Clinical profile of predefined asthma phenotypes in a large cohort of UK primary care patients (Clinical Practice Research Datalink)

Abstract

Background

Distinct asthma phenotypes have previously been suggested, including benign asthma, atopic asthma and obese non-eosinophilic asthma. This study aims to establish if these phenotypes can be identified using data recorded in primary care clinical records and reports on patient characteristics and exacerbation frequency.

Methods

A population-based cohort study identified 193,999 asthma patients in UK primary care from 2007 to 2017. We used linked primary and secondary care data from the Clinical Practice Research Datalink, Hospital Episode Statistics and Office for National Statistics. Patients were classified into predefined phenotypes or included in an asthma “not otherwise specified” (NOS) group. We used negative binomial regression to calculate the exacerbation rates and adjusted rate ratios. Rate ratios were further stratified by asthma treatment step.

Results

In our cohort, 3.9% of patients were categorized as benign asthma, 28.6% atopic asthma and 4.8% obese non-eosinophilic asthma. About 62.7% of patients were asthma NOS, including asthma NOS without treatment (10.4%), only on short-acting beta agonist (6.1%) and on maintenance treatment (46.2%). Crude severe exacerbation rates per 1,000 person-years were lowest for benign asthma (106.8 [95% CI: 101.2–112.3]) and highest for obese non-eosinophilic asthma (469.0 [451.7–486.2]). Incidence rate ratios for all phenotype groups decreased when stratified by treatment step but remained raised compared to benign asthma.

Conclusion

Established phenotypes can be identified in a general asthma population, although many patients did not fit into the specific phenotypes which we studied. Phenotyping patients and knowledge of asthma treatment step could help anticipate clinical course and therefore could aid clinical management but is only possible in a minority of primary care patients based on current phenotypes and electronic health records (EHRs).

Keywords:

• asthma
• phenotypes
• primary health care
• electronic health records
• epidemiology
• phenotyping

Author contributions

All authors contributed to data analysis, drafting and revising the article, gave final approval of the version to be published, and agree to be accountable for all aspects of the work.

Disclosure

FN is funded by a GlaxoSmithKline (GSK) scholarship during his PhD program. IJD is funded by an unrestricted grant from, has consulted for, and holds stock in GSK. HM is an employee of GSK R&D and owns shares of GSK. JKQ’s research group has received funding from The Health Foundation, Medical Research Council, Wellcome Trust, British Lung Foundation, GSK, Insmed, AstraZeneca (AZ), Bayer and Boehringer Ingelheim (BI) for other projects, none of which relate to this work. JKQ has received funds from AZ, GSK, Chiesi, Teva and BI for Advisory board participation or travel. The authors report no other conflicts of interest in this work.