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

Authors Nissen F, Douglas IJ, Müllerová H, Pearce N, Bloom CI, Smeeth L, Quint JK

Received 2 August 2018

Accepted for publication 3 November 2018

Published 8 January 2019 Volume 2019:12 Pages 7—19


Checked for plagiarism Yes

Review by Single anonymous peer review

Peer reviewer comments 2

Editor who approved publication: Dr Amrita Dosanjh

Francis Nissen,1 Ian J Douglas,1 Hana Müllerová,2 Neil Pearce,1 Chloe I Bloom,3 Liam Smeeth,1 Jennifer K Quint3

1Department of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK; 2RWD & Epidemiology, GSK R&D, Uxbridge, UK; 3National Heart and Lung Institute, Imperial College, London, UK

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).

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

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