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Electronic health records hold great promise for clinical and epidemiologic research. Undertaking atopic eczema (AE) research using such data is challenging because of its episodic and heterogeneous nature. We sought to develop and validate a diagnostic algorithm that identifies AE cases based on codes used for electronic records used in the UK Health Improvement Network. We found that at least one of five diagnosis codes plus two treatment codes for any skin-directed therapy were likely to accurately identify patients with AE. To validate this algorithm, a questionnaire was sent to the physicians of 200 randomly selected children and adults. The primary outcome, positive predictive value for a physician-confirmed diagnosis of AE, was 86% (95% confidence interval = 80-91). Additional criteria increased the PPV up to 95% but would miss up to 89% of individuals with physician-confirmed AE. The first and last entered diagnosis codes for individuals showed good agreement with the physician-confirmed age at onset and last disease activity; the mean difference was 0.8 years (95% confidence interval = -0.3 to 1.9) and -1.3 years (95% confidence interval = -2.5 to -0.1), respectively. A combination of diagnostic and prescription codes can be used to reliably estimate the diagnosis and duration of AE from The Health Improvement Network primary care electronic health records in the UK.

BackgroundThere are sparse and conflicting data regarding the long-term clinical course of atopic dermatitis (AD). Although often described as a childhood disease, newer population-based estimates suggest the prevalence of pediatric and adult disease may be similar.MethodsOur objective was to determine whether there is a decline in the prevalence of AD in population-based cohorts of patients followed longitudinally beyond childhood. We conducted a systematic review and meta-analysis including studies assessing AD prevalence across 3 or more points in time. The primary outcome was weighted overall risk difference (percentage decrease in AD prevalence).ResultsOf 2080 references reviewed, 7 studies with 13 515 participants were included. Participants were assessed at 3-6 time points, ranging from age 3 months to 26 years. The percentage decrease in prevalence after age 12 was 1%, which was not significantly different from zero (95% confidence interval -2%-5%). Similar results were found with other age cut-offs.ConclusionThe prevalence of AD in longitudinal birth cohort studies is similar in childhood and adolescence/early adulthood.

Hair follicles (HFs) undergo lifelong cyclical transformations, progressing through stages of rapid growth (anagen), regression (catagen), and relative "quiescence" (telogen). Given that HF cycling abnormalities underlie many human hair growth disorders, the accurate classification of individual cycle stages within skin biopsies is clinically important and essential for hair research. For preclinical human hair research purposes, human scalp skin can be xenografted onto immunocompromised mice to study human HF cycling and manipulate long-lasting anagen in vivo. Although available for mice, a comprehensive guide on how to recognize different human hair cycle stages in vivo is lacking. In this article, we present such a guide, which uses objective, well-defined, and reproducible criteria, and integrates simple morphological indicators with advanced, (immuno)-histochemical markers. This guide also characterizes human HF cycling in xenografts and highlights the utility of this model for in vivo hair research. Detailed schematic drawings and representative micrographs provide examples of how best to identify human HF stages, even in suboptimally sectioned tissue, and practical recommendations are given for designing human-on-mouse hair cycle experiments. Thus, this guide seeks to offer a benchmark for human hair cycle stage classification, for both hair research experts and newcomers to the field.