A new spirometry-based algorithm to predict occupational pulmonary restrictive impairment.
Occup Med (Lond)
; 66(1): 50-3, 2016 Jan.
Article
em En
| MEDLINE
| ID: mdl-26464478
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.Palavras-chave
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Base de dados:
MEDLINE
Assunto principal:
Espirometria
/
Algoritmos
/
Pulmão
/
Pneumopatias
/
Doenças Profissionais
Idioma:
En
Ano de publicação:
2016
Tipo de documento:
Article