Using different methods to process forced expiratory volume in one second (FEV (1)) data can impact on the interpretation of FEV (1) as an outcome measure to understand the performance of an adult cystic fibrosis centre: A retrospective chart review

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 centre outcome. Methods: This is a single-centre retrospective analysis of routinely collected data from 2013-2016 among 208 adults. Year-to-year %FEV (1) change was calculated by subtracting best %FEV (1) at Year 1 from Year 2 (i.e. negative values indicate fall in %FEV (1)), 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, year-to-year variation in %FEV (1) calculated using GLI equation was adjusted for baseline %FEV (1) to understand the impact of case-mix adjustment. Results: Year-to-year fall in %FEV (1) reduced with all three data processing methods but the magnitude of this change 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 reduced year-to-year fall in %FEV (1) was robust, different data processing methods yielded varying results when year-to-year variation in %FEV (1) was compared using a standard related group non-parametric statistical test. Observational studies with year-to-year variation in %FEV (1) as an outcome measure should carefully consider and clearly specify the data processing methods used.