China Journal of Leprosy and Skin Diseases ›› 2026, Vol. 42 ›› Issue (1): 30-34.doi: 10.12144/zgmfskin202601030

• Original Articles • Previous Articles     Next Articles

Development of a relapse prediction model for psoriasis vulgaris based on serum TARC, CCL19 and LRG1 levels

LI Xin1, WANG Jun2, MIAO Guoying1, ZHANG Jianzhong1, LI Lingyu1   

  1. 1 Department of Dermatology, Affiliated Hospital of Hebei University of Engineering, Handan 056002, China; 2 Practical Training Center, Handan Vocational College of Science and Technology, Handan 056046, China
  • Online:2026-01-15 Published:2026-01-15

PDF (PC)

3

Like

Abstract: Objective: To identify the recurrence factors of psoriasis vulgaris (PV) and construct an early warning model. Methods: A total of 258 PV patients admitted to the Affiliated Hospital of Hebei University of Engineering from January 2022 to December 2023 were enrolled as the research subjects. The samples were randomly divided into a training set and a test set at a ratio of 7∶3. All patients were followed up for 1 year via outpatient visits or telephone interviews, and were categorized into a recurrence group and a non-recurrence group based on their disease status. Clinical data and serum levels of thymus and activation-regulated chemokine (TARC), C-C motif chemokine ligand 19 (CCL19), and leucine-rich α-2-glycoprotein 1 (LRG1) were compared between the two groups. Logistic regression analysis was performed to identify the influencing factors of PV recurrence. Four machine learning models were constructed, and receiver operating characteristic (ROC) curves and area under the curve (AUC) were used to evaluate their predictive performance. Results: Among the 258 PV patients, 8 were lost to follow-up, and 120 experienced recurrence. In both the training set and test set, the recurrence group showed significantly higher serum levels of TARC, CCL19, and LRG1, as well as higher Self-Rating Anxiety Scale (SAS) scores, Self-Rating Depression Scale (SDS) scores, and incidence of sleep disorders compared with the non-recurrence group (Ps＜0.05). Logistic regression analysis confirmed four variables—serum TARC, CCL19, LRG1 levels, and sleep disorders—as independent influencing factors of PV recurrence, which were used for model construction. ROC curve analysis revealed that the random forest (RF) algorithm model achieved higher AUC values in both the training set and test set than the deep learning (DL), gradient boosting machine (GBM), and generalized linear model (GLM) (Ps＜0.05). Conclusion: The random forest algorithm model established based on serum TARC, CCL19, LRG1 levels and sleep disorders can effectively predict the recurrence risk of PV.

Key words: psoriasis vulgaris, recurrence, early warning model, TARC, CCL19, LRG1