Our current understanding of the complex pathways leading to bronchiectasis are poorly understood. Mathematical modelling and machine learning are powerful techniques that can define and identify new pathways and endotypes. The Team have previously identified that children with persistent symptoms 3-4 weeks after hospitalisation for acute lower respiratory infections had an increased risk of developing bronchiectasis within the next 13 months. The CRE is in a unique position to identify factors associated with the development of bronchiectasis as previous completed studies has resulted in a large sample biobank and prospective cohort data sets on children who later developed bronchiectasis.
To determine predictive factors and pathways associated with the development of bronchiectasis using mathematical models.
The study will utilise datasets from the ABIS, PETAL, DACs, Pneumatters and D-Kids studies and a further cohort of 839 children with acute lower respiratory infection enrolled from the emergency department.
This study is funded through the AusBREATHE CRE.