Huang Shan, Chen Bailin, Qi Yiming, Duan Xingwu, Bai Yanping
Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China.
Graduate School, Beijing University of Chinese Medicine, Beijing, China.
Front Med (Lausanne). 2024 Nov 26;11:1488096. doi: 10.3389/fmed.2024.1488096. eCollection 2024.
Some patients with psoriasis experience relapses shortly after discontinuation of biologics. However, there is a lack of risk prediction tools to identify those at high risk of relapse.
To develop and validate a risk prediction model for psoriasis relapse after biologics discontinuation.
Publications from PubMed, EMBASE, Medline, and the Cochrane Library were systematically searched and meta-analyses were conducted to identify risk factors for psoriasis relapse after biologics discontinuation. Statistically significant risk factors were identified and used to create a risk assessment model weighted by the impact of each factor. The model was externally validated using a cohort of 416 Chinese psoriasis patients.
Eight studies ( = 2066) were included in the meta-analysis. Body mass index (BMI), smoking, disease duration, comorbid psoriatic arthritis (PsA), remission speed and extent during treatment, history of biologic therapy, and therapy duration were identified as correlates of relapse in the meta-analysis and were incorporated into the prediction model. The median age of the 416 patients in the validation cohort was 41.5 (IQR 32, 53) years, with 63% male, and a baseline PASI score of 15.4 (IQR 10.5, 21). It was verified that the area under the curve (AUC) of the prediction model was 0.796 (95% CI, 0.753-0.839), with an optimal cut-off value of 11.25 points, sensitivity of 65.1%, and specificity of 82.2%.
Multivariate models using available clinical parameters can predict relapse risk in psoriasis patients after biologics discontinuation. Early individual identification of patients at risk of relapse, and screening of candidate cohorts for long-term treatment or dose reduction may benefit both patients and physicians.
一些银屑病患者在停用生物制剂后不久就会复发。然而，缺乏风险预测工具来识别那些复发风险高的患者。
建立并验证一个生物制剂停用后银屑病复发的风险预测模型。
系统检索PubMed、EMBASE、Medline和Cochrane图书馆的出版物，并进行荟萃分析以确定生物制剂停用后银屑病复发的风险因素。识别出具有统计学意义的风险因素，并用于创建一个根据每个因素的影响加权的风险评估模型。该模型在416名中国银屑病患者队列中进行了外部验证。
荟萃分析纳入了8项研究（n = 2066）。体重指数（BMI）、吸烟、病程、合并银屑病关节炎（PsA）、治疗期间的缓解速度和程度、生物治疗史以及治疗持续时间在荟萃分析中被确定为复发的相关因素，并被纳入预测模型。验证队列中416名患者的中位年龄为41.5岁（四分位间距32, 53），男性占63%，基线银屑病面积和严重程度指数（PASI）评分为15.4（四分位间距10.5, 21）。验证发现预测模型的曲线下面积（AUC）为0.796（95%可信区间，0.753 - 0.839），最佳截断值为11.25分，敏感性为65.1%，特异性为82.2%。
使用可用临床参数的多变量模型可以预测银屑病患者生物制剂停用后的复发风险。早期个体识别复发风险患者，并筛选长期治疗或减量的候选队列可能对患者和医生都有益。
