A new spirometry-based algorithm to predict occupational pulmonary restrictive impairment
File(s)
Accepted version
Author(s)
De Matteis, S
Cullinan, Paul
Type
Journal Article
Abstract
Background Spirometry is often included in workplace-based respiratory surveillance programmes but its performance in the identification of restrictive lung disease is poor, especially when the prevalence of this condition is low in the tested population.
Aims To improve the specificity (Sp) and positive predictive value (PPV) of current spirometry-based algorithms in the diagnosis of restrictive pulmonary impairment in the workplace and to reduce the proportion of false positives findings and, as a result, unnecessary referrals for lung volume measurements.
Methods We re-analysed two studies of hospital patients, respectively used to derive and validate a recommended spirometry-based algorithm [forced vital capacity (FVC) < 85% predicted and forced expiratory volume in 1 s (FEV1)/FVC > 55%] for the recognition of restrictive pulmonary impairment. We used true lung restrictive cases as a reference standard in 2×2 contingency tables to estimate sensitivity (Sn), Sp and PPV and negative predictive values for each diagnostic cut-off. We simulated a working population aged <65 years and with a disease prevalence ranging 1–10% and compared our best algorithm with those previously reported using receiver operating characteristic curves.
Results There were 376 patients available from the two studies for inclusion. Our best algorithm (FVC < 70% predicted and FEV1/FVC ≥ 70%) achieved the highest Sp (96%) and PPV (67 and 15% for a disease prevalence of 10 and 1%, respectively) with the lowest proportion of false positives (4%); its high Sn (71%) predicted the highest proportion of correctly classified restrictive cases (91%).
Conclusions Our new spirometry-based algorithm may be adopted to accurately exclude pulmonary restriction and to possibly reduce unnecessary lung volume testing in an occupational health setting.
Aims To improve the specificity (Sp) and positive predictive value (PPV) of current spirometry-based algorithms in the diagnosis of restrictive pulmonary impairment in the workplace and to reduce the proportion of false positives findings and, as a result, unnecessary referrals for lung volume measurements.
Methods We re-analysed two studies of hospital patients, respectively used to derive and validate a recommended spirometry-based algorithm [forced vital capacity (FVC) < 85% predicted and forced expiratory volume in 1 s (FEV1)/FVC > 55%] for the recognition of restrictive pulmonary impairment. We used true lung restrictive cases as a reference standard in 2×2 contingency tables to estimate sensitivity (Sn), Sp and PPV and negative predictive values for each diagnostic cut-off. We simulated a working population aged <65 years and with a disease prevalence ranging 1–10% and compared our best algorithm with those previously reported using receiver operating characteristic curves.
Results There were 376 patients available from the two studies for inclusion. Our best algorithm (FVC < 70% predicted and FEV1/FVC ≥ 70%) achieved the highest Sp (96%) and PPV (67 and 15% for a disease prevalence of 10 and 1%, respectively) with the lowest proportion of false positives (4%); its high Sn (71%) predicted the highest proportion of correctly classified restrictive cases (91%).
Conclusions Our new spirometry-based algorithm may be adopted to accurately exclude pulmonary restriction and to possibly reduce unnecessary lung volume testing in an occupational health setting.
Date Issued
2016-01
Date Acceptance
2015-06-05
Citation
Occupational Medicine-Oxford, 2016, 66 (1), pp.50-53
ISSN
1471-8405
Publisher
Oxford University Press (OUP)
Start Page
50
End Page
53
Journal / Book Title
Occupational Medicine-Oxford
Volume
66
Issue
1
Copyright Statement
© The Author 2015. This is a pre-copyedited, author-produced PDF of an article accepted for publication in Occupational Medicine-Oxford following peer review. The version of record Occupational Medicine 2016;66:50–53 is available online at: http://occmed.oxfordjournals.org/content/66/1/50.full.pdf+html
Subjects
Diagnostic algorithm
occupational health
restrictive lung pattern
spirometry.
Environmental & Occupational Health
1117 Public Health And Health Services
Publication Status
Published
Date Publish Online
2015-10-13