Predicting persistence of atopic dermatitis in children using clinical attributes and serum proteins.

BACKGROUND: Atopic dermatitis (AD) is the most common inflammatory skin disease in children, with 30% of all those diagnosed developing chronic or relapsing disease by adolescence. Such disease persistence cannot yet be predicted. The aim of the present study was to predict the natural course of AD using clinical parameters and serum proteins. METHODS: Sera of 144 children with AD (age 0-3 years) were analyzed for IgE and 33 cytokines, chemokines, and growth factors. Patient disease course until the age of 7 years was assessed retrospectively. Unsupervised k-means clustering was performed to define disease endotypes. Identified factors associated with AD persistence at the age of 7 years were validated in children with AD in an independent cohort (LISA Munich; n = 168). Logistic regression and XGBoosting methods followed by cross-validation were applied to predict individual disease outcomes. RESULTS: Three distinct endotypes were found in infancy, characterized by a unique inflammatory signature. Factors associated with disease persistence were disease score (SCORAD), involvement of the limbs, flexural lesion distribution at the age of 3 years, allergic comorbidities, and disease exacerbation by the trigger factors stress, pollen exposure, and change in weather. Persistence was predicted with a sensitivity of 81.8% and a specificity of 82.4%. Factors with a high impact on the prediction of persistence were SCORAD at the age of 3 years, trigger factors, and low VEGF serum levels. CONCLUSION: Atopic dermatitis in infancy comprises three immunological endotypes. Disease persistence can be predicted using serum cytokines and clinical variables.

IMPROVE 1.0: Individual Monitoring of Psoriasis Activity by Regular Online App Questionnaires and Outpatient Visits.

Smartphone apps gain more and more importance in supporting management of chronic diseases. Psoriasis is a highly prevalent, lifelong chronic inflammatory skin disease with a high impact on patient's quality of life. Disease management includes regular topical and systemic treatment of skin lesions as well as co-treatment of metabolic and psychologic disorders. In this study, we investigated the potential of a new smartphone app (IMPROVE 1.0) for individual monitoring of disease activity and disease influencing factors. Twelve out of 50 psoriasis patients asked for study participation performed self-assessment of psoriasis severity, life quality, and stress scores using the app over a period of 1 year. Every 2 months, study participants were carefully examined by a dermatologist in order to control the quality of app-reported data. We found that psoriasis severity and life quality values as entered in the app closely correlate to physician's examination. Furthermore, we detected strong correlations of disease activity with life quality and psoriasis serum biomarker. Temporal relations between psoriasis aggravation and previous changes of lifestyle factors, such as increased stress levels, were observed in individual patients, indicating a high potential for preventive interventions in future psoriasis apps. The vast majority of study participants evaluated IMPROVE 1.0 app positively and wish to include the app into their daily life. Hence, we demonstrate that smartphone apps are a useful tool to raise self-awareness for the dimensions of complex diseases and fully integrate psoriasis patients into individual disease management. These data are important to develop more advanced digital tools supporting the management of chronic diseases in the future.