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1.
Occup Environ Med ; 67(8): 562-7, 2010 Aug.
Article in English | MEDLINE | ID: mdl-20439321

ABSTRACT

INTRODUCTION: Serial peak expiratory flow (PEF) monitoring is a useful confirmatory test for occupational asthma diagnosis. As weekends off work may not be long enough for PEF records to recover, this study investigated whether including longer periods off work in PEF monitoring improves the sensitivity of occupational asthma diagnosis. METHODS: Serial PEF measurements from workers with occupational asthma and from workers not at work during their PEF record, containing minimum data amounts and at least one rest period with > or = 7 consecutive days off work, were analysed. Diagnostic sensitivity and specificity of the area between the curves (ABC) score from waking time and Oasys score for occupational asthma were calculated for each record by including only consecutive rest days 1-3 in any rest period, including only consecutive rest days from day 4 onwards in any rest period or including all available data. RESULTS: Analysing all available off work data (including periods away from work of > or = 7 days) increased the mean ABC score by 17% from 35.1 to 41.0 l/min/h (meaning a larger difference between rest and work day PEF values) (p=0.331) and the Oasys score from 3.2 to 3.3 (p=0.588). It improved the sensitivity of the ABC score for an occupational asthma diagnosis from 73% to 80% while maintaining specificity at 96%. The effect on the Oasys score using discriminant analysis was small (sensitivity changed from 85% to 88%). CONCLUSIONS: Sensitivity of PEF monitoring using the ABC score for the diagnosis of occupational asthma can be improved by having a longer period off work.


Subject(s)
Asthma/diagnosis , Occupational Diseases/diagnosis , Peak Expiratory Flow Rate/physiology , Rest/physiology , Absenteeism , Adult , Asthma/physiopathology , Female , Humans , Male , Middle Aged , Occupational Diseases/physiopathology , Risk Assessment , Time Factors
2.
Chest ; 135(2): 307-314, 2009 Feb.
Article in English | MEDLINE | ID: mdl-18812450

ABSTRACT

BACKGROUND: Evidence-based guidelines recommend serial measurements of peak expiratory flow (PEF) on days at and away from work as the first step in the objective confirmation of occupational asthma. The aim of this study was to improve the diagnostic value of computer-based PEF analysis by using the program Oasys-2 to calculate a score from the area between the curves (ABC) of PEF on days at and away from work. METHODS: Mean 2-hourly PEFs were plotted separately for workdays and rest days for 109 workers with occupational asthma and 117 control asthmatics. A score based on the ABC was computed from records containing >or= 4 day shifts, >or= 4 rest days, and >or= 6 readings per day. Patients were randomly classified into two data sets (analysis and test sets). Receiver operator characteristic (ROC) curve analysis determined a cutoff point from set 1 that best identified those with occupational asthma, which was then tested in set 2. RESULTS: Logistic regression analysis showed that all ABC PEF scores were significant predictors of occupational asthma, with the best being ABC per hour from waking (odds ratio, 11.9 per 10 L/h/min; 95% confidence interval, 10.8 to 13.1). ROC curve analysis showed that a difference of 15 L/min/h provided a high specificity without compromising sensitivity in diagnosing occupational asthma. Analysis of data set 2 confirmed a specificity of 100% and sensitivity of 72%. CONCLUSION: The ABC PEF score is sensitive and specific for the diagnosis of occupational asthma and can be calculated from a shorter PEF surveillance than is needed for the current Oasys-2 work effect index.


Subject(s)
Asthma/diagnosis , Occupational Diseases/diagnosis , Occupational Exposure/adverse effects , Peak Expiratory Flow Rate , Workplace/statistics & numerical data , Adult , Asthma/epidemiology , Case-Control Studies , Confidence Intervals , Female , Humans , Incidence , Logistic Models , Male , Middle Aged , Occupational Diseases/epidemiology , Odds Ratio , ROC Curve , Risk Assessment , Sensitivity and Specificity , Severity of Illness Index , Statistics, Nonparametric , Time Factors , Young Adult
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