Abstract
Background: Forced expiratory volume in one second (FEV 1) is an important cystic fibrosis (CF) prognostic marker and an established endpoint for CF clinical trials. FEV 1 is also used in observation studies, e.g. to compare different centre's outcomes. We wished to evaluate whether different methods of processing FEV 1 data can impact on a centre's outcome. Methods: This is a single-centre retrospective analysis of routinely collected data from 2013-2016 which included 208 adults with CF. Year-to-year %FEV 1 change was calculated by subtracting best %FEV 1 at Year 1 from Year 2 (i.e. negative values indicate %FEV 1 decline), and compared using Friedman test. Three methods were used to process %FEV 1 data. First, %FEV 1 calculated with Knudson equation was extracted directly from spirometer machines. Second, FEV 1 volume were extracted then converted to %FEV 1 using clean height data and Knudson equation. Third, FEV 1 volume were extracted then converted to %FEV 1 using clean height data and GLI equation. In addition, %FEV 1 decline calculated using GLI equation was adjusted for baseline %FEV 1 to understand the impact of case-mix adjustment. Results: There was a trend of reduction in %FEV 1 decline with all three data processing methods but the magnitude of %FEV 1 decline differed. Median change in %FEV 1 for 2013-2014, 2014-2015 and 2015-2016 was -2.0, -1.0 and 0.0 respectively using %FEV 1 in Knudson equation whereas the median change was -1.1, -0.9 and -0.3 respectively using %FEV 1 in the GLI equation. A statistically significant p-value (0.016) was only obtained when using %FEV 1 in Knudson equation extracted directly from spirometer machines. Conclusions: Although the trend of reduction in %FEV 1 decline was robust, different data processing methods yielded varying results when %FEV 1 decline was compared using a standard related group non-parametric statistical test. Observational studies with %FEV 1 decline as an outcome measure should carefully consider and clearly specify the data processing methods used.
Highlights
IntroductionCystic fibrosis (CF) is a multi-system genetic condition but the two main affected organs are lungs (resulting in recurrent infections and respiratory failure) and gastrointestinal tract (resulting in fat malabsorption and poor growth)[1]
Cystic fibrosis (CF) is a multi-system genetic condition but the two main affected organs are lungs and gastrointestinal tract[1]
There was a trend of reduction in %FEV1 decline with all three data processing methods but the magnitude of %FEV1 decline differed
Summary
Cystic fibrosis (CF) is a multi-system genetic condition but the two main affected organs are lungs (resulting in recurrent infections and respiratory failure) and gastrointestinal tract (resulting in fat malabsorption and poor growth)[1]. An important quality improvement initiative is benchmarking, which involves identifying high-performing centres and the practices associated with outstanding performance[3,4,5]. Since forced expiratory volume in one second (FEV1) is an important CF prognostic marker[6,7,8,9], it is often used as an outcome measure for benchmarking[3,4,5,10]. We set out to understand the impact of using different FEV1 data processing methods on our CF centre’s outcome. Forced expiratory volume in one second (FEV1) is an important cystic fibrosis (CF) prognostic marker and an established endpoint for CF clinical trials. We wished to evaluate whether different methods of processing FEV1 data can impact on a centre’s outcome
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