Predicting disease progression in cystic fibrosis
Oded Breuer1,2, Daan Caudri1,2,3, Stephen Stick1,2
1a Telethon Kids Institute , University of Western Australia , Perth , Australia.
Expert Review of Respiratory Medicine
|September 4, 2018
Summary
Predicting cystic fibrosis (CF) lung disease progression is crucial. Forced expiratory volume in 1 sec (FEV1) predicts mortality, while CT scans and biomarkers predict early disease, guiding future interventions.
Area of Science:
- Pulmonary Medicine
- Medical Prognostics
Background:
- Progressive lung disease is a primary cause of morbidity and mortality in cystic fibrosis (CF) patients.
- Accurate prediction of lung disease progression is vital for timely, aggressive treatment to prevent lung function loss and end-stage respiratory failure.
Purpose of the Study:
- To review and identify key predictors of respiratory disease progression in individuals with cystic fibrosis.
Main Methods:
- A comprehensive literature search was conducted using Web of Science and Medline.
- Search terms included 'cystic fibrosis,' 'disease progression,' 'lung function decline,' 'prognosis,' 'prediction/predictive,' 'risk factors,' and 'survival.'
Main Results:
- Forced expiratory volume in 1 second (FEV1) and its rate of decline are significant predictors of mortality in CF.
- Computed tomography (CT) scores and airway secretion biomarkers are key predictors of early-stage CF lung disease.
- Comprehensive scores integrating clinical, lung function, imaging, and laboratory data are anticipated for future use in predicting progression and clinical trials.
Conclusions:
- FEV1 remains a critical predictor of CF mortality.
- CT and biomarkers offer insights into early disease stages.
- Future efforts should focus on developing integrated predictive scores to guide interventions and clinical trial design, potentially delaying structural lung disease progression.
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