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  4. Diagnosis of asthma in symptomatic children based on measures of lung function: an analysis of data from a population-based birth cohort study.
 
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Diagnosis of asthma in symptomatic children based on measures of lung function: an analysis of data from a population-based birth cohort study.
File(s)
Lancet CAM Diagnostic Algorithm.pdf (620.49 KB)
Published version
Author(s)
Murray, Clare
Foden, Philip
Lowe, Lesley
Durrington, Hannah
Custovic, Adnan
more
Type
Journal Article
Abstract
BACKGROUND: Concerns have been expressed about asthma overdiagnosis. The UK National Institute of Health and Care Excellence (NICE) proposed a new diagnostic algorithm applying four lung function measures sequentially (ratio of forced expiratory volume in 1 s [FEV1] to forced vital capacity [FVC] <70%, bronchodilator reversibility ≥12%, fractional exhaled nitric oxide [FeNO] ≥35 parts per billion, and peak expiratory flow variability >20%). We aimed to assess the diagnostic value of three of the tests individually, and then test the proposed algorithm in symptomatic children. METHODS: We used follow-up data at age 13-16 years from the Manchester Asthma and Allergy Study, a prospective, population-based, birth cohort study. We initially present results for the whole population, then by subgroup of disease. To simulate the situation in primary care, we included participants reporting symptoms of wheeze, cough, or breathlessness in the previous 12 months and who were not on regular inhaled corticosteroids. We used an epidemiological definition of current asthma, defined as all three of physician-diagnosed asthma, current wheeze, and current use of asthma treatment, reported by parents in a validated questionnaire. We assigned children with negative answers to all three questions as non-asthmatic controls. We also measured spirometry, bronchodilator reversibility, and FeNO at follow-up; data for peak expiratory flow variability were not available. We calculated the proportion of participants with a current positive lung function test at each step of the algorithm, and recorded the number of participants that met our definition of asthma. FINDINGS: Of 1184 children born into the cohort, 772 attended follow-up at age 13-16 years between July 22, 2011, and Nov 11, 2014. Among 630 children who completed spirometry, FEV1:FVC was less than 70% in ten (2%) children, of whom only two (20%) had current asthma. Bronchodilator reversibility was positive in 54 (9%) of 624 children, of whom only 12 (22%) had current asthma. FeNO was 35 or more parts per billion in 115 (24%) of 485 children, of whom 29 (25%) had current asthma. Only four of 56 children with current asthma had positive results for all three tests (spirometry, bronchodilator reversibility, and FeNO). Conversely, 24 (43%) of the 56 children with current asthma were negative on all three tests. FEV1:fvc (p=0·0075) and FeNO (p<0·0001), but not bronchodilator reversibility (p=0·97), were independently associated with asthma in multivariable logistic regression models. Among children who reported recent symptoms, the diagnostic accuracy of the algorithm was poor. INTERPRETATION: Our findings challenge the proposed cutoff values for spirometry, the order in which the lung function tests are done, and the position of bronchodilator reversibility within the algorithm sequence. Until better evidence is available, the proposed NICE algorithm on asthma diagnosis should not be implemented in children. FUNDING: UK Medical Research Council.
Date Issued
2017-07-12
Date Acceptance
2017-07-12
Citation
Lancet Child & Adolescent Health, 2017, 1 (2), pp.114-123
URI
http://hdl.handle.net/10044/1/55099
DOI
https://www.dx.doi.org/10.1016/S2352-4642(17)30008-1
ISSN
2352-4642
Publisher
Elsevier
Start Page
114
End Page
123
Journal / Book Title
Lancet Child & Adolescent Health
Volume
1
Issue
2
Copyright Statement
© The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license.
License URL
http://creativecommons.org/licenses/by/4.0/
Sponsor
Medical Research Council (MRC)
Identifier
PII: S2352-4642(17)30008-1
Grant Number
MR/K002449/1
Publication Status
Published
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