Statistical analysis
REGRESSION
IBM SPSS 23.0 (IBM USA) was used to process the data. According to the commonly used clinical cut-off points based on the relevant literature, some measurements in the original data were classified and transformed into count data, and the numerical values and percentages were used for statistical description. Normally distributed variables were expressed as mean ± standard deviation, and differences between groups were analyzed using the t-test. Variables with skewed distributions were expressed as medians (25th percentile, 75th percentile), and the Mann-Whitney test was used to assess differences between the two groups. Categorical variables were summarized as numbers and frequencies, and univariate analyses were performed using Fisher’s exact test or Pearson’s chi-square test. Univariate analysis was performed using the chi-square test or Fisher exact probability method to screen the relative risk factors. Multivariate logistic regression analysis (stepwise forward conditional) was used to screen the independent risk factors. All patients were randomly divided into a training group and a validation group in a 7:3 ratio. Multifactorial logistic regression analysis was conducted on the patients in the training group to construct a nomogram. The validation group was then used to assess the predictive performance of the nomogram. The “rms” package (R version 4.2.1) was used to establish the nomogram, and then bootstrap repeated sampling was used for internal verification and calibration curve drawing. To reduce overfitting bias, the nomogram model was internally verified using 1000 repeated samplings. The area under the receiver operating characteristic (ROC) curve (AUC) was used to evaluate model discrimination. Decision curve analysis (DCA) was used to quantify the net benefit under different threshold probabilities in the patient cohort, which can guide clinical decision-making. Across all tests, statistical significance was set at 