References

1. The evaluation of an improved method of occupational asthma diagnosis from timepoint analysis of serial PEF records CBSG Burge1, VC Moore1, AS Robertson1, CFA Pantin2, PS Burge1 1Occupational Lung Disease Unit, Birmingham Heartlands Hospital, UK; 2North Staffordshire Hospital, UK Results Compared with independent diagnoses (challenges etc) • The diagnosis of Occupational asthma needs objective confirmation. • Serial measurements of PEF are recommended as the best first step for validation. • Analysis of serial PEF records requires expertise, and is assisted by the 3 methods of analysis available in the Oasys plotter. • Timepoint analysis is the best method of identifying statistically significant small work-related changes in PEF. • The original timepoint method is a statistcial score looking for a signficantPEF drop between average work and rest days. It is based on a method by Stenton et al for analysing specific inhalation challenges. It has a Specificity of 88% (rising to 92% if lone drops near waking are excluded), a Sensitivity of 77% and is able to make a diagnosis from smaller changes in PEF and hence to identify disease earlier. These 2-hourly plots are from a lady working with Isocyanates from 11/2003, 01/2004 and away from exposure in 01/2005. Conclusions New method has improved theoretical validity but is no better in practice. Standardising daily readings from waking time does not reduce pooled SD for days off work suggesting that patients do not record this accurately or that waking time is not a big factor in the timing of the lowest daily PEF (this would go against current thinking). Timepoint analysis of serial PEF measurements remains the best method of validating occupational asthma when changes in PEF are small (as may occur early in the course of occupational asthma). It is currently limited by many workers waking later on days off than on workdays Limitations The lower boundary (in grey) shows the 95% confidence limit from the following formula: “s√(1/m+1/n)t(k(n-1), (0.95)1/k)” where s is the pooled standard deviation from a one way ANOVA of the rest days, n /m are the number of rest /work days, k is the number of comparisons, (0.95)1/k is the bonferroni adjusted confidence level and t is the students t statistic. Any work (red line) below this limit constitutes a positive diagnosis. The original method requires low rest day variation to work well and is only applicable to patients who wake up at similar times on rest and work days, restricting analysis to around half of available records. The specificity of 88% is a long way below the 95% predicted from the statistical model. Improvements Future enhancements • Accurate time measurements from logging meters should improve the precision. • Evaluate for FEV1 (should be equally applicable). • Analyse only those parts of a record where a patient wakes up at similar times, to allow analysis of a greater percentage of records All hospital lung function laboratories should be able to analyse serial PEF measurements for the diagnosis of occupational asthma. The Oasys PEF analysis program is available free of charge from our website: www.occupationalasthma.com References Lone drops near waking are ignored in version 1. Version 2 excludes waking readings. The correction factor is applied at every point in version 2 so the limit is not parallel to the rest curve. Stenton SC et al, Statistical approaches to the identification of late asthmatic reactions, Eur Respir J, 1994; 7 : 806-812 Burge CBSG et al, Diagnosis of occupational asthma from time point differences in serial PEF measurements, Thorax, 2009; 64 : 1032-1036