Evaluation of measurement errors in the Patient-Oriented Eczema Measure (POEM) outcome.

BACKGROUND: The Patient-Oriented Eczema Measure (POEM) is the recommended core outcome instrument for atopic dermatitis (AD) symptoms. POEM is reported by recalling the presence/absence of seven symptoms in the last 7 days. OBJECTIVE: To evaluate measurement errors in POEM recordings due to imperfect recall. METHODS: Using data from a clinical trial of 247 AD patients aged 12-65 years, we analysed the reported POEM score (r-POEM) and the POEM derived from the corresponding daily scores for the same seven symptoms without weekly recall (d-POEM). We quantified recall error by comparing the r-POEM and d-POEM for 777 patient-weeks collected from 207 patients, and estimated two components of recall error: (1) recall bias due to systematic errors in measurements and (2) recall noise due to random errors in measurements, using a bespoke statistical model. RESULTS: POEM scores have a relatively low recall bias, but a high recall noise. Recall bias was estimated at 1.2 points lower for the r-POEM on average than the d-POEM, with a recall noise of 5.7 points. For example, a patient with a recall-free POEM of 11 (moderate) could report their POEM score anywhere from 5 to 14 (with 95% probability) because of recall error. Model estimates suggested that patients tend to recall itch and dryness more often than experienced (positive bias of less than 1 day), but less often for the other symptoms (bleeding, cracking, flaking, oozing/weeping and sleep disturbance; negative bias ranging 1-4 days). CONCLUSIONS: In this clinical trial data set, we found that patients tended to slightly underestimate their symptoms when reporting POEM, with significant variation in how well they were able to recall the frequency of their symptoms every time they reported POEM. A large recall noise should be taken into consideration when interpreting POEM scores.

Data-driven research on eczema: Systematic characterization of the field and recommendations for the future.

Background: The past decade has seen a substantial rise in the employment of modern data-driven methods to study atopic dermatitis (AD)/eczema. The objective of this study is to summarise the past and future of data-driven AD research, and identify areas in the field that would benefit from the application of these methods. Methods: We retrieved the publications that applied multivariate statistics (MS), artificial intelligence (AI, including machine learning-ML), and Bayesian statistics (BS) to AD and eczema research from the SCOPUS database over the last 50 years. We conducted a bibliometric analysis to highlight the publication trends and conceptual knowledge structure of the field, and applied topic modelling to retrieve the key topics in the literature. Results: Five key themes of data-driven research on AD and eczema were identified: (1) allergic co-morbidities, (2) image analysis and classification, (3) disaggregation, (4) quality of life and treatment response, and (5) risk factors and prevalence. ML&AI methods mapped to studies investigating quality of life, prevalence, risk factors, allergic co-morbidities and disaggregation of AD/eczema, but seldom in studies of therapies. MS was employed evenly between the topics, particularly in studies on risk factors and prevalence. BS was focused on three key topics: treatment, risk factors and allergy. The use of AD or eczema terms was not uniform, with studies applying ML&AI methods using the term eczema more often. Within MS, papers using cluster and factor analysis were often only identified with the term AD. In contrast, those using logistic regression and latent class/transition models were "eczema" papers. Conclusions: Research areas that could benefit from the application of data-driven methods include the study of the pathogenesis of the condition and related risk factors, its disaggregation into validated subtypes, and personalised severity management and prognosis. We highlight BS as a new and promising approach in AD and eczema research.