PHARMACOKINETICS AND CLINICAL OUTCOMES OF TOBRAMYCIN IN ADULT CYSTIC FIBROSIS PATIENTS WITH ACUTE PULMONARY EXACERBATION
Bibliographic record
Abstract
Background Acute pulmonary exacerbation (APE) in cystic fibrosis patients is frequent and associated with a decline in pulmonary function, quality of life and survival. Tobramycin is often used in regimens requiring activity against Pseudomonas aeruginosa , however, an important number of centers do not use official dosing recommendation. The current dosing strategy may be suboptimal. Methods This retrospective cohort analysis was performed on all adult cystic fibrosis patients that were admitted at a tertiary care facility for treatment of APE and with tobramycin between January 2015 and December 2019. The primary objective was to evaluate the predictive performance of previously published pharmacokinetic (PK) models and, secondly, to evaluate potential factors that impact clinical outcomes. Clinical outcomes were only evaluated in a sub-group of patients with cultures positive for P. aeruginosa . Results A total of 202 APEs from 51 patients were included in the PK analysis. Two population PK models were assessed and failed to fit our data. In all, 109 APEs from 32 patients were included in the clinical analysis. Factors that significantly impacted clinical outcome were the number of prior APE and concomitant antibiotics. Clinical success rate for regimens containing at least one active agent against P. aeruginosa according to its susceptibility was 67%. Conclusion Population PK models evaluated in this study cannot be used to perform simulations. A new model must be developed for our population. In patients positive for P. aeruginosa , Ceftazidime in combination to tobramycin may be a superior regimen. APE history remains predictive for outcomes in adult CF patients treated for an APE.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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".