Machine learning prediction of asthma and allergic rhinitis in children with early-onset atopic dermatitis.

Abstract

Early-onset atopic dermatitis (AD) is a known precursor to respiratory atopic diseases, but identifying which children will develop persistent moderate-to-severe asthma and allergic rhinitis at school age remains difficult. We sought to develop and validate machine learning models that predict individualized risk for moderate-to-severe persistent asthma and allergic rhinitis in children diagnosed with AD before age 3. We conducted a retrospective birth cohort study using longitudinal electronic health record data from Kaiser Permanente Southern California. Two prediction models were developed for each outcome (asthma and rhinitis) among children aged 5-11: a comprehensive electronic health records model using detailed, structured clinical variables; and a simplified clinical model that was based on fewer, routinely available clinical features. Model performance was evaluated by area under the curve (AUC), sensitivity, positive predictive value (PPV), and calibration across risk strata. Among 10,688 eligible children, asthma models demonstrated strong discrimination (AUC = 0.893 comprehensive; AUC = 0.892 simplified). At 95% specificity, the comprehensive model achieved 40.4% sensitivity and 39.3% PPV; the simplified model reached 36.2% sensitivity and 33.8% PPV. Rhinitis models showed moderate performance (AUC = 0.793 and AUC = 0.773); at 90% specificity, the comprehensive model achieved 35.5% sensitivity and 72.7% PPV, while the simplified model reached 34.0% sensitivity and 69.2% PPV. Calibration was acceptable, with strong agreement in the highest-risk groups. Machine-learning models using early-life clinical data can accurately stratify risk for moderate-to-severe persistent asthma and allergic rhinitis by school age, supporting proactive, individualized care.