Published online: December 15, 2020
Data available in electronic health records (EHR) have the potential to improve asthma care and research. However, this work can only be accomplished when EHR data allow for accurate measures of severity, which at present are complex and inconsistent. Computable phenotyping is a practice that can be used to standardize and ready EHR data for research purposes.
In an interdisciplinary study recently published in The Journal of Allergy and Clinical Immunology (JACI), Peer et al. created a standardized pediatric asthma severity computable phenotype based in the National Heart, Lung, and Blood Institute (NHLBI) clinical asthma guidelines for use in EHR-based health initiatives and studies. Severity was defined as the worst severity observed based on any of the following EHR data elements in a given year for pediatric patients: long-term medication regimen, ICD-10 diagnosis codes, lung function test (FEV1%), and asthma symptoms. The authors assessed agreement between different EHR data elements contributing to the computable phenotype. Since data in the EHR may not reflect the underlying disease that researchers are interested in studying, but rather the entirety of complex patient, clinician, and health system interactions and documentation (a relatively recent concept named informed presence), the authors then used multivariable logistic regression to understand for which patients could an asthma severity classification be ascertained and why.
The asthma severity computable phenotype performed as expected in comparison to national statistics and the literature. Severity classification for a child was more severe when based only on the long-term medication regimen component and less severe when based only on the symptom data component. The study demonstrated that use of the severity computable phenotype results in better, clinically-grounded classification. As anticipated, children for whom severity could be ascertained were characteristically different in terms of their healthcare use and data documented. Children for whom severity could be ascertained from 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.
The authors presented an approach through which “big data” from the EHR can contribute to pediatric asthma research and practice that is low-cost, standardized, leverages existing data, and does not add to clinician or clinical staff burden. For clinical practice, the strength of the asthma severity computable phenotype is that it circumvents issues related to inconsistent documentation of asthma severity components and the use of varying asthma severity definitions by clinicians, and it could be used for EHR-based decision support tools for population health management. The asthma severity computable phenotype is relevant for clinical research as an area of particular importance that EHR data may be ideally suited for is risk prediction of asthma exacerbations and deaths. The authors’ study of real-world EHR data uncovered racial disparities in childhood asthma healthcare utilization similar to those documented in the literature and national statistics; therefore, it is important to note that while asthma severity classification from the EHR is possible, it may depend on patient engagement in healthcare and their demographic characteristics.
The Journal of Allergy and Clinical Immunology (JACI) is an official scientific journal of the AAAAI, and is the most-cited journal in the field of allergy and clinical immunology.