Risk factors for lung disease progression in children with cystic fibrosis.
Marieke van HorckKim van de KantBjorn WinkensGeertjan WesselingVincent GulmansHan HendriksChris van der GrintenQuirijn JöbsisEdward DompelingPublished in: The European respiratory journal (2018)
To identify potential risk factors for lung disease progression in children with cystic fibrosis (CF), we studied the longitudinal data of all children with CF (aged ≥5 years) registered in the Dutch CF Registry (2009-2014).Lung disease progression was expressed as a decline in lung function (forced expiratory volume in 1 s (FEV1) % pred) and pulmonary exacerbation rate. Potential risk factors at baseline included sex, age, best FEV1 % pred, best forced vital capacity % pred, genotype, body mass index z-score, pancreatic insufficiency, medication use (proton pump inhibitors (PPIs), prophylactic antibiotics and inhaled corticosteroids), CF-related diabetes, allergic bronchopulmonary aspergillosis and colonisation with Pseudomonas aeruginosaThe data of 545 children were analysed. PPI use was associated with both annual decline of FEV1 % pred (p=0.017) and future pulmonary exacerbation rate (p=0.006). Moreover, lower FEV1 % pred at baseline (p=0.007), prophylactic inhaled antibiotic use (p=0.006) and pulmonary exacerbations in the baseline year (p=0.002) were related to pulmonary exacerbations in subsequent years.In a cohort of Dutch children with CF followed for 5 years, we were able to identify several risk factors for future exacerbations. In particular, the association between PPI use and lung disease progression definitely requires further investigation.
Keyphrases
- cystic fibrosis
- lung function
- chronic obstructive pulmonary disease
- young adults
- pseudomonas aeruginosa
- pulmonary hypertension
- body mass index
- risk factors
- type diabetes
- cardiovascular disease
- electronic health record
- physical activity
- escherichia coli
- big data
- artificial intelligence
- intensive care unit
- human health
- skeletal muscle
- glycemic control
- risk assessment
- deep learning