Keywords
Cystic fibrosis, epidemiology, patient outcome assessment, forced expiratory volume
Cystic fibrosis, epidemiology, patient outcome assessment, forced expiratory volume
As recommended by Prof Burgel, we have:
See the authors' detailed response to the review by Edward McKone
See the authors' detailed response to the review by Pierre-Régis Burgel
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 performance3–5. Since forced expiratory volume in one second (FEV1) is an important CF prognostic marker6–9, it is often used as an outcome measure for benchmarking3–5,10.
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 year-to-year %FEV1 fall 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.
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 %FEV113–15, thus may affect the interpretation of year-to-year variation in %FEV1.
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 fall in %FEV1 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 year-to-year change in %FEV1 was less than expected (indicating better health outcome) whilst an adjusted %FEV1 change <0 meant the subject’s year-to-year change in %FEV1 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. Bland-Altman analyses21 were also used to compare year-to-year variation in FEV1 as calculated with Knudson equation against year-to-year variation in FEV1 as calculated with GLI equation, to understand the impact of using different reference equations. Analyses were performed using SPSS v24 (IBM Corp) and Prism v7 (GraphPad Software). P-value <0.05 was considered statistically significant.
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).
2013 | 2014 | 2015 | 2016 | |
---|---|---|---|---|
Excluded Lung transplantation, n On ivacaftor, n | 6 7 | 6 7 | 9 9 | 7 13 |
Included, n | 166 | 170 | 185 | 186 |
Age in years, median (IQR) | 25 (19 – 31) | 26 (20 – 32) | 27 (20 – 34) | 27 (21 – 34) |
Female, n (%) | 76 (45.8) | 80 (47.1) | 87 (47.0) | 90 (48.4) |
Genotype status: ¶ ≥1 unknown mutation(s), n (%) ≥1 class IV-V mutation(s), n (%) Homozygous class I-III, n (%) | 11 (6.6) 26 (15.7) 129 (77.7) | 13 (7.6) 29 (17.1) 128 (75.3) | 16 (8.6) 36 (19.5) 133 (71.9) | 15 (8.1) 34 (18.3) 137 (73.7) |
Pancreatic insufficient, † n (%) | 137 (82.5) | 135 (79.4) | 142 (76.8) | 145 (78.0) |
CF related diabetes, ‡ n (%) | 39 (23.5) | 42 (24.7) | 42 (22.7) | 54 (29.0) |
P. aeruginosa status: § No P. aeruginosa, n (%) Intermittent P. aeruginosa, n (%) Chronic P. aeruginosa, n (%) | 60 (36.1) 37 (22.3) 69 (41.6) | 57 (33.5) 36 (21.2) 77 (45.3) | 74 (40.0) 31 (16.8) 80 (43.2) | 78 (41.9) 29 (15.6) 79 (42.5) |
BMI, median (IQR) | 22.3 (19.7 – 24.6) | 22.7 (20.0 – 25.0) | 23.0 (20.3 – 26.0) | 23.2 (20.4 – 26.0) |
Best %FEV1, median (IQR) | 78.7 (54.1 – 92.5) | 76.6 (54.4 – 89.7) | 77.8 (60.4 – 89.0) | 78.5 (58.5 – 89.6) |
¶ Genotype status as defined by international consensus22. Homozygous class I-III mutations indicate ‘severe genotype’.
† Pancreatic insufficiency was diagnosed by the clinical team on the basis of ≥2 faecal pancreatic elastase levels <200µg/g stool and symptoms consistent with maldigestion and malabsorption, in accordance to the UK Cystic Fibrosis (CF) Trust guideline.
‡ CF related diabetes was diagnosed by the clinical team on the basis of oral glucose tolerance test and continuous subcutaneous glucose monitoring results, in accordance to the UK CF Trust guideline.
§ Pseudomonas aeruginosa status was determined according to the Leeds criteria23.
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 year-to-year change in %FEV1 results were obtained with different FEV1 data processing methods. There was statistically significant reduction in year-to-year fall in %FEV1 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 year-to-year variation in %FEV1, but the p-value was no longer statistically significant (p=0.062). The use of GLI equation altered the magnitude of year-to-year variation in %FEV1 although the trend of reduced year-to-year fall in %FEV1 persisted (p=0.135). Adjustment for baseline %FEV1 further increased the p-value (p=0.210).
Methods of processing FEV1 data: | Change in %FEV1, median (IQR) | Friedman test p-values | ||
---|---|---|---|---|
2013 to 2014 (n = 158) | 2014 to 2015 (n = 162) | 2015 to 2016 (n = 176) | ||
(1) %FEV1 (calculated with Knudson equation) extracted from spirometer machines used for analysis † | –2.0 (–6.0 to 1.0) | –1.0 (–3.3 to 2.0) | 0.0 (–3.0 to 2.0) | 0.016 |
(2) FEV1 volume (in L) extracted and height data were cleaned, then %FEV1 calculated using Knudson equation ‡ | –2.0 (–5.0 to 1.0) | –1.0 (–4.0 to 1.0) | 0.0 (–3.8 to 2.0) | 0.062 |
(3) FEV1 volume (in L) extracted and height data were cleaned, then %FEV1 calculated using GLI equation ϕ | –1.1 (–4.6 to 1.5) | –0.9 (–3.2 to 1,5) | –0.3 (–2.9 to 1.8) | 0.135 |
(4) FEV1 volume (in L) extracted and height data were cleaned, then %FEV1 calculated using GLI equation, then change %FEV1 adjusted for baseline %FEV1 using ESCF reference values § | 0.7 (–2.4 to 3.6) | 1.1 (–1.4 to 3.5) | 1.6 (–1.3 to 3.7) | 0.210 |
† The vast majority of the %FEV1 data were from spirometer machines at the Sheffield Adult cystic fibrosis (CF) centre, which were calculated with Knudson equation17 in whole numbers. Some %FEV1 data were from spirometer machines at the Pulmonary Function Unit which operationalised the Knudson equation differently; by calculating age to one decimal place to determine the predicted FEV1. These spirometer machines also provided %FEV1 to two decimal places, but this was rounded to whole numbers for the purpose of analysis. These results were presented at the 2017 North American CF Conference and were published as an abstract in Pediatric Pulmonology12.
‡ FEV1 volumes were available in litres to two decimal places from spirometer machines. Height data were also extracted to allow the calculation of predicted FEV1. This led us to uncover the inconsistency recording of height, which affected 30–40% of the study subjects and would have introduced erroneous variability to the %FEV1 because all equations for predicted %FEV1 are dependent on height. Height data were cleaned to weed out error. Where there was uncertainty regarding the height, the higher value was used to obtain a conservative estimate of %FEV1. To replicate calculation process of the spirometer machines at the Sheffield Adult CF centre, age was rounded down to a whole number and predicted FEV1 in volume were calculated to two decimal places using Knudson equation17. This was used to derive the %FEV1, which was then rounded to whole numbers for the purpose of analysis.
ϕ FEV1 and height data were extracted as above. %FEV1 was calculated using the GLI equation18 using an Excel Macro available at the European Respiratory Society website.
§ %FEV1 calculated using the GLI equation18 as described above, then adjusted for baseline %FEV1 as described in the ‘Methods’ section. An adjusted %FEV1 change of >0 meant the subject’s year-to-year fall in %FEV1 was less than expected for his / her baseline %FEV1, indicating better health outcomes.
Similar results were obtained when restricting the analyses to those aged ≥18 years (see Table 3). Bland-Altman analyses comparing year-to-year variation in %FEV1 calculated from clean FEV1 data using Knudson equation17 vs year-to-year variation in %FEV1 calculated from clean FEV1 data using GLI equation18 indicate the tendency for Knudson equation17 to over-estimate the magnitude of year-to-year fall in %FEV1 by a mean difference of 0.1–0.4% (see Figure 1).
We demonstrated that different centre-level year-to-year variation in %FEV1 results were obtained using different FEV1 data processing methods. In particular, year-to-year fall in %FEV1 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 equation24. One advantage of using the GLI equation, which is seamless across all ages, is that it improves the interpretation of %FEV1 decline24,25. 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 year-to-year variation in %FEV1 were derived using a cohort from around 15 years ago20, and may not represent the current population. Our results nonetheless highlighted that year-to-year variation in %FEV1 can be extremely sensitive to the FEV1 data processing methods. This is one of the challenges of using year-to-year variation in %FEV1 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 level26. 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 indicator27.
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 year-to-year variation in %FEV1 as an outcome measure should carefully consider and clearly specify the data processing methods used.
Regulatory approval for the analysis was obtained from NHS Health Research Authority (IRAS number 210313).
Dataset 1: Sheffield forced expiratory volume in one second (FEV1) data 10.5256/f1000research.14981.d20560328
The author(s) declared that no grants were involved in supporting this piece of work.
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Is the work clearly and accurately presented and does it cite the current literature?
Yes
Is the study design appropriate and is the work technically sound?
Partly
Are sufficient details of methods and analysis provided to allow replication by others?
Yes
If applicable, is the statistical analysis and its interpretation appropriate?
Partly
Are all the source data underlying the results available to ensure full reproducibility?
Yes
Are the conclusions drawn adequately supported by the results?
Partly
Competing Interests: No competing interests were disclosed.
Reviewer Expertise: Stats
Competing Interests: No competing interests were disclosed.
Reviewer Expertise: Adult pulmonologist with experience in the care of adults with cystic fibrosis. Researcher.
Is the work clearly and accurately presented and does it cite the current literature?
Yes
Is the study design appropriate and is the work technically sound?
Yes
Are sufficient details of methods and analysis provided to allow replication by others?
Yes
If applicable, is the statistical analysis and its interpretation appropriate?
Partly
Are all the source data underlying the results available to ensure full reproducibility?
No source data required
Are the conclusions drawn adequately supported by the results?
Partly
Competing Interests: No competing interests were disclosed.
Reviewer Expertise: Adult pulmonologist with experience in the care of adults with cystic fibrosis. Researcher.
Is the work clearly and accurately presented and does it cite the current literature?
Yes
Is the study design appropriate and is the work technically sound?
Partly
Are sufficient details of methods and analysis provided to allow replication by others?
Yes
If applicable, is the statistical analysis and its interpretation appropriate?
Partly
Are all the source data underlying the results available to ensure full reproducibility?
Yes
Are the conclusions drawn adequately supported by the results?
Yes
References
1. Rosenfeld M, Pepe MS, Longton G, Emerson J, et al.: Effect of choice of reference equation on analysis of pulmonary function in cystic fibrosis patients.Pediatr Pulmonol. 2001; 31 (3): 227-37 PubMed AbstractCompeting Interests: No competing interests were disclosed.
Alongside their report, reviewers assign a status to the article:
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