title: A new spirometry-based algorithm to predict occupational pulmonary restrictive impairment.
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.
  
  
  OBJECTIVE
  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.
authors.affiliation: Department of Respiratory Epidemiology, Occupational Medicine and Public Health, Imperial College London, London SW3 6LR, UK, s.de-matteis@imperial.ac.uk.
authors.forename: S
authors.initials: S
authors.lastname: De Matteis
authors.affiliation: Respiratory Unit, Navarra Hospital Complex, Pamplona 31008, Spain.
authors.forename: A A
authors.initials: AA
authors.lastname: Iridoy-Zulet
authors.affiliation: Department of Respiratory Medicine, The Ottawa Hospital, Ottawa, Ontario K1H 8L6, Canada.
authors.forename: S
authors.initials: S
authors.lastname: Aaron
authors.affiliation: Occupational Health Service, Imperial College London, London SW7 2AZ, UK.
authors.forename: A
authors.initials: A
authors.lastname: Swann
authors.affiliation: Department of Respiratory Epidemiology, Occupational Medicine and Public Health, Imperial College London, London SW3 6LR, UK.
authors.forename: P
authors.initials: P
authors.lastname: Cullinan
mesh_terms: D000328:Adult; D000465:Algorithms; D005260:Female; D005541:Forced Expiratory Volume; D006801:Humans; D008168:Lung / Q000503:physiopathology*; D008171:Lung Diseases / Q000503:physiopathology*; D008297:Male; D008875:Middle Aged; D009784:Occupational Diseases / Q000503:physiopathology*; D011237:Predictive Value of Tests; D012372:ROC Curve; D013147:Spirometry / Q000592:standards*; D014797:Vital Capacity
keywords: Diagnostic algorithm; occupational health; restrictive lung pattern; spirometry.