Background: 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.
Objective: We aim to assess whether serum biomarkers can predict the outcome of systemic immunosuppressive therapy in adult AD patients.
Methods: 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 (EASI, (o)SCORAD and POEM) two-weeks ahead. We conducted feature selection to identify the most important biomarkers for the prediction of AD severity scores.
Results: 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.
Conclusions: 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.