A screening tool to identify risk for bronchiectasis progression in children with cystic fibrosis

The AREST CF Study Group, Daan Caudri*, Lidija Turkovic, Nicholas H. de Klerk, Tim Rosenow, Conor P. Murray, Ewout W. Steyerberg, Sarath C. Ranganathan, Peter Sly, Stephen M. Stick, Oded Breuer

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

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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.

Original languageEnglish
Pages (from-to)122-131
Number of pages10
JournalPediatric Pulmonology
Issue number1
Publication statusPublished - Jan 2022

Bibliographical note

Funding Information:
Stephen M. Stick reports grants from the NHMRC and the USCF Foundation during the conduct of this study. Other authors declare that there are no conflict of interests.

Funding Information:
Australian Respiratory Early Surveillance Team Cystic Fibrosis program was supported by the NHMRC Grants APP1000896 and 1020555, as well as Cystic Fibrosis Foundation USA and Australia. Daan Caudri received grant support from the Rothwell Foundation, the Ter Meulen Grant of the Royal Netherlands Academy of Arts and Sciences, and a 2014 Research Fellowship from the Sophia Children's Hospital Fund. None of the funding bodies were in any way involved in the data collection, interpretation of the data, or writing of the manuscript. The full authorship includes the members of AREST CF. The full list of AREST CF members can be found at www.arestcf.org .

Publisher Copyright:
© 2021 The Authors. Pediatric Pulmonology published by Wiley Periodicals LLC.

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