RESUMEN
BACKGROUND: The rise in prevalence of atopic dermatitis (AD) has been correlated with numerous elements of the exposome, modern-day lifestyle, and familial history. The combined analysis of familial history and other risk elements may allow us to understand the driving factors behind the development of AD. OBJECTIVE: To develop prediction models to assess the risk of developing AD using a large and diverse cohort (N = 77,525) and easily assessed risk factors. METHODS: We analyzed electronic medical record data from Leumit Health System. Documented predictive factors include sex, season of birth, environment (urban/rural), socioeconomic status, household smoking, diagnosed skin conditions, number of siblings, a paternal, maternal, or sibling history of an atopic condition, and antibiotic prescriptions during pregnancy or after birth. Predictive models were trained and validated on the data set. RESULTS: Medium (odds ratio [OR] 2.04, CI 1.92-2.17, P < .001) and high (OR 2.13, CI 1.95-2.34, P < .001) socioeconomic status, a previous diagnosis of contact dermatitis (OR 2.57, CI 2.37-2.78, P < .001), presence of siblings with an AD diagnosis (OR 2.21, CI 2.04-2.40, P < .001), and the percentage of siblings with any atopic condition (OR 2.58, CI 2.09-3.17, P < .001) drove risk for AD in a logistic regression model. A random forest prediction model with a sensitivity of 61% and a specificity of 84% was developed. Generalized mixed models accounting for the random effect of familial relationships boasted an area under the curve of 0.98. CONCLUSION: Predictive modeling using noninvasive and accessible inputs is a powerful tool to stratify risk for developing AD.
