Forced Expiratory Volume in 1 Second Variability Helps Identify Patients with Cystic Fibrosis at Risk of Greater Loss of Lung Function

Use of FEV1 in cystic fibrosis epidemiologic studies and clinical trials: A statistical perspective for the clinical researcher

“…This is important because correlation between a CF patient’s initial and subsequent FEV 1 observations may be sustained over as much as 15 years(22). Further, increased FEV 1 variability has been associated with worse decline in pulmonary function (30). Not accounting for these sources of variation can lead to higher Type I error rates (false positives) when, for instance, testing for differences in rate of decline between different patient groups, diminished precision of model estimates, and incorrect conclusions about the degree of influence that certain predictors have on FEV 1 decline.…”

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

“…One advantage of using the GLI equation, which is seamless across all ages, is that it improves the interpretation of %FEV 1 decline 21, 22 . Another advantage is that %FEV 1 decline can be adjusted for baseline %FEV 1 using ESCF reference values (since the ESCF values for %FEV 1 decline were calculated using the GLI equation 20 ).…”

“…Another limitation of our analysis is that the ESCF reference values used to adjust %FEV 1 decline were derived using a cohort from around 15 years ago 20 , and may not represent the current population. Our results nonetheless highlighted that %FEV 1 decline can be extremely sensitive to the FEV 1 data processing methods.…”

“…In addition to calculating year-to-year %FEV 1 change using three different FEV 1 data processing methods, %FEV 1 change calculated with GLI equation was also adjusted for baseline %FEV 1 using reference values from Epidemiologic Study of CF (ESCF) 20 . The ESCF study found median %FEV 1 change of –3%/year, –2%/year and –0.5%/year for baseline %FEV1 ≥100%, 40–99.9% and <40% respectively 20 . Adjusted %FEV 1 change was calculated by subtracting median ESCF %FEV 1 change from actual %FEV 1 change.…”

Forced expiratory volume in 1 second variability in cystic fibrosis—has the clinical utility been lost in statistical translation?

“…In a report published in The Journal , Morgan et al studied the extent to which clinically intuitive calculations of FEV 1 variability are associated with its subsequent decline. 2 However, simplicity often comes at the expense of scientific or statistical rigor. The authors performed aggregate calculations of longitudinal FEV 1 rather than longitudinal modeling, which would have efficiently accounted for correlated predictors and patient-specific decline.…”