Ivacaftor Is Associated with Reduced Lung Infection by Key Cystic Fibrosis Pathogens. A Cohort Study Using National Registry Data.
Freddy J FrostDilip S NazarethSusan C CharmanCraig WinstanleyMartin J WalshawPublished in: Annals of the American Thoracic Society (2020)
Rationale: Ivacaftor can greatly improve clinical outcomes in people with cystic fibrosis (CF) and has been shown to have in vitro antibacterial properties, yet the long-term microbiological outcomes of treatment are unknown.Objectives: To investigate changes in respiratory microbiology associated with long-term ivacaftor use.Methods: This was a retrospective cohort study using data from the UK CF Registry 2011-2016. Primary outcome was the annual prevalence ratios for key CF pathogens between ivacaftor users and their contemporaneous comparators. Multivariable log-binomial regression models were designed to adjust for confounders. Changes in Pseudomonas aeruginosa status were compared between groups using nonparametric maximum likelihood estimate for the purposes of Kaplan-Meier approximation.Results: Ivacaftor use was associated with early and sustained reduction in P. aeruginosa rates (2016 adjusted prevalence ratio, 0.68; 95% confidence interval, 0.58-0.79; P < 0.001) via a combination of increased clearance in those with infection (ivacaftor: 33/87 [37.9%] vs. nonivacaftor: 432/1,872 [22.8%]; P < 0.001) and reduced acquisition in those without infection (49/134 [36.6%] vs. 1,157/2,382 [48.6%]; P = 0.01). The improved prevalence of P. aeruginosa infection was independent of reduced sampling in the ivacaftor cohort. Ivacaftor was also associated with reduced prevalence of Staphylococcus aureus and Aspergillus spp. but not Burkholderia cepacia complex.Conclusions: In this study, long-term ivacaftor use was associated with reduced infection with important CF pathogens including P. aeruginosa. These findings have implications for antibiotic stewardship and the need for ongoing chronic antimicrobial therapy in this cohort.
Keyphrases
- cystic fibrosis
- pseudomonas aeruginosa
- lung function
- staphylococcus aureus
- risk factors
- biofilm formation
- clinical trial
- gram negative
- antimicrobial resistance
- escherichia coli
- machine learning
- metabolic syndrome
- acinetobacter baumannii
- big data
- stem cells
- adipose tissue
- skeletal muscle
- bone marrow
- insulin resistance
- cross sectional
- deep learning
- cell therapy
- respiratory tract
- wound healing