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A computational phenotype for pediatric asthma exacerbations requiring hospitalization using electronic health record

Colin Rogerson1,2, Danielle Severns1, Jason Stemple1

  • 1Department of Pediatrics, Indiana University School of Medicine, Indianapolis, IN, 46202, United States.

JAMIA Open
|June 24, 2026
PubMed

Insights

We developed a computational phenotype to accurately identify pediatric asthma exacerbations requiring hospitalization using electronic health records. This tool improves patient selection for research studies.

Area of Science:

  • Pediatric Pulmonology
  • Health Informatics
  • Clinical Epidemiology

Background:

  • Computational phenotypes enhance patient selection for observational studies.
  • Accurate identification of pediatric asthma exacerbations is crucial for research.

Purpose of the Study:

  • To derive and validate a computational phenotype for pediatric asthma exacerbation requiring hospitalization.
  • To improve the accuracy of identifying relevant patient cohorts from electronic health records.

Main Methods:

  • Retrospective cohort study utilizing electronic health record (EHR) data.
  • Iterative development and refinement of phenotype criteria based on diagnostic codes and medication data.
  • Validation of positive predictive value (PPV) through manual chart review and sensitivity analysis against an established database.

Main Results:

  • The final computational phenotype achieved a 93% positive predictive value (PPV) for pediatric asthma exacerbation requiring hospitalization.
  • Compared to using only diagnostic codes (34% PPV), the developed phenotype significantly improved accuracy.
  • The phenotype demonstrated high sensitivity (97%) when applied to a critical care cohort.

Conclusions:

  • A validated computational phenotype using EHR data can effectively identify pediatric asthma exacerbations needing hospitalization.
  • This phenotype offers a valuable tool for future observational studies in pediatric asthma.
  • Potential need for institution-specific adjustments to the phenotype for optimal performance.
Abstract

Keywords:
asthmaclinical researchinformaticspediatrics

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