Univariate and multivariate logistic regression analyses were used to assess demographic and clinicalcharacteristics of patients in the training set, screen differential factors, and construct a prediction model. Afive⁃fold cross⁃validation method was used for internal validation after construction of the prediction model.
The discrimination（area under the curve［AUC］）, calibration（Hosmer ⁃ Lemeshow test）and accuracy
（sensitivity, specificity, positive predictive value, and negative predictive value）of the prediction modelwere evaluated in the test set. Results A total of 107 cases in the comorbidity group and 428 cases in thecontrol group were successfully matched. The training set included 430 cases, and the test set included 105 cases. Based on multivariate logistic regression results, a total of 6 factors were included in the prediction model, including course of vitiligo（odds ratio［OR］= 1.04, 95% confidence interval［CI］: 1.02 - 1.07, P＜ 0.001）
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