Leveraging machine learning models to forecast pediatric allergic rhinitis exacerbation risk based on environmental exposure data.

OBJECTIVE: Acute exacerbations of pediatric allergic rhinitis (AR) are difficult to anticipate, and individualized risk tools integrating clinical and environmental data are lacking. We developed an interpretable machine-learning model to predict 90-day AR exacerbation risk in children and conducted a preliminary transportability assessment in an independent cohort, following TRIPOD + AI guidelines. METHODS: A development cohort (n = 2,000) was assembled by linking NHANES (2005-2006 and 2011-2018 cycles) with EPA Air Quality System and NOAA meteorological data; an independent external cohort (n = 50) was recruited at Wuhan Children's Hospital (2023-2024). Clinical predictors [Total Nasal Symptom Score (TNSS), total IgE, eosinophils, comorbidities] and environmental exposures (same-day and 0-7-day-lag PM2.5, PM10, O3, NO2, temperature, humidity) were analyzed. L2-regularized logistic regression (LR) was prespecified as the primary model and benchmarked against random forest, XGBoost, LightGBM, and SVM using nested 5-fold cross-validation and multiple imputation (m = 5). Discrimination, calibration, decision-curve analysis, and SHAP interpretability were evaluated. RESULTS: Exacerbation occurred in 34.4% (687/2,000) of the development cohort and 36.0% (18/50) of the external cohort. The primary LR model achieved an internal AUC of 0.81 (95% CI: 0.74-0.88) and an external AUC of 0.71 (95% CI: 0.50-0.92); the wide confidence interval reflects the limited precision of the small validation sample. Baseline TNSS, same-day PM2.5, and total IgE were the most influential predictors across feature-selection and SHAP analyses. Children exposed to PM2.5 > 75 μg/m3 had approximately five-fold higher exacerbation odds than those exposed to <35 μg/m3 (OR = 5.08, 95% CI: 3.45-7.47). A sensitivity model excluding TNSS retained moderate discrimination (external AUC 0.66, 95% CI: 0.49-0.83), supporting the independent contribution of environmental and immunologic factors. Decision-curve analysis showed positive net benefit across threshold probabilities of 0.10-0.35. CONCLUSION: An interpretable LR model integrating baseline symptom burden with ambient PM2.5 exposure demonstrated promising predictive performance for pediatric AR exacerbation. However, the small external cohort (n = 50) yielded wide confidence intervals, and findings should be regarded as preliminary transportability evidence rather than definitive external validity. Larger multicenter prospective studies with site-specific recalibration are required before clinical implementation.