A screening tool to identify risk for bronchiectasis progression in children with cystic fibrosis. Issue 1 (12th October 2021) - Caudri, Daan; Turkovic, Lidija; de Klerk, Nicholas H.; Rosenow, Tim; Murray, Conor P.; Steyerberg, Ewout W.; Ranganathan, Sarath C.; Sly, Peter; Stick, Stephen M.; Breuer, Oded
Abstract: 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 R 2 ) 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.