COMPAC: COMputable Phenotype for Asthma in Children

Background Pediatric asthma is one of the most common chronic diseases of childhood. Reliable identification of pediatric asthma patients in electronic health records (EHRs) is essential for both research and clinical care. However, existing computable phenotypes (CPs) exhibit varying effectiveness. This study aims to evaluate current CPs and develop a new CP, named COMPAC (COMputable Phenotype for Asthma in Children), to improve EHR-based identification of pediatric asthma patients. Methods Multiple CP rules were designed using various combinations of diagnosis codes, prescriptions, and clinical note text. A cohort from the University of Florida Integrated Data Repository (IDR) was used for validation through manual chart reviews. Performance was assessed using standard metrics and compared to existing CPs. Additionally, bootstrapping and demographic subgroup analyses were conducted to compare the performance of the new COMPAC to previously published CPs. Results COMPAC demonstrated improved case identification compared to existing CPs, with high sensitivity (0.728; 95% confidence interval [CI]: 0.607–0.864), positive predictive value (0.886; 95% CI: 0.737–1.0), and an overall F1 score of 0.797 (95% CI: 0.682–0.90). Notably, COMPAC outperformed two previously published CPs in terms of F1 score. Performance varied across demographic subgroups, with COMPAC showing the best results in males, non-Hispanic Whites, and the 6–12 year-old age group, though its performance was lower in the 2–5 year-old age range. Conclusion COMPAC offers an improved approach for pediatric asthma case identification in EHRs. However, further validation across different sites and refinement to capture a broader range of clinical presentations are necessary to optimize its sensitivity and specificity.