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Background: The marked heterogeneity in cystic fibrosis (CF) disease complicates the selection of those most likely to benefit from existing or emergent treatments. Objective: We aimed to predict the progression of bronchiectasis in preschool children with CF. Methods: Using data collected up to 3 years of age, in the Australian Respiratory Early Surveillance Team for CF cohort study, clinical information, chest computed tomography (CT) scores, and biomarkers from bronchoalveolar lavage were assessed in a multivariable linear regression model as predictors for CT bronchiectasis at age 5–6. Results: Follow-up at 5–6 years was available in 171 children. Bronchiectasis prevalence at 5–6 was 134/171 (78%) and median bronchiectasis score was 3 (range 0–12). The internally validated multivariate model retained eight independent predictors accounting for 37% (adjusted R2) of the variance in bronchiectasis score. The strongest predictors of future bronchiectasis were: pancreatic insufficiency, repeated intravenous treatment courses, recurrent lower respiratory infections in the first 3 years of life, and lower airway inflammation. Dichotomizing the resulting prediction score at a bronchiectasis score of above the median resulted in a diagnostic odds ratio of 13 (95% confidence interval [CI], 6.3–27) with positive and negative predictive values of 80% (95% CI, 72%–86%) and 77% (95% CI, 69%–83%), respectively. Conclusion: Early assessment of bronchiectasis risk in children with CF is feasible with reasonable precision at a group level, which can assist in high-risk patient selection for interventional trials. The unexplained variability in disease progression at individual patient levels remains high, limiting the use of this model as a clinical prediction tool.
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- 1 Finished
1/01/10 → 31/12/15