Determination of pediatric asthma severity using retrospective electronic health record prescription data: an analysis before and after the 2020 national asthma education and prevention program guideline update

Pan, Jinqian;Xu, Jie;Fedele, David;Wu, Yonghui;Brailsford, Jennifer;Fishe, Jennifer

Description

Development of a feasible, accurate method to assess pediatric asthma severity would benefit clinical care and research. This study sought to determine pediatric asthma severity from retrospective electronic health record prescription data, and to compare severities before and after the 2020 National Asthma Education and Prevention Program (NAEPP) guideline update. This retrospective analysis included patients ages 2 – 18 years of age from a large, multi-center healthcare provider. We assessed severity by mapping asthma-related medication prescriptions with NAEPP step-up therapy guidelines. We compared severities between the 2007 NAEPP guideline (2011-2020) and 2020 NAEPP guideline (2021-2023) periods. We identified 55,818 pediatric asthma patients with a median age of 7.5 years. Overall, were either mild intermittent (41.6%) or mild persistent severity (34.9%), followed by 9.7% moderate persistent and 4.8% severe persistent severity. From the 2007 to 2020 NAEPP period, there was a ∼5% decrease in mild persistent and moderate persistent severity, and a large increase in undefined severity from 7.0% to 19.6% after the 2020 NAEPP update. This study’s approach using outpatient medication prescription data to determine pediatric asthma severity according to NAEPP guidelines classified over 90% of patients. After the 2020 NAEPP guideline update, we found decreases in the proportion of children having mild and moderate persistent severity, and a large increase in patients for whom severity could not be determined due to off-label prescriptions. Mapping pediatric asthma severity through medication prescriptions is feasible, but further work is required to understand off-label prescribing patterns in pediatric asthma.

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Mentions (0)

Metrics

Dataset Index

0.8

FAIR Score

85%

Citations

1

Mentions

0

Metrics Over Time

Publication Details

DOI

Publisher

Taylor & Francis

License

Creative Commons Attribution 4.0 International

Assigned Domain

Subfield

Geriatrics and Gerontology

Field

Medicine

Domain

Health Sciences

Confidence Score

51%

Source

Scholar Data Model

Keywords

MedicineMicrobiologyFOS: Biological sciencesGeneticsMolecular BiologyPharmacologyBiotechnologyImmunologyFOS: Clinical medicineScience PolicyMental Health

Normalization Factors

FT

65.38

CTw

1.00

MTw

1.00