Towards a data-driven personalised management of Atopic Dermatitis severity

Atopic Dermatitis (AD, eczema) is a common inflammatory skin disease, characterised by dry and itchy skin. AD cannot be cured, but its long-term outcomes can be managed with treatments. Given the heterogeneity in patients' responses to treatment, designing personalised rather than ``one-size-fits-all" treatment strategies is of high clinical relevance. In this thesis, we aim to pave the way towards a data-driven personalised management of AD severity, whereby severity data would be collected automatically from photographs without the need for patients to visit a clinic, be used to predict the evolution of AD severity, and generate personalised treatment recommendations. First, we developed EczemaNet, a computer vision pipeline using convolution neural networks that detects areas of AD from photographs and then makes probabilistic assessments of AD severity. EczemaNet was internally validated with a medium-size dataset of images collected in a published clinical trial and demonstrated fair performance. Then, we developed models predicting the daily to weekly evolution of AD severity. We highlighted the challenges of extracting signals from noisy severity data, with small and practically not significant effects of environmental factors and biomarkers on prediction. We showed the importance of using high-quality measurements of validated and objective (vs subjective) severity scores. We also stressed the importance of modelling individual severity items rather than aggregate scores, and introduced EczemaPred, a principled approach to predict AD severity using Bayesian state-space models. Our models are flexible by design, interpretable and can quantify uncertainty in measurements, parameters and predictions. The models demonstrated good performance to predict the Patient-Oriented SCOring AD (PO-SCORAD). Finally, we generated personalised treatment recommendations using Bayesian decision analysis. We observed that treatment effects and recommendations could be confounded by the clinical phenotype of patients. We also pretrained our model using historical data and combined clinical and self-assessments. In conclusion, we have demonstrated the feasibility and the challenges of a data-driven personalised management of AD severity.Open Acces

Systemic and stratum corneum biomarkers of severity in infant atopic dermatitis include markers of innate and T helper cell-related immunity and angiogenesis

BACKGROUND: Biomarkers of atopic dermatitis (AD) are largely lacking, especially in infant AD. Those that have been examined to date have focused mostly on serum cytokines with few on non-invasive biomarkers in the skin. OBJECTIVES: We aimed to explore biomarkers obtainable from non-invasive sampling of infant skin. We compared these to plasma biomarkers and structural and functional measures of the skin barrier. METHODS: We recruited 100 infants at first presentation with AD, who were treatment naïve to topical or systemic anti-inflammatory therapies and 20 healthy children. We sampled clinically unaffected skin by tape stripping the stratum corneum (SC). Multiple cytokines and chemokines and natural moisturizing factors (NMF) were measured in the SC and plasma. We recorded disease severity and skin barrier function. RESULTS: 19 SC and 12 plasma biomarkers showed significant difference between healthy and AD skin. Some biomarkers were common to both the SC and plasma, and others were compartment-specific. Identified biomarkers of AD severity included Th2 skewed markers (IL-13, CCL17, CCL22, IL-5), markers of innate activation (IL-18, Il-1α, IL1β, CXCL8), angiogenesis (Flt-1, VEGF) and others (sICAM-1, vCAM-1, IL-16, IL-17A). CONCLUSIONS: We identified clinically relevant biomarkers of AD, including novel markers, easily sampled and typed in infants. These markers may provide objective assessment of disease severity and suggest new therapeutic targets, or response measurement targets for AD. Future studies will be required to determine if these biomarkers, seen in very early AD, can predict disease outcomes or comorbidities

Personalized prediction of daily eczema severity scores using a mechanistic machine learning model.

BACKGROUND: Atopic dermatitis (AD) is a chronic inflammatory skin disease with periods of flares and remission. Designing personalized treatment strategies for AD is challenging, given the apparent unpredictability and large variation in AD symptoms and treatment responses within and across individuals. Better prediction of AD severity over time for individual patients could help to select optimum timing and type of treatment for improving disease control. OBJECTIVE: We aimed to develop a proof of principle mechanistic machine learning model that predicts the patient-specific evolution of AD severity scores on a daily basis. METHODS: We designed a probabilistic predictive model and trained it using Bayesian inference with the longitudinal data from two published clinical studies. The data consisted of daily recordings of AD severity scores and treatments used by 59 and 334 AD children over 6 months and 16 weeks, respectively. Validation of the predictive model was conducted in a forward-chaining setting. RESULTS: Our model was able to predict future severity scores at the individual level and improved chance-level forecast by 60%. Heterogeneous patterns in severity trajectories were captured with patient-specific parameters such as the short-term persistence of AD severity and responsiveness to topical steroids, calcineurin inhibitors and step-up treatment. CONCLUSIONS: Our proof of principle model successfully predicted the daily evolution of AD severity scores at an individual level and could inform the design of personalized treatment strategies that can be tested in future studies. Our model-based approach can be applied to other diseases with apparent unpredictability and large variation in symptoms and treatment responses such as asthma