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Harnessing data to improve lives

For more information

If you are Interested in the work of the Centre or are looking to collaborate, please contact:

Miss Danielle Edwards
danielle.edwards@imperial.ac.uk

Participant privacy information [DOC]

Cystic Fibrosis (CF) is one of the most common life-shortening inherited diseases in the UK, affecting over 10,400 people nationally, and over 90,000 worldwide. The UK CF Registry is a de-identified database run by the Cystic Fibrosis Trust that records the health of everyone consenting in the UK with Cystic Fibrosis. The Registry collects information from consenting patients at their ‘annual review’ care visit and the data are available from 1996 onwards. The UK CF Registry provides a wealth of data which can be used to better understand the progression of Cystic Fibrosis and to further improve the lives of people with CF.

One of only a few Strategic Research Centres funded by the Cystic Fibrosis Trust the Centre is co-led by Dr Diana Bilton and Dr Siobhán Carr at Imperial College London. The Centre aims to extend the use of Registry data by linking it with other health records. CF-EpiNet is comprised of a team of academics and clinicians working across different hospitals and universities.

Our aims

  • To develop methods to link, store and analyse information held within the UK CF Registry with other UK data sources, for example, Hospital Episode Statistics (HES data)
  • To apply advanced statistical techniques appropriate for analysis of longitudinal outcome data in CF to assess causal pathways and therapeutic impacts
  • To identify risk factors at important time points that are associated with and predict the impact of disease on patients’ lives, disease progression and survival, with a focus on addressing social inequalities in cystic fibrosis
  • To improve the evidence available to inform economic models and decisions about appropriate CF care 

 

Projects


Registry enhancement

Aims to optimise the scientific value of the Registry

  • Legacy dataset
  • Linkage of data to other national administrative datasets – HES data (NHS Digital)
  • Quality of Life survey – observational clinical trial

Application of state-of-the art statistical modelling techniques to longitudinal data

Aims to identify modifiable targets

  • Early life exposures: nutrition, Pseudomonas growth
  • Medication
  • Social inequalities

Economic modelling

  • Cost-effectiveness of interventions and strategies for the management of Cystic Fibrosis
  • Utility weighting

Collaborators

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