China Journal of Leprosy and Skin Diseases ›› 2025, Vol. 41 ›› Issue (8): 571-577.doi: 10.12144/zgmfskin202508571

• Original Articles • Previous Articles     Next Articles

Analysis of factors influencing the treatment of moderate to severe plaque psoriasis with secukinumab

HE Xiaojun, HAN Junya, WANG Decheng, HUANG Yaqun, FANG Xianfeng   

  1. Department of Dermatology and Venereology, Yichang Central People's Hospital, the First College of Clinical Medical Science, China Three Gorges University, Yichang 443003, China
  • Online:2025-08-15 Published:2025-07-31

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Abstract: Objective: To analyze the relevant factors affecting the response of patients with moderate to severe plaque psoriasis treated with secukinumab and establish a nomogram prediction model. Methods: A retrospective analysis was conducted on the clinical data, laboratory test results and lesion characteristics of 276 patients with moderate to severe plaque psoriasis who visited our hospital from September 2019 to June 2024. The therapeutic effect was evaluated based on the Psoriasis Lesion Area and Severity Index (PASI) score. Univariate and multivariate Logistic regression analyses were used to analyze the factors influencing the response, and a nomogram prediction model was constructed. The model efficacy was evaluated through the ROC curve and internal validation of Bootstrap. Results: Univariate analysis showed that plaque size, BMI, blood glucose level, total cholesterol level, triglyceride level, baseline total PASI score, baseline BSA and baseline lesion infiltration score were correlated with treatment response (P＜0.05). Multivariate Logistic regression analysis showed that baseline head and neck PASI score, BMI, blood glucose level and triglyceride level were independent influencing factors (P＜0.05). The area of the ROC curve of the nomogram prediction model constructed based on the above related factors was 0.901 (95% CI: 0.860-0.943). The C-index of Bootstrap's internal validation was 0.901 (95% CI: 0.898-0.904), and the calibration curve and decision curve indicated that the model had good predictive efficacy and clinical application value. Conclusion: Baseline PASI score of the head and neck, BMI, blood glucose level and triglyceride level are independent related factors affecting the response of patients with moderate to severe plaque psoriasis treated with secukinumab. The nomogram prediction model constructed based on the above variables has good prediction efficacy and can provide guidance for the treatment of patients with psoriasis.

Key words: psoriasis, secukinumab, treatment response, relevant factors, nomogram model