Predicting disease progression in cystic fibrosis

ABSTRACT

Introduction: Progressive lung disease is the major cause of morbidity and mortality in patients with cystic fibrosis (CF). Methods of correctly predicting the future progression of lung disease in patients with CF are essential for directing aggressive treatment to prevent loss of lung function and end stage respiratory failure.

Areas covered: This review addresses predictors of respiratory disease progression in patients with CF. We searched Web of Science and Medline, with no restriction on publication date, with the search terms ‘cystic fibrosis’ and ‘disease progression’, ‘lung function decline’, ‘prognosis’, ‘prediction/predictive’, ‘prediction/prognostic scores’, ‘risk factors’, ‘outcome measures/endpoints/disease surrogate’, ‘longitudinal/long term’, ‘statistical model’, and ‘survival’.

Expert commentary: Forced expiratory volume in 1 sec (FEV₁) and rate of FEV₁ decline, remain the most significant predictors of mortality in patients with CF while CT scores and airway secretion biomarkers are the main predictors of early CF lung disease. Comprehensive scores incorporating clinical, lung function, imaging and laboratory data will become essential in the future for predicting disease progression and for use in clinical trials. Early interventions may delay the progression of structural lung disease.

KEYWORDS:

• FEV₁% predicted
• Lung clearance index (LCI)
• Chest CT scores

Declaration of interest

The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.

Reviewer disclosures

Peer reviewers on this manuscript have no relevant financial or other relationships to disclose.

Additional information

Funding

O Breuer hasAUSiMED (Australia/Israel Medical Research) been supported by a Lowy Foundation Paediatric Fellowship arranged by . D Caudri received support for a Research Fellowship from the Rothwell Foundation, the Ter Meulen Grant of the Royal Netherlands Academy of Arts and Sciences, and the Sophia Childrens’ Hospital Fund.