Abstract: Objective: To investigate risk factors for the development of psoriatic arthritis (PsA) in patients with psoriasis (PsO) and to construct a predictive model. Methods: Data from PsO patients in the National Health and Nutrition Examination Survey (NHANES) were utilized as the training set (n=328), while data from Chinese hospital-based PsO patients served as the validation set (n=306). Predictor variables were screened using univariate analysis and multivariate backward stepwise regression. The model's discriminatory power, predictive accuracy, and clinical utility were assessed via receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). A nomogram was developed and subjected to rationality analysis. Additionally, four machine learning models were employed to evaluate applicability and overall performance. Results: Significant differences (P＜0.05) were observed between PsA and non-PsA patients in the training set regarding age, sex, mean systolic blood pressure, prevalence of hypertension and chronic kidney disease, urine protein, and glycated hemoglobin. Nine variables were ultimately incorporated into the final model: age, sex, hypertension, mean systolic blood pressure, neutrophil count, aspartate aminotransferase, glucose, total cholesterol, and high-density lipoprotein cholesterol. The model demonstrated consistent discriminatory ability between the training set (AUC=0.741) and the validation set (AUC=0.694), albeit with observed bias in predictive probability and a constrained range of clinical net benefit. The nomogram's total score yielded greater clinical net benefit compared to individual variables. Among the machine learning models evaluated, the decision tree algorithm exhibited superior discrimination (AUC=0.886) with robust sensitivity and specificity, despite marginally lower accuracy. Conclusion: The prediction model developed in this study provides a convenient and effective tool for stratifying PsA risk in PsO patients, facilitating the identification of high-risk individuals for enhanced clinical monitoring and personalized disease management strategies.

Key words: psoriasis, psoriatic arthritis, machine learning, prediction model, decision tree