A screening tool to identify risk for bronchiectasis progression in children with cystic fibrosis
Daan Caudri1,2,3, Lidija Turkovic1, Nicholas H de Klerk1
1Telethon Kids Institute, The University of Western Australia, Perth, Australia.
Insights
Predicting bronchiectasis in children with cystic fibrosis (CF) is possible using early life data. This helps identify high-risk individuals for clinical trials, though individual prediction remains challenging.
Area of Science:
- Pediatric Pulmonology
- Respiratory Medicine
- Clinical Trials
Background:
- Cystic fibrosis (CF) presents with significant heterogeneity, complicating treatment selection.
- Identifying children at risk for CF-related lung disease progression is crucial for early intervention.
Purpose of the Study:
- To develop a predictive model for bronchiectasis progression in preschool children with CF.
- To identify early predictors of lung damage in young CF patients.
Main Methods:
- Utilized data from the Australian Respiratory Early Surveillance Team for CF cohort study.
- Assessed clinical information, CT scans, and bronchoalveolar lavage biomarkers up to age 3.
- Employed multivariable linear regression to predict bronchiectasis at ages 5-6.
Main Results:
- Bronchiectasis affected 78% of children by ages 5-6, with a median CT score of 3.
- A multivariate model with eight predictors explained 37% of the variance in bronchiectasis scores.
- Key predictors included pancreatic insufficiency, IV treatment courses, recurrent infections, and airway inflammation.
Conclusions:
- Early risk assessment for bronchiectasis in CF is feasible at a group level, aiding high-risk patient selection for trials.
- The model shows promise for identifying children likely to develop significant bronchiectasis.
- Individual patient-level prediction remains limited due to high unexplained variability.
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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