Evidence map›Paper›PMID 40459987›Full record

ArticleThe Journal of asthma : official journal of the Association for the Care of Asthma2025

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.

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

Abstract readMulticenter Study
In one paragraph

Article in The Journal of asthma : official journal of the Association for the Care of Asthma, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

6 authors.

Jinqian PanDepartment of Health Outcomes and Biomedical Informatics, University of Florida College of Medicine, Gainesville, Florida, USA.
Jie XuDepartment of Health Outcomes and Biomedical Informatics, University of Florida College of Medicine, Gainesville, Florida, USA.
David FedeleNemours Center for Healthcare Delivery Science, Jacksonville, Florida, USA.
Yonghui WuDepartment of Health Outcomes and Biomedical Informatics, University of Florida College of Medicine, Gainesville, Florida, USA.
Jennifer BrailsfordCenter for Data Solutions, University of Florida College of Medicine, Jacksonville, Florida, USA.
Jennifer FisheCenter for Data Solutions, University of Florida College of Medicine, Jacksonville, Florida, USA.

Funding

Identifying pediatric asthma subtypes using novel privacy-preserving federated machine learning methodsR01HL169277 · NHLBI · UNIVERSITY OF FLORIDA · PI Jennifer Noel Fishe, Jie Xu · 2023 to 2026
$2.7M
NHLBI NIH HHS R01 HL169277
6 · The paper itself

Abstract

objectiveDevelopment 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.

methodsThis 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.

resultsWe 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

conclusionsThis 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.

Indexed as

Anti-Asthmatic AgentsAsthmaElectronic Health RecordsAdolescentChildChild, PreschoolDrug PrescriptionsFemaleHumansMalePractice Guidelines as TopicRetrospective StudiesSeverity of Illness IndexUnited StatesAnti-Asthmatic Agentsinhaled corticosteroidsnational asthma education and prevention programPediatric asthma

Identifiers

PMID40459987
PMCPMC12287910

What Socratic holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.