Using different methods to process forced expiratory volume in one second (FEV1) data can impact on the interpretation of FEV1 as an outcome measure to understand the performance of an adult cystic fibrosis centre: A retrospective chart review [version 2; referees: 2 approved]

Abstract

Background: Forced expiratory volume in one second (FEV1) is an important cystic fibrosis (CF) prognostic marker and an established endpoint for CF clinical trials. FEV1 is also used in observation studies, e.g. to compare different centre’s outcomes. We wished to evaluate whether different methods of processing FEV1 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 %FEV1 change was calculated by subtracting best %FEV1 at Year 1 from Year 2 (i.e. negative values indicate fall in %FEV1), and compared using Friedman test. Three methods were used to process %FEV1 data. First, %FEV1 calculated with Knudson equation was extracted directly from spirometer machines. Second, FEV1 volume were extracted then converted to %FEV1 using clean height data and Knudson equation. Third, FEV1 volume were extracted then converted to %FEV1 using clean height data and GLI equation. In addition, year-to-year variation in %FEV1 calculated using GLI equation was adjusted for baseline %FEV1 to understand the impact of case-mix adjustment. Results: Year-to-year fall in %FEV1 reduced with all three data processing methods but the magnitude of this change differed. Median change in %FEV1 for 2013-2014, 2014-2015 and 2015-2016 was –2.0, –1.0 and 0.0 respectively using %FEV1 in Knudson equation whereas the median change was –1.1, –0.9 and –0.3 respectively using %FEV1 in the GLI equation. A statistically significant p-value (0.016) was only obtained when using %FEV1 in Knudson equation extracted directly from spirometer machines. Conclusions: Although the trend of reduced year-to-year fall in %FEV1 was robust, different data processing methods yielded varying results when year-to-year variation in %FEV1 was compared using a standard related group non-parametric statistical test. Observational studies with year-to-year variation in %FEV1 as an outcome measure should carefully consider and clearly specify the data processing methods used.