{Reference Type}: Journal Article
{Title}: DEVELOPMENT OF A PREDICTIVE MODEL FOR PEDIATRIC ATOPIC DERMATITIS - A RETROSPECTIVE CROSS-SECTIONAL NATIONWIDE DATABASE STUDY.
{Author}: Landau T;Gamrasni K;Levin A;Barlev Y;Sanders O;Benor S;Brandwein M;
{Journal}: Ann Allergy Asthma Immunol
{Volume}: 0
{Issue}: 0
{Year}: 2024 Jun 18
{Factor}: 6.248
{DOI}: 10.1016/j.anai.2024.06.010
{Abstract}: <B>BACKGROUND: </B>The rise in prevalence of atopic dermatitis 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 atopic dermatitis.<BR><B>OBJECTIVE: </B>We aimed to develop prediction models to assess the risk of developing atopic dermatitis using a large and diverse cohort (N=77,525) and easily-assessed risk factors.<BR><B>METHODS: </B>We analyzed electronic medical record data from Leumit Health System. Documented predictive factors include sex, season of birth, environment (urban/rural), socio-economic 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 following birth. Predictive models were trained and validated on the dataset.<BR><B>RESULTS: </B>Medium (OR 2.04, CI 1.92-2.17, p<0.001) and high (OR 2.13, CI 1.95-2.34, p<0.001) socioeconomic status, a previous diagnosis of contact dermatitis (OR 2.57, CI 2.37-2.78, p<0.001), presence of siblings with an AD diagnosis (OR 2.21, CI 2.04-2.40, p<0.001) and the percentage of siblings with any atopic condition (OR 2.58, CI 2.09-3.17, p<0.001) 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.<BR><B>CONCLUSIONS: </B>Predictive modeling using non-invasive and accessible inputs is a powerful tool to stratify risk for developing atopic dermatitis.