Developing and evaluating a pediatric asthma severity computable phenotype derived from electronic health records
| Autor: | Chanmin Kim, Komal Peer, M. Patricia Fabian, Aaron Legler, Renée Boynton-Jarrett, Jessica H. Leibler, William G. Adams, Jonathan I. Levy, Megan Sandel |
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| Rok vydání: | 2021 |
| Předmět: |
medicine.medical_specialty
Concordance media_common.quotation_subject Immunology macromolecular substances Logistic regression Severity of Illness Index Article 03 medical and health sciences 0302 clinical medicine Electronic Health Records Humans Immunology and Allergy Medicine 030212 general & internal medicine Child Intensive care medicine Pediatric asthma Asthma media_common Selection bias business.industry Age Factors ICD-10 medicine.disease Phenotype Respiratory Function Tests respiratory tract diseases Patient Health Questionnaire Logistic Models 030228 respiratory system business |
| Zdroj: | J Allergy Clin Immunol |
| ISSN: | 0091-6749 |
| Popis: | BACKGROUND: Extensive data available in electronic health records (EHR) have the potential to improve asthma care and understanding of factors influencing asthma outcomes. However, this work can only be accomplished when the EHR data allow for accurate measures of severity, which at present are complex and inconsistent. OBJECTIVE: To create and evaluate a standardized pediatric asthma severity phenotype based in clinical asthma guidelines for use in EHR-based health initiatives and studies; and to examine the presence and absence of these data in relation to patient characteristics. METHODS: We developed an asthma severity computable phenotype and compared the concordance of different severity components contributing to the phenotype to trends in the literature. We used multivariable logistic regression to assess the presence of EHR data relevant to asthma severity. RESULTS: The asthma severity computable phenotype performs as expected in comparison to national statistics and the literature. Severity classification for a child is maximized when based on the long-term medication regimen component and minimized when based only on the symptom data component. Use of the severity phenotype results in better, clinically-grounded classification. Children for whom severity could be ascertained from these EHR data were more likely to be seen for asthma in the outpatient setting, and less likely to be older or Hispanic. Black children were less likely to have lung function testing data present. CONCLUSION: We developed a pragmatic computable phenotype for pediatric asthma severity that is transportable to other EHRs. CLINICAL IMPLICATION: Ascertainment of asthma severity from the EHR is viable but depends on intensity of patient engagement in healthcare and demographic characteristics. CAPSULE SUMMARY: We present a pragmatic approach to use electronic health record (EHR) data to classify pediatric asthma severity. |
| Databáze: | OpenAIRE |
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