Article Text

Understanding the natural progression in %FEV1 decline in patients with cystic fibrosis: a longitudinal study
  1. David Taylor-Robinson1,
  2. Margaret Whitehead1,
  3. Finn Diderichsen2,
  4. Hanne Vebert Olesen3,
  5. Tania Pressler4,
  6. Rosalind L Smyth5,
  7. Peter Diggle6
  1. 1Department of Public Health and Policy, University of Liverpool, Liverpool, UK
  2. 2Department of Social Medicine, University of Copenhagen, Copenhagen, Denmark
  3. 3Cystic Fibrosis Center, Aarhus University Hospital, Aarhus, Denmark
  4. 4Cystic Fibrosis Center, Rigshospitalet, Copenhagen, Denmark
  5. 5Division of Child Health, University of Liverpool, Liverpool, UK
  6. 6School of Health and Medicine, Lancaster University, Lancaster, UK
  1. Correspondence to Dr David Taylor-Robinson, MRC Population Health Scientist, Department of Public Health and Policy, Whelan Building, University of Liverpool, Liverpool L69 3GB, UK; dctr{at}


Background Forced expiratory volume in 1 s as a percentage of predicted (%FEV1) is a key outcome in cystic fibrosis (CF) and other lung diseases. As people with CF survive for longer periods, new methods are required to understand the way %FEV1 changes over time. An up to date approach for longitudinal modelling of %FEV1 is presented and applied to a unique CF dataset to demonstrate its utility at the clinical and population level.

Methods and findings The Danish CF register contains 70 448 %FEV1 measures on 479 patients seen monthly between 1969 and 2010. The variability in the data is partitioned into three components (between patient, within patient and measurement error) using the empirical variogram. Then a linear mixed effects model is developed to explore factors influencing %FEV1 in this population. Lung function measures are correlated for over 15 years. A baseline %FEV1 value explains 63% of the variability in %FEV1 at 1 year, 40% at 3 years, and about 30% at 5 years. The model output smooths out the short-term variability in %FEV1 (SD 6.3%), aiding clinical interpretation of changes in %FEV1. At the population level significant effects of birth cohort, pancreatic status and Pseudomonas aeruginosa infection status on %FEV1 are shown over time.

Conclusions This approach provides a more realistic estimate of the %FEV1 trajectory of people with chronic lung disease by acknowledging the imprecision in individual measurements and the correlation structure of repeated measurements on the same individual over time. This method has applications for clinicians in assessing prognosis and the need for treatment intensification, and for use in clinical trials.

  • Cystic fibrosis
  • longitudinal
  • model
  • lung function
  • %FEV1
  • clinical epidemiology
  • COPD epidemiology
  • paediatric asthma
  • paediatric lung disease
  • paediatric physician
  • respiratory infection
  • viral infection

This is an open-access article distributed under the terms of the Creative Commons Attribution Non-commercial License, which permits use, distribution, and reproduction in any medium, provided the original work is properly cited, the use is non commercial and is otherwise in compliance with the license. See: and

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  • RLS and PJD are joint senior authors.

  • Funding This work was supported by an MRC Population Health Scientist Fellowship to DTR (G0802448). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

  • Competing interests None.

  • Ethics approval The study was approved by the Danish Data inspectorate (Datatilsynet). Danish CF registry data were used, analysed anonymously.

  • Provenance and peer review Not commissioned; externally peer reviewed.

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