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

Introduction

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. Median survival has improved to 45 years, in part because of improvement in care quality². An important quality improvement initiative is benchmarking, which involves identifying high-performing centres and the practices associated with outstanding performance^3–5. Since forced expiratory volume in one second (FEV₁) is an important CF prognostic marker^6–9, it is often used as an outcome measure for benchmarking^3–5,10.

Different statistical methods of analysing FEV₁ data can yield different results¹¹, but there is scant attention paid to the methods of processing FEV₁ data. We previously reported a statistically significant reduction in %FEV₁ decline for our CF centre from 2013–2016¹². We now set out to understand the impact of using different FEV₁ data processing methods on our CF centre’s outcome.

Methods

This is a single-centre retrospective analysis of routinely collected clinical data from 2013–2016. Regulatory approval for the analysis was obtained from NHS Health Research Authority (IRAS number 210313). All adults with CF diagnosed according to the UK CF Trust criteria aged ≥16 years were included, except those with lung transplantation or on ivacaftor. These treatments have transformative effects on %FEV₁^13–15, thus may affect the interpretation of %FEV₁ decline.

Demographic data (age, gender, genotype, pancreatic status, CF related diabetes, Pseudomonas aeruginosa status), body mass index (BMI) and FEV₁ data were collected by two investigators (HZH and RC / HZH and MEG) independently reviewing paper notes and electronic records. Where data from the two investigators differ, the original data from paper notes or electronic records were reviewed to by both investigators to ensure the accuracy of abstracted data. This process ensures the accuracy of abstracted data and helps avoid potential bias from inaccurate or inconsistent data collection¹⁶. FEV₁ data were processed with three different methods prior to analysis. First, %FEV₁ readings (calculated with Knudson equation¹⁷ and available in whole numbers) were directly extracted from spirometer machines. Second, FEV₁ volumes (in litres, to two decimal places) were extracted and clean height data were used to calculate %FEV₁ (as whole numbers) with Knudson equation¹⁷. Third, FEV₁ volumes (in litres, to two decimal places) were extracted and clean height data were used to calculate %FEV₁ with GLI equation¹⁸ using an Excel Macro (Microsoft Excel 2013).

Best %FEV₁, i.e. the highest %FEV₁ reading in a calendar year for each study subject was used for analysis since it is most reflective of the true baseline %FEV₁¹⁹. Year-to-year %FEV₁ change was calculated by subtracting best %FEV₁ at Year 1 from Year 2 (i.e. negative values indicate %FEV₁ decline and positive values indicate increase in %FEV₁). In addition to calculating year-to-year %FEV₁ change using three different FEV₁ data processing methods, %FEV₁ change calculated with GLI equation was also adjusted for baseline %FEV₁ using reference values from Epidemiologic Study of CF (ESCF)²⁰. The ESCF study found median %FEV₁ change of –3%/year, –2%/year and –0.5%/year for baseline %FEV1 ≥100%, 40–99.9% and <40% respectively²⁰. Adjusted %FEV₁ change was calculated by subtracting median ESCF %FEV₁ change from actual %FEV₁ change. Thus, an adjusted %FEV₁ change >0 meant the subject’s %FEV1 decline was less than expected (indicating better health outcome) whilst an adjusted %FEV₁ change <0 meant the subject’s %FEV₁ decline was more than expected (indicating worse health outcome). %FEV₁ change from 2013–2014 to 2015–2016 calculated using different FEV₁ data processing methods were compared using Friedman test. Analyses were performed using SPSS v24 (IBM Corp) and p-value <0.05 was considered statistically significant.

Results

This analysis included 208 adults, with 147 adults providing data for all four years. Overall, the cohort was ageing but baseline %FEV₁ increased from 2014 onwards (see Table 1).

The %FEV₁ increase was in part due to younger adults with higher %FEV₁ transitioning from paediatric care because %FEV₁ tended to decline from year to year (see Table 2). However, different %FEV₁ decline results were obtained with different FEV₁ data processing methods. There was statistically significant reduction in the rate of %FEV₁ decline using %FEV₁ readings as recorded in spirometer machines (p=0.016). Cleaning of height data and standardisation of %FEV₁ calculation with Knudson equation¹⁷ did not alter the magnitude of %FEV₁ decline, but the p-value was no longer statistically significant (p=0.062). The use of GLI equation altered the magnitude of %FEV₁ decline although the trend of reduction in %FEV₁ decline persisted (p=0.135). Adjustment for baseline %FEV₁ further increased the p-value (p=0.210).

Discussion

We demonstrated that different centre-level %FEV₁ decline results were obtained using different FEV₁ data processing methods. In particular, year-on-year %FEV₁ decline was smaller in magnitude when %FEV₁ was calculated using GLI equation¹⁸ instead of Knudson equation¹⁷. This is in part due to the demographic of our centre which has a relatively young adult population. A previous study found a near-linear %FEV₁ decline from childhood to adulthood with GLI equation, whereas there was accelerated %FEV₁ decline during adolescence and young adulthood when %FEV₁ was calculated with Knudson equation²¹. One advantage of using the GLI equation, which is seamless across all ages, is that it improves the interpretation of %FEV₁ decline^21,22. Another advantage is that %FEV₁ decline can be adjusted for baseline %FEV₁ using ESCF reference values (since the ESCF values for %FEV₁ decline were calculated using the GLI equation²⁰).

The limitation for all single-centre analysis is the potential lack of generalisability. Another limitation of our analysis is that the ESCF reference values used to adjust %FEV₁ decline were derived using a cohort from around 15 years ago²⁰, and may not represent the current population. Our results nonetheless highlighted that %FEV₁ decline can be extremely sensitive to the FEV₁ data processing methods. This is one of the challenges of using %FEV₁ decline to infer quality of care. Another challenge is that %FEV₁ lacks sensitivity as an outcome measure. A recent sample size estimation using the UK CF registry data suggests that 273 adults per centre are needed to detect a 5% FEV₁ difference at the 95% significance level²⁵. The sensitivity of measures used to detect variations in care quality is particularly pertinent to CF because a relatively small population is spread across many centres. Indeed, only 6/28 (21.4%) of all UK adult CF centres have ≥273 adults. That means process measures, e.g. medication adherence, is important to detect variations in quality of CF care. Mant & Hicks previous demonstrated that measuring processes of care proven in randomised controlled trials to reduce death allows detection of meaningful differences in care quality for myocardial infarction with just 75 cases, whereas 8179 cases would be needed if mortality was used as the quality indicator²⁶.

Given the limitations of FEV₁ as an outcome measure in CF, results of centre comparisons based on FEV₁ data should be carefully interpreted. Observational studies with %FEV₁ decline as an outcome measure should carefully consider and clearly specify the data processing methods used.

Ethical considerations

Regulatory approval for the analysis was obtained from NHS Health Research Authority (IRAS number 210313).

Data availability

Dataset 1: Sheffield forced expiratory volume in one second (FEV₁) data 10.5256/f1000research.14981.d205603²⁷