This study collected and analyzed clinical data of psoriasis patients to develop and validate a psoriasis relapse risk prediction model. It aims to support early relapse risk assessment in clinical practice and inform the design of preventive interventions. To develop and validate a risk prediction model for psoriasis relapse. A convenience sampling method was used to select 504 psoriasis patients admitted to a tertiary hospital in China between January 2022 and December 2024, including 353 cases in the training set and 151 cases in the testing set. Independent risk factors for psoriasis relapse were identified through univariate analysis and logistic regression analysis to develop a prediction model. A nomogram and SHAP summary plot were generated for model visualization, and the model's goodness of fit and discriminative ability were evaluated. The 1-year relapse rate of psoriasis patients after treatment was 66.67%. Logistic regression identified six independent risk factors for psoriasis relapse: BMI, diabetes, biologic use, smoking, upper respiratory tract infection (URTI), and non-standard medication, all of which were incorporated into the model. The area under the ROC curve (AUC) values for the training and testing sets were 0.767 [95% CI 0.715-0.818] and 0.704 [95% CI 0.620-0.789], respectively. The model showed moderate discrimination and good calibration. Decision curve analysis (DCA) confirmed clinically meaningful net benefit in both training and test sets. The predictive model for psoriasis relapse risk established in this study demonstrated only moderate predictive performance. This model can serve as a preliminary exploratory tool, providing a certain degree of quantitative reference for assessing the risk of psoriasis relapse; however, rigorous external validation in independent multicenter cohorts is still required before clinical application.
