Huang Shan, Bai Yanping, Qi Ruozhou, Yu Hongda, Duan Xingwu
Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China.
Department of Dermatology, China-Japan Friendship Hospital, National Center for Integrative Chinese and Western Medicine, Beijing, China.
J Dermatolog Treat. 2025 Dec;36(1):2480743. doi: 10.1080/09546634.2025.2480743. Epub 2025 Mar 19.
Identifying the risk of psoriasis relapse after discontinuing biologics can help optimize treatment strategies, potentially reducing relapse rates and alleviating the burden of disease management.
To develop and validate a personalized prediction model for psoriasis relapse following the discontinuation of biologics.
This study enrolled patients who achieved remission following biologic therapy. Relapse predictors were identified using the Boruta algorithm combined with multivariate Cox regression. A nomogram and an online calculator were created to aid in the visualization and computation of outcomes. The model's performance was thoroughly assessed using Receiver Operating Characteristic (ROC) curves, Area Under the Curve (AUC), C-statistics, calibration plots, and Decision Curve Analysis (DCA).
The study included 597 patients, with 534 in the derivation cohort and 63 in the validation cohort. Anxiety, disease duration, prior biologic treatments, treatment duration, time to achieve PASI 75, and maximum PASI response were identified as influential factors for relapse and were incorporated into the model. Both internal and external evaluations indicate that the model exhibits good predictive accuracy.
A multivariate model leveraging standard clinical data can relatively accurately predict the risk of psoriasis relapse post-biologic discontinuation, guiding personalized treatment strategies.
确定停用生物制剂后银屑病复发的风险有助于优化治疗策略，可能降低复发率并减轻疾病管理负担。
开发并验证一种用于预测生物制剂停用后银屑病复发的个性化预测模型。
本研究纳入了生物治疗后达到缓解的患者。使用Boruta算法结合多变量Cox回归确定复发预测因素。创建了列线图和在线计算器以辅助结果的可视化和计算。使用受试者工作特征（ROC）曲线、曲线下面积（AUC）、C统计量、校准图和决策曲线分析（DCA）对模型性能进行了全面评估。
该研究纳入了597例患者，其中推导队列534例，验证队列63例。焦虑、病程、既往生物治疗、治疗持续时间、达到PASI 75的时间以及最大PASI反应被确定为复发的影响因素并纳入模型。内部和外部评估均表明该模型具有良好的预测准确性。
利用标准临床数据的多变量模型可以相对准确地预测生物制剂停用后银屑病复发的风险，指导个性化治疗策略。
