2.4 Statistical analysis
Demographics and baseline characteristics of subjects were presented as mean ± standard deviation for continuous data with normal distribution and median (IQR) for continuous data with skewed variables, and compared by Student’s t -test and Mann-Whitney U test, respectively. Categorical data were presented as percentages and compared using the Chi-square or Fisher’s exact test, as appropriate. Odds ratios (ORs) and 95% confidence intervals (95% CI) were calculated in unadjusted and multivariate-adjusted logistic regression model analyses. Stepwise multivariate logistic regression analysis was used to explore the independent influencing factors of osteoporosis and ED. Least absolute shrinkage and selection operator (LASSO) logistic regression, which is suitable for the regression of high-dimensional data, was used to select important preoperative indices for predicting postoperative hypokalemia. LASSO logistic regression analysis was performed using the glmnet R package. Receive operating characteristic (ROC) analysis was performed to assess the accuracy of the different models for osteoporosis. All statistical assessments were performed using RStudio statistical programming language (version 3.1.6). Two-tailed P<0.05 were considered indicative of statistical significance.