114 Influence of a highly effective modulator on airway colonization in patients with cystic fibrosis aged 6 years and older
Bibliographic record
Abstract
Abstract Background Elexacaftor-tezacaftor-ivacaftor (ETI) has become a mainstay in the treatment of CF patients who are at least heterozygotes for the F508del mutation. It improves pulmonary function and quality of life and reduces respiratory exacerbations. However, scarce data is available regarding its effect on lung bacterial colonization. Objectives Our objective was to assess the influence of ETI on the number of different respiratory pathogens found in patients with CF (pwCF) one year before and one year after its initiation. Our secondary objective was to compare the prevalence of Pseudomonas aeruginosa and Staphylococcus aureus in pwCF airways for the same periods. Design/Methods We conducted an observational cohort study in our CF clinic. PwCF were included if they were aged 6 to 17 years. We studied 56 patients in total after ethical approval was obtained. We calculated the mean difference between the number of different pathogens 12 months and 6 months before and after ETI. Mean differences were also calculated to compare the prevalence of Pseudomonas aeruginosa and Staphylococcus aureus for the same periods. Samples were obtained by sputum or throat cultures. We used multivariate generalized linear models, based on the generalized estimating equations (GEE) method. Models were adjusted for age, genotype, and the duration of antibiotic treatment for exacerbation or eradication. Results We found a statistically significant difference in means of 0.65 (95% CI: 0.51,0.81; p=0.002) between the number of respiratory pathogens in the year following ETI compared to the year before. Regarding Pseudomonas aeruginosa, we found a statistically significant decrease in its prevalence in the year after ETI, with an odds ratio of 0.45 (95% CI: 0.24,0.83; p=0.02). For Staphylococcus aureus, we found a non-statistically significant decrease in its prevalence after ETI with an odds ratio of 0.79 (95% CI: 0.41,1.5; p=0.47). Conclusion Our study shows a reduction in the number of bacteria in the airways of pwCF one year after ETI. It also shows a decrease in the prevalence of Pseudomonas aeruginosa and Staphylococcus aureus. These findings are consistent with the improvement seen in FEV1 and reduction of respiratory exacerbations after ETI. It will eventually be important to study its effect on the diversity of respiratory microbiota and to understand its effect on chronic lung inflammation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".