Environmental and clinical data utility in pediatric asthma exacerbation risk prediction models

Abstract

Abstract Background Asthma exacerbations are triggered by a variety of clinical and environmental factors, but their relative impacts on exacerbation risk are unclear. There is a critical need to develop methods to identify children at high-risk for future exacerbation to allow targeted prevention measures. We sought to evaluate the utility of models using spatiotemporally resolved climatic data and individual electronic health records (EHR) in predicting pediatric asthma exacerbations. Methods We extracted retrospective EHR data for 5982 children with asthma who had an encounter within the Duke University Health System between January 1, 2014 and December 31, 2019. EHR data were linked to spatially resolved environmental data, and temporally resolved climate, pollution, allergen, and influenza case data. We used xgBoost to build predictive models of asthma exacerbation over 30–180 day time horizons, and evaluated the contributions of different data types to model performance. Results Models using readily available EHR data performed moderately well, as measured by the area under the receiver operating characteristic curve (AUC 0.730–0.742) over all three time horizons. Inclusion of spatial and temporal data did not significantly improve model performance. Generating a decision rule with a sensitivity of 70% produced a positive predictive value of 13.8% for 180 day outcomes but only 2.9% for 30 day outcomes. Conclusions EHR data-based models perform moderately wellover a 30–180 day time horizon to identify children who would benefit from asthma exacerbation prevention measures. Due to the low rate of exacerbations, longer-term models are likely to be most clinically useful. Trial Registration: Not applicable.

Keywords

• Asthma
• Pediatrics
• Predictive modeling
• Environmental data
• Machine learning