Hurault G, Roekevisch E, Schram M E, Szegedi K, Kezic S, Middelkamp-Hup M A, Spuls P I, Tanaka R J
Department of Bioengineering Imperial College London London UK.
Department of Dermatology, Amsterdam Public health, Infection and Immunity Amsterdam UMC, Location AMC University of Amsterdam Amsterdam The Netherlands.
Skin Health Dis. 2022 Jan 7;2(1):e77. doi: 10.1002/ski2.77. eCollection 2022 Mar.
Atopic dermatitis (AD or eczema) is a most common chronic skin disease. Designing personalised treatment strategies for AD based on patient stratification is of high clinical relevance, given a considerable variation in the clinical phenotype and responses to treatments among patients. It has been hypothesised that the measurement of biomarkers could help predict therapeutic responses for individual patients.
We aim to assess whether serum biomarkers can predict the outcome of systemic immunosuppressive therapy in adult AD patients.
We developed a statistical machine learning model using the data of an already published longitudinal study of 42 patients who received azathioprine or methotrexate for over 24 weeks. The data contained 26 serum cytokines and chemokines measured before the therapy. The model described the dynamic evolution of the latent disease severity and measurement errors to predict AD severity scores (Eczema Area and Severity Index, (o)SCORing of AD and Patient Oriented Eczema Measure) two-weeks ahead. We conducted feature selection to identify the most important biomarkers for the prediction of AD severity scores.
We validated our model in a forward chaining setting and confirmed that it outperformed standard time-series forecasting models. Adding biomarkers did not improve predictive performance.
In this study, biomarkers had a negligible and non-significant effect for predicting the future AD severity scores and the outcome of the systemic therapy.
特应性皮炎（AD或湿疹）是一种最常见的慢性皮肤病。鉴于患者之间临床表型和治疗反应存在相当大的差异，基于患者分层为AD设计个性化治疗策略具有高度临床相关性。据推测，生物标志物的测量有助于预测个体患者的治疗反应。
我们旨在评估血清生物标志物是否能预测成年AD患者全身免疫抑制治疗的结果。
我们使用一项已发表的对42例接受硫唑嘌呤或甲氨蝶呤治疗超过24周的患者的纵向研究数据，开发了一个统计机器学习模型。该数据包含治疗前测量的26种血清细胞因子和趋化因子。该模型描述了潜在疾病严重程度的动态演变和测量误差，以提前两周预测AD严重程度评分（湿疹面积和严重程度指数、AD的（o）SCORing和患者导向性湿疹测量）。我们进行了特征选择，以确定预测AD严重程度评分最重要的生物标志物。
我们在向前链接设置中验证了我们的模型，并确认其性能优于标准时间序列预测模型。添加生物标志物并未改善预测性能。
在本研究中，生物标志物对预测未来AD严重程度评分和全身治疗结果的影响可忽略不计且无统计学意义。
