the model, most of the data were used to train the model to ensure its accuracy, while a small part of the data were used for validation. Variables were described in the training dataset and validation dataset, respectively. Categorical variables were described as percentiles (%); continuous variables of non-normal distribution were displayed as medians and quartiles, and continuous variables of normal distribution were expressed as mean and standard deviation (mean (S.E.)). The chi-square test was used to compare the differences between categorical variables, and the t-test or nonparametric test was used to compare the differences between two groups of continuous variables. In the training cohort, LASSO regression and stepwise Logistic regression based on AIC (Akaike Information Criterion) were used for feature selection. Statistically significant variables (P < 0.05) were identified as independent risk factors and were included in the final logistic regression model, and a corresponding nomogram was plotted. The area under the ROC curve (AUC) was used to assess the prediction accuracy of the model; calibration curve was used to assess the consistency between the predicted value of the model and the actual value, and decision curve was used to analyze the clinical benefits of the model. Tableone software package was used for data description; glmnet software package was used for LASSO regression analysis; rms software package was used for plotting the nomogram and calibration curve, and pROC software package was used for plotting ROC curve. R 4.2.1 (