Developing and evaluating a pediatric asthma severity computable phenotype derived from electronic health records

Area of Science:

• Health Informatics
• Pediatric Pulmonology
• Clinical Data Science

Background:

• Electronic Health Records (EHRs) contain extensive data valuable for improving asthma care and understanding outcomes.
• Current EHR data present challenges for accurate and consistent asthma severity measurement.
• Standardized measures are crucial for leveraging EHR data in asthma research and clinical initiatives.

Purpose of the Study:

• To develop and evaluate a standardized pediatric asthma severity phenotype using clinical guidelines for EHR-based studies.
• To assess the presence and characteristics of relevant EHR data in relation to patient attributes.

Main Methods:

• Developed an asthma severity computable phenotype based on clinical guidelines.
• Compared phenotype components to existing literature and national statistics.
• Utilized multivariable logistic regression to analyze the availability of EHR data pertinent to asthma severity.

Main Results:

• The computable phenotype aligns with national statistics and literature, providing clinically grounded asthma severity classification.
• Severity classification is most robust when incorporating long-term medication data, and less so with symptom data alone.
• Patient characteristics associated with ascertainable severity include outpatient visits, age, ethnicity, and race, with disparities in lung function data for Black children.

Conclusions:

• A pragmatic and transportable computable phenotype for pediatric asthma severity has been developed for use in various EHR systems.
• This standardized approach enhances the utility of EHR data for pediatric asthma research and care improvement.