Biomarker underuse contributes to an inability to phenotype patients with severe uncontrolled asthma

Abstract

Background: Biomarker measurements improve the phenotyping of patients with severe uncontrolled asthma (SUA) and predict therapeutic responses. The use of biomarkers in asthma, however, remains underused.Objective: To test the hypothesis that biomarker measurements of patients with SUA remain markedly underused and contributes to asthma morbidity and oral corticosteroid use.Methods: Leveraging claims data linked to electronic health record data, we calculated biomarker use by providers treatingpatients with SUA from January 2017 to August 2020.Results: From 3.6 million clients, 3817 had a primary diagnosis of asthma; most were between 50 and 60 years old. Also, 63.2% were female patients; those under ages 10 years were primarily boys. Of the 728 patients who reported race, 69.9% were white and 21.8% were African American. Of the 840 who reported ethnicity, 14% were Latinx. A predetermined definition of SUA identified 348 patients with SUA. In a nested sample of 151 patients with SUA, 43% were managed by primary care physicians (PCP), 4% by specialists, and 49.7% by both. Of this sample, 61.5% had a measurement of serum eosinophils, 9.9% total immunoglobulin E values, and 9.3% radioallergosorbent skin tests; 38% received no tests, whereas 9.9% had more than one. Specialists ordered a biomarker test 4.6 times more often than did PCPs, whereas PCPs ordered 70% of the prednisone prescriptions for recurrent asthma exacerbations.Conclusion: Specialists were more likely to order biomarkers than were PCPs. Patients managed exclusively by PCPs were more likely prescribed oral prednisone. Real-world evidence shows that biomarkers are infrequently used to characterize patients with SUA, especially among patients exclusively managed by PCPs. Programs that encouraged biomarker use may improve SUA management and oral corticosteroid burden.

Validity of ICD‐10‐based algorithms to identify patients with influenza in inpatient and outpatient settings

“…Outpatient EHR data included data from the emergency department, nonemergency hospital‐based settings (e.g., outpatient surgery unit), and a large multispecialty healthcare group within the network. Linked data from these two data sources have previously been used 9 . Claims and EHR data were probabilistically linked to identify subjects within both data sources (Match*Pro v0.45, Rockville, Maryland) using date of birth as a blocking parameter and, as matching variables, gender and the first five characters of first and last names, respectively 10 …”

“…Linked data from these two data sources have previously been used. 9 Claims and EHR data were probabilistically linked to identify subjects within both data sources (Match*Pro v0.45, Rockville, Maryland) using date of birth as a blocking parameter and, as matching variables, gender and the first five characters of first and last names, respectively. 10 Eligible subjects were aged ≥1 year with documented testing for influenza and available results.…”