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BackgroundAtopic dermatitis (AD) is a chronic inflammatory skin disease with periods of flares and remission. Designing personalised 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.ObjectiveWe 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.MethodsWe 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.ResultsOur 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.ConclusionsOur proof of principle model successfully predicted the daily evolution of AD severity scores at an individual level, and could inform the design of personalised treatment strategies that can be tested in future studies. Our model?based approach can be applied to other diseases such as asthma with apparent unpredictability and large variation in symptoms and treatment responses

© 2020 The Authors. Clinical & Experimental Allergy published by John Wiley & Sons Ltd. Background: Food allergy diagnosis in clinical studies can be challenging. Oral food challenges (OFC) are time-consuming, carry some risk and may, therefore, not be acceptable to all study participants. Objective: To design and evaluate an algorithm for detecting IgE-mediated food allergy in clinical study participants who do not undergo OFC. Methods: An algorithm for trial participants in the Barrier Enhancement for Eczema Prevention (BEEP) study who were unwilling or unable to attend OFC was developed. BEEP is a pragmatic, multi-centre, randomized-controlled trial of daily emollient for the first year of life for primary prevention of eczema and food allergy in high-risk infants (ISRCTN21528841). We built on the European iFAAM consensus guidance to develop a novel food allergy diagnosis algorithm using available information on previous allergenic food ingestion, food reaction(s) and sensitization status. This was implemented by a panel of food allergy experts blind to treatment allocation and OFC outcome. We then evaluated the algorithm's performance in both BEEP and Enquiring About Tolerance (EAT) study participants who did undergo OFC. Results: In 31/69 (45%) BEEP and 44/55 (80%) EAT study control group participants who had an OFC the panel felt confident enough to categorize children as “probable food allergy” or “probable no food allergy”. Algorithm-derived panel decisions showed high sensitivity 94% (95%CI 68, 100) BEEP; 90% (95%CI 72, 97) EAT and moderate specificity 67% (95%CI 39, 87) BEEP; 67% (95%CI 39, 87) EAT. Sensitivity and specificity were similar when all BEEP and EAT participants with OFC outcome were included. Conclusion: We describe a new algorithm with high sensitivity for IgE-mediated food allergy in clinical study participants who do not undergo OFC. Clinical Relevance: This may be a useful tool for excluding food allergy in future clinical studies where OFC is not conducted.

Additional file 1. Sensitivity analyses, secondary outcomes and interim analyses.

BackgroundAtopic eczema onset is described primarily in early childhood; the frequency and characteristics of adult-onset disease remain controversial.ObjectiveTo determine the proportion of individuals who report atopic eczema symptoms between birth and mid adulthood, and to examine demographic, immunologic, and genetic factors associated with period of symptom onset.MethodsWe conducted a longitudinal study using data from two nationally representative community-based birth cohorts from the United Kingdom: the British Cohort Studies 1958 and 1970. Individuals were followed from birth through age 42-50. The primary outcome was the age period of self-reported atopic eczema symptom onset based on repeated measures of self-reported atopic eczema at each survey wave.ResultsThe annual period prevalence of atopic eczema ranged from 5-15% in two cohorts of over 17,000 participants each followed from birth through mid-age. There was no clear trend in prevalence by age, and among adults reporting active atopic eczema during a given year, only 38% had symptom onset reported in childhood. When compared with individuals whose eczema started in childhood, those with adult-onset disease were more likely to be women, from Scotland or Northern England, of lower childhood socio-economic group, smokers in adulthood, and less likely to have a history of asthma. In a sub-analysis using data from the 1958 cohort only, genetic mutations previously associated with atopic eczema, including filaggrin null mutations, and allergen-specific IgE were more common among those with childhood-onset disease.ConclusionRates of self-reported atopic eczema remain high after childhood, and adult-onset atopic eczema has different risk factor associations than childhood-onset eczema.

Background Atopic eczema onset is described primarily in early childhood, and the frequency and characteristics of adult-onset disease remain controversial. Objective We sought to determine the proportion of subjects who report atopic eczema symptoms between birth and midadulthood and to examine demographic, immunologic, and genetic factors associated with period of symptom onset. Methods We conducted a longitudinal study using data from 2 nationally representative community-based birth cohorts from the United Kingdom: the British Cohort Studies 1958 and 1970. Subjects were followed from birth through age 42 to 50 years. The primary outcome was the age period of self-reported atopic eczema symptom onset based on repeated measures of self-reported atopic eczema at each survey wave. Results The annual period prevalence of atopic eczema ranged from 5% to 15% in 2 cohorts of more than 17,000 participants each followed from birth through middle age. There was no clear trend in prevalence by age, and among adults reporting active atopic eczema during a given year, only 38% had symptom onset reported in childhood. When compared with subjects whose eczema started in childhood, those with adult-onset disease were more likely to be women, from Scotland or Northern England, of lower childhood socioeconomic group, smokers in adulthood, and less likely to have a history of asthma. In a subanalysis using data from the 1958 cohort only, genetic mutations previously associated with atopic eczema, including filaggrin-null mutations, and allergen-specific IgE were more common among those with childhood-onset disease. Conclusion Rates of self-reported atopic eczema remain high after childhood, and adult-onset atopic eczema has different risk factor associations than childhood-onset eczema., Graphical abstract

Some previously described environmental associations for atopic eczema may be due to reverse causation. We explored the role of reverse causation by comparing individual- and school-level results for multiple atopic eczema risk factors. The International Study of Asthma and Allergies in Childhood (i.e, ISAAC) Phase Three surveyed children in schools (the sampling unit) regarding atopic eczema symptoms and potential risk factors. We assessed the effect of these risk factors on atopic eczema symptoms using mixed-effect logistic regression models, first with individual-level exposure data and second with school-level exposure prevalence. Overall, 546,348 children from 53 countries were included. At ages 6–7 years, the strongest individual-level associations were with current paracetamol use (odds ratio [OR] = 1.45, 95% confidence interval [CI] = 1.37–1.54), which persisted at school-level (OR = 1.55, 95% CI = 1.10–2.21), early-life antibiotics (OR = 1.41, 95% CI = 1.34–1.48), and early-life paracetamol use (OR = 1.28, 95% CI = 1.21–1.36), with the former persisting at the school level, whereas the latter was no longer observed (OR = 1.35, 95% CI = 1.00–1.82 and OR = 0.94, 95% CI = 0.69–1.28, respectively). At ages 13–14 years, the strongest associations at the individual level were with current paracetamol use (OR = 1.57, 95% CI = 1.51–1.63) and open-fire cooking (OR = 1.46, 95% CI = 1.33–1.62); both were stronger at the school level (OR = 2.57, 95% CI = 1.84–3.59 and OR = 2.38, 95% CI = 1.52–3.73, respectively). Association with exposure to heavy traffic (OR = 1.31, 95% CI = 1.27–1.36) also persisted at the school level (OR = 1.40, 95% CI = 1.07–1.82). Most individual- and school-level effects were consistent, tending to exclude reverse causation.

Some previously described environmental associations for atopic eczema may be due to reverse causation. We explored the role of reverse causation by comparing individual- and school-level results for multiple atopic eczema risk factors. The International Study of Asthma and Allergies in Childhood (i.e, ISAAC) Phase Three surveyed children in schools (the sampling unit) regarding atopic eczema symptoms and potential risk factors. We assessed the effect of these risk factors on atopic eczema symptoms using mixed-effect logistic regression models, first with individual-level exposure data and second with school-level exposure prevalence. Overall, 546,348 children from 53 countries were included. At ages 6-7 years, the strongest individual-level associations were with current paracetamol use (odds ratio [OR] = 1.45, 95% confidence interval [CI] = 1.37-1.54), which persisted at school-level (OR = 1.55, 95% CI = 1.10-2.21), early-life antibiotics (OR = 1.41, 95% CI = 1.34-1.48), and early-life paracetamol use (OR = 1.28, 95% CI = 1.21-1.36), with the former persisting at the school level, whereas the latter was no longer observed (OR = 1.35, 95% CI = 1.00-1.82 and OR = 0.94, 95% CI = 0.69-1.28, respectively). At ages 13-14 years, the strongest associations at the individual level were with current paracetamol use (OR = 1.57, 95% CI = 1.51-1.63) and open-fire cooking (OR = 1.46, 95% CI = 1.33-1.62); both were stronger at the school level (OR = 2.57, 95% CI = 1.84-3.59 and OR = 2.38, 95% CI = 1.52-3.73, respectively). Association with exposure to heavy traffic (OR = 1.31, 95% CI = 1.27-1.36) also persisted at the school level (OR = 1.40, 95% CI = 1.07-1.82). Most individual- and school-level effects were consistent, tending to exclude reverse causation.

BACKGROUND: Early accurate detection of all skin cancer types is essential to guide appropriate management and to improve morbidity and survival. Melanoma and squamous cell carcinoma (SCC) are high-risk skin cancers which have the potential to metastasise and ultimately lead to death, whereas basal cell carcinoma (BCC) is usually localised with potential to infiltrate and damage surrounding tissue. Anxiety around missing early curable cases needs to be balanced against inappropriate referral and unnecessary excision of benign lesions. Teledermatology provides a way for generalist clinicians to access the opinion of a specialist dermatologist for skin lesions that they consider to be suspicious without referring the patients through the normal referral pathway. Teledermatology consultations can be 'store-and-forward' with electronic digital images of a lesion sent to a dermatologist for review at a later time, or can be live and interactive consultations using videoconferencing to connect the patient, referrer and dermatologist in real time. OBJECTIVES: To determine the diagnostic accuracy of teledermatology for the detection of any skin cancer (melanoma, BCC or cutaneous squamous cell carcinoma (cSCC)) in adults, and to compare its accuracy with that of in-person diagnosis. SEARCH METHODS: We undertook a comprehensive search of the following databases from inception up to August 2016: Cochrane Central Register of Controlled Trials, MEDLINE, Embase, CINAHL, CPCI, Zetoc, Science Citation Index, US National Institutes of Health Ongoing Trials Register, NIHR Clinical Research Network Portfolio Database and the World Health Organization International Clinical Trials Registry Platform. We studied reference lists and published systematic review articles. SELECTION CRITERIA: Studies evaluating skin cancer diagnosis for teledermatology alone, or in comparison with face-to-face diagnosis by a specialist clinician, compared with a reference standard of histological confirmation or clinical follow-up and expert opinion. We also included studies evaluating the referral accuracy of teledermatology compared with a reference standard of face-to-face diagnosis by a specialist clinician. DATA COLLECTION AND ANALYSIS: Two review authors independently extracted all data using a standardised data extraction and quality assessment form (based on QUADAS-2). We contacted authors of included studies where there were information related to the target condition of any skin cancer missing. Data permitting, we estimated summary sensitivities and specificities using the bivariate hierarchical model. Due to the scarcity of data, we undertook no covariate investigations for this review. For illustrative purposes, we plotted estimates of sensitivity and specificity on coupled forest plots for diagnostic threshold and target condition under consideration. MAIN RESULTS: The review included 22 studies reporting diagnostic accuracy data for 4057 lesions and 879 malignant cases (16 studies) and referral accuracy data for reported data for 1449 lesions and 270 'positive' cases as determined by the reference standard face-to-face decision (six studies). Methodological quality was variable with poor reporting hindering assessment. The overall risk of bias was high or unclear for participant selection, reference standard, and participant flow and timing in at least half of all studies; the majority were at low risk of bias for the index test. The applicability of study findings were of high or unclear concern for most studies in all domains assessed due to the recruitment of participants from secondary care settings or specialist clinics rather than from primary or community-based settings in which teledermatology is more likely to be used and due to the acquisition of lesion images by dermatologists or in specialist imaging units rather than by primary care clinicians.Seven studies provided data for the primary target condition of any skin cancer (1588 lesions and 638 malignancies). For the correct diagnosis of lesions as malignant using photographic images, summary sensitivity was 94.9% (95% confidence interval (CI) 90.1% to 97.4%) and summary specificity was 84.3% (95% CI 48.5% to 96.8%) (from four studies). Individual study estimates using dermoscopic images or a combination of photographic and dermoscopic images generally suggested similarly high sensitivities with highly variable specificities. Limited comparative data suggested similar diagnostic accuracy between teledermatology assessment and in-person diagnosis by a dermatologist; however, data were too scarce to draw firm conclusions. For the detection of invasive melanoma or atypical intraepidermal melanocytic variants both sensitivities and specificities were more variable. Sensitivities ranged from 59% (95% CI 42% to 74%) to 100% (95% CI 48% to 100%) and specificities from 30% (95% CI 22% to 40%) to 100% (95% CI 93% to 100%), with reported diagnostic thresholds including the correct diagnosis of melanoma, classification of lesions as 'atypical' or 'typical, and the decision to refer or to excise a lesion.Referral accuracy data comparing teledermatology against a face-to-face reference standard suggested good agreement for lesions considered to require some positive action by face-to-face assessment (sensitivities of over 90%). For lesions considered of less concern when assessed face-to-face (e.g. for lesions not recommended for excision or referral), agreement was more variable with teledermatology specificities ranging from 57% (95% CI 39% to 73%) to 100% (95% CI 86% to 100%), suggesting that remote assessment is more likely recommend excision, referral or follow-up compared to in-person decisions. AUTHORS' CONCLUSIONS: Studies were generally small and heterogeneous and methodological quality was difficult to judge due to poor reporting. Bearing in mind concerns regarding the applicability of study participants and of lesion image acquisition in specialist settings, our results suggest that teledermatology can correctly identify the majority of malignant lesions. Using a more widely defined threshold to identify 'possibly' malignant cases or lesions that should be considered for excision is likely to appropriately triage those lesions requiring face-to-face assessment by a specialist. Despite the increasing use of teledermatology on an international level, the evidence base to support its ability to accurately diagnose lesions and to triage lesions from primary to secondary care is lacking and further prospective and pragmatic evaluation is needed.