Predicting disease progression in cystic fibrosis

Oded Breuer, Daan Caudri, Stephen Stick, Lidija Turkovic

Research output: Contribution to journalReview articlepeer-review

20 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)905-917
Number of pages13
JournalExpert Review of Respiratory Medicine
Issue number11
Publication statusPublished - 2 Nov 2018


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