In infants with CF, pulmonary inflammation is associated with lower lung function, whereas pulmonary infection is associated with a greater rate of decline in lung function. Strategies targeting pulmonary inflammation and infection are required to prevent early decline in lung function in infants with CF.

There is emerging evidence that cystic fibrosis lung disease begins early in infancy. Newborn screening allows early detection and surveillance of pulmonary disease and the possibility of early intervention in this life-shortening condition. We report two children with cystic fibrosis who underwent a comprehensive assessment from diagnosis that included measurement of lung function, limited-slice high-resolution CT and BAL performed annually. Early aggressive surveillance enabled significant lung disease and bronchiectasis to be detected during the first few years of life and led to a change in management, highlighting a clinical role for CT scanning during the preschool years in children with cystic fibrosis.

The airway microbiota in children newly diagnosed with bronchiectasis largely retains its diversityTo the Editor:There is a great deal of interest in the airway microbiota, its diversity and the role of specific microbial taxa in the pathophysiology of lung disease [1]. Non-cystic fibrosis bronchiectasis is a significant public health problem in many countries, including New Zealand, where prevalence is high and morbidity and mortality are substantial [2][3][4]. A role for bacteria in the pathophysiology of bronchiectasis is widely accepted but poorly characterised due to inherent difficulties with lower airway sampling, especially in young children. Culture-based studies demonstrate associations with Haemophilus influenzae, Moraxella catarrhalis and Streptococcus pneumoniae in children [2,5], and Pseudomonas aeruginosa in adults and those with severe disease [6,7]. Culture-independent methods have revealed complex airway microbial communities with differences between children and adults with bronchiectasis, and between adults with cystic fibrosis and bronchiectasis [8]. A shared core microbiota was reported for children with bronchiectasis, protracted bacterial bronchitis (PBB) and cystic fibrosis, with most of these taxa also seen in healthy controls [8]. Subsequent research indicated that microbiota composition could be used to distinguish bronchiectasis, PBB and control children [9]. In children with PBB, bronchial brush samples were dominated by Haemophilus, Moraxella, Streptococcus and Neisseria [10], while bronchoalveolar lavage (BAL) samples showed that Bacteroides and Haemophilus were more common than in disease controls, with Lactococcus and Lactobacillus less common [11]. Differences in study findings could be due to methodological differences, disease heterogeneity and timing of airway samples. Adults with bronchiectasis demonstrate a dominance of either Haemophilus or Pseudomonas. Pseudomonal dominance is associated with end-stage lung disease and potentially adverse clinical outcomes [6,7]. Recent longitudinal studies in children with cystic fibrosis demonstrate a reduction in microbial diversity with age, with establishment of more traditional disease-modifying taxa [12]. An emerging common theme is that reduced microbiota diversity, and certain bacterial taxa, may potentially be associated with adverse clinical outcomes.We undertook a prospective study at Starship Children's Hospital, Auckland, New Zealand between 2015 and 2017, enrolling children being investigated for bronchiectasis as per our regional guidelines [13]. Flexible bronchoscopic BAL (1 mL•kg −1 lavage of 0.9% saline, maximum 20 mL) was performed from the 2-3 most affected lobes in children with bronchiectasis as identified by chest computed tomography (CT) scan. Bronchiectasis was radiologically defined as a broncho-arterial ratio of more than 1.0 in combination with other well-described radiological features [2,14]. We also recruited controls undergoing elective surgery, with no respiratory history. A single non-bronchoscopic lavag...

Introduction: The airway microbiota has been linked to specific paediatric respiratory diseases, but studies are often small. It remains unclear whether particular bacteria are associated with a given disease, or if a more general, non-specific microbiota association with disease exists, as suggested for the gut. We investigated overarching patterns of bacterial association with acute and chronic paediatric respiratory disease in an individual participant data (IPD) meta-analysis of 16S rRNA gene sequences from published respiratory microbiota studies.Methods: We obtained raw microbiota data from public repositories or via communication with corresponding authors. Cross-sectional analyses of the paediatric (<18 years) microbiota in acute and chronic respiratory conditions, with >10 case subjects were included. Sequence data were processed using a uniform bioinformatics pipeline, removing a potentially substantial source of variation. Microbiota differences across diagnoses were assessed using alpha- and beta-diversity approaches, machine learning, and biomarker analyses.Results: We ultimately included 20 studies containing individual data from 2624 children. Disease was associated with lower bacterial diversity in nasal and lower airway samples and higher relative abundances of specific nasal taxa including Streptococcus and Haemophilus. Machine learning success in assigning samples to diagnostic groupings varied with anatomical site, with positive predictive value and sensitivity ranging from 43 to 100 and 8 to 99%, respectively.Conclusion: IPD meta-analysis of the respiratory microbiota across multiple diseases allowed identification of a non-specific disease association which cannot be recognised by studying a single disease. Whilst imperfect, machine learning offers promise as a potential additional tool to aid clinical diagnosis.

The goals of asthma management are accurate diagnosis, prompt initiation of treatment and monitoring of disease progression to limit potential morbidity and mortality. While the diagnosis and management is largely based on history taking and clinical examination, there are an increasing number of tools available that could be used to aid diagnosis, define phenotypes, monitor progress and assess response to treatment. Tools such as the Asthma Predictive Index could help in making predictions about the possibility of asthma in childhood based on certain clinical parameters in pre-schoolers. Lung function measurements such as peak expiratory flow, spirometry, bronchodilator responsiveness, and bronchial provocation tests help establish airway obstruction and variability over time. Tools such as asthma questionnaires, lung function measurements and markers of airway inflammation could be used in combination with clinical assessments to assess ongoing asthma control. Recent advances in digital technology, which open up new frontiers in asthma management, need to be evaluated and embraced if proven to be of value. This review summarises the role of currently available tools in asthma diagnosis and management. While many of the tools are readily available in resource rich settings, it becomes more challenging when working in resource poor settings. A rational approach to the use of these tools is recommended.