Statistical analysis
cirrhosis
REGRESSION, CIRRHOSIS
MedCalc software (version 20.1.0), IBM SPSS Statistics (version 22.0), and R software (version 4.1.2) were used for statistical analyses. The study comprised 274 patients, with 183 randomly allocated to the training cohort to generate the prediction model and the remaining 91 patients assigned to the validation group to test model performance. Quantitative data that were normally distributed were subjected to the unpaired t-test, whereas non-normally distributed data were subjected to Mann–Whitney U tests. The comparisons between the categories of categorical variables were analyzed using the Chi-square test.For the training cohort, univariate logistic regression analysis was used to find putative cirrhosis predictors. The multivariate regression model only contained variables that had a P value 0.05 in the univariate analysis. An independent predictor-based nomogram was created in the R software, utilizing the rms library. Utilizing the area under the receiver operating characteristic curve (AUROC), the discriminatory performance of the developed models was compared. Model calibration was performed by plotting calibration curves of true probabilities against model predicted probabilities. To determine net benefits, decision curve analysis (DCA) was performed. The threshold for statistical significance was set at P < 0.05.