Dataset Information

Impact of environmental factors in predicting daily severity scores of atopic dermatitis.

ABSTRACT:

Background

Atopic dermatitis (AD) is a chronic inflammatory skin disease that affects 20% of children worldwide. Environmental factors including weather and air pollutants have been shown to be associated with AD symptoms. However, the time-dependent nature of such a relationship has not been adequately investigated. This paper aims to assess whether real-time data on weather and air pollutants can make short-term prediction of AD severity scores.

Methods

Using longitudinal data from a published panel study of 177 paediatric patients followed up daily for 17 months, we developed a statistical machine learning model to predict daily AD severity scores for individual study participants. Exposures consisted of daily meteorological variables and concentrations of air pollutants, and outcomes were daily recordings of scores for six AD signs. We developed a mixed-effect autoregressive ordinal logistic regression model, validated it in a forward-chaining setting and evaluated the effects of the environmental factors on the predictive performance.

Results

Our model successfully made daily prediction of the AD severity scores, and the predictive performance was not improved by the addition of measured environmental factors. Potential short-term influence of environmental exposures on daily AD severity scores was outweighed by the underlying persistence of preceding scores.

Conclusions

Our data does not offer enough evidence to support a claim that weather or air pollutants can make short-term prediction of AD signs. Inferences about the magnitude of the effect of environmental factors on AD severity scores require consideration of their time-dependent dynamic nature.

SUBMITTER: Hurault G

PROVIDER: S-EPMC8099209 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundAtopic dermatitis (AD) is a chronic inflammatory skin condition with a highly variable clinical phenotype.ObjectiveThis study aimed to identify historical and clinical features and biomarkers associated with AD severity.MethodsA US registry of extensively phenotyped AD participants (aged 0.73-80 years) were enrolled at 9 academic centers. Information on family and personal medical history, examination, skin swabs (culture), and serum biomarkers was collected to evaluate their association with AD severity.ResultsParticipants with AD (N = 2862) whose disease was categorized as mild (11.6%), moderate (58.0%), or severe (30.4%) based on Rajka-Langeland scoring were enrolled. The trend test, when adjusting for gender, race, and age, demonstrated that severity was strongly (P ≤ .04) associated with a personal/family history of allergic disorders, history of alopecia, exposure to passive smoke, ocular herpes infection, skin bacterial and viral infections, and history of arrhythmia. Features observed more frequently (P ≤ .002), as a function of severity, included skin infections (impetigo, human papillomavirus, and molluscum contagiosum virus), Staphylococcus aureus colonization, excoriations, hyperlinear palms, ichthyosis, blepharitis, conjunctivitis, ectropion, and wheezing. Serum IgE, allergen and food (≤6 years) Phadiatop, and eosinophilia were strongly linked to severity (P < .001).ConclusionsIn a diverse US AD population, severity was associated with a history of atopic disorders, skin and extracutaneous bacterial and viral infections (by history and physical examination), higher IgE, eosinophilia and allergen sensitization, atopic skin manifestations (ie, excoriation, hyperlinear palms, and ichthyosis), and atopic ocular features (ie, blepharitis, conjunctivitis, and ectropion) as well as asthma findings (ie, wheezing). Data from our prospective registry significantly advance our understanding of AD phenotypes and endotypes, which is critical to achieve optimal management.

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