Antibiotic Regimen Changes during Cystic Fibrosis Pediatric Pulmonary Exacerbation Treatment
Bibliographic record
Abstract
Abstract Rationale/Objectives Antibiotic selection for in-hospital treatment of pulmonary exacerbations (PEx) in people with cystic fibrosis (CF) is typically guided by previous respiratory culture results or past PEx antibiotic treatment. In the absence of clinical improvement during PEx treatment, antibiotics are frequently changed in search of a regimen that better alleviates symptoms and restores lung function. The clinical benefits of changing antibiotics during PEx treatment are largely uncharacterized Methods This was a retrospective cohort study using the Cystic Fibrosis Foundation Patient Registry Pediatric Health Information System. PEx were included if they occurred in children with CF from 6 to 21 years old who had been treated with intravenous antibiotics between January 1, 2006, and December 31, 2018. PEx with lengths of stay <5 or >21 days or for which treatment was delivered in an intensive care unit were excluded. An antibiotic change was defined as the addition or subtraction of any intravenous antibiotic between Hospital Day 6 and the day before hospital discharge. Inverse probability of treatment weighting was used to adjust for disease severity and indication bias, which might influence a decision to change antibiotics. Results In all, 4,099 children with CF contributed 18,745 PEx for analysis, of which 8,169 PEx (43.6%) included a change in intravenous antibiotics on or after Hospital Day 6. The mean change in pre- to post-treatment percent predicted forced expiratory volume in 1 second (ppFEV1) was 11.3 (standard error, 0.21) among events in which an intravenous antibiotic change occurred versus 12.2 (0.18) among PEx without an intravenous antibiotic change (P = 0.001). Similarly, the odds of return to ⩾90% of baseline ppFEV1 were less for PEx with antibiotic changes than for those without changes (odds ratio [OR], 0.89 [95% confidence interval (CI), 0.80–0.98]). The odds of returning to ⩾100% of baseline ppFEV1 did not differ between PEx with versus without antibiotic changes (OR, 0.94 [95% CI, 0.86–1.03]). In addition, PEx treated with intravenous antibiotic changes were associated with higher odds of future PEx (OR, 1.17 [95% CI, 1.12–1.22]). Conclusions In this retrospective study, changing intravenous antibiotics during PEx treatment in children with CF was common and not associated with improved clinical outcomes.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".