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COMPAC: COMputable Phenotype for Asthma in Children

Jennifer Fishe, Jinqian Pan, David Fedele

    Research Square
    |May 19, 2025
    PubMed

    Insights

    A new computable phenotype for asthma in children, COMPAC, improves pediatric asthma identification in electronic health records. COMPAC shows higher accuracy than existing methods, aiding research and clinical care.

    Area of Science:

    • Biomedical Informatics
    • Clinical Informatics
    • Pediatric Pulmonology

    Background:

    • Pediatric asthma is a prevalent chronic childhood illness.
    • Accurate identification of pediatric asthma patients in electronic health records (EHRs) is crucial for research and clinical practice.
    • Existing computable phenotypes (CPs) for asthma identification in EHRs have limitations in effectiveness.

    Purpose of the Study:

    • To evaluate the effectiveness of current CPs for identifying pediatric asthma.
    • To develop and validate a novel CP, named COMPAC (COMputable Phenotype for Asthma in Children), for improved EHR-based identification of pediatric asthma.

    Main Methods:

    • Developed multiple CP rules using diagnosis codes, prescriptions, and clinical note text.
    • Validated CPs using a cohort from the University of Florida Integrated Data Repository via manual chart reviews.
    • Assessed performance using standard metrics and compared COMPAC against existing CPs, including subgroup analyses.

    Main Results:

    • COMPAC demonstrated superior case identification compared to existing CPs.
    • Achieved high sensitivity (0.728) and positive predictive value (0.886), with an F1 score of 0.797.
    • Outperformed two prior CPs in F1 score; performance varied across demographic subgroups.

    Conclusions:

    • COMPAC provides an enhanced method for identifying pediatric asthma in EHRs.
    • Further multi-site validation and refinement are needed to optimize COMPAC's sensitivity and specificity.
    • The study highlights the need for improved tools for pediatric asthma phenotyping in clinical data.
    Abstract

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