Intrapulmonary pharmacokinetics of antibiotics used to treat nosocomial pneumonia caused by Gram-negative bacilli: A systematic review
Bibliographic record
Abstract
BACKGROUND: Knowledge of antibiotic concentrations achievable in the epithelial lining fluid (ELF) will help guide antibiotic dosing for treating patients with Gram-negative bacillary ventilator-associated pneumonia (VAP). OBJECTIVE: To compare: (1) the ELF:serum penetration ratio of antibiotics in patients with pneumonia, including VAP, with that in healthy study participants; and (2) the ELF and/or tracheal aspirate antibiotic concentrations following intravenous and nebuliser delivery. METHODS: Web of Science, EMBASE and PubMed databases were searched and a systematic review undertaken. RESULTS: Fifty-two studies were identified. ELF penetration ratios for aminoglycosides and most β-lactam antibiotics administered intravenously were between 0.12 and 0.57, whereas intravenous colistin may be undetectable in the ELF. In contrast, estimated mean fluoroquinolone ELF penetration ratios of up to 1.31 were achieved. Importantly, ELF penetration ratios appear reduced in critically ill patients with pneumonia compared with in healthy volunteers receiving intravenous ceftazidime, levofloxacin and fosfomycin; thus, dose adjustment is likely to be required in critically ill patients. In contrast to the systemic administration route, nebulisation of antibiotics achieves high ELF concentrations. Nebulised 400 mg twice-daily amikacin resulted in a median peak ELF steady-state concentration of 976.01 mg/L (interquartile range 410.3-2563.1 mg/L). Similarly, nebulised 1 million international units of colistin resulted in a peak ELF concentration of 6.73 mg/L (interquartile range 4.80-10.10 mg/L). CONCLUSION: Further pharmacokinetic studies investigating the mechanisms for ELF penetration in infected patients and healthy controls are needed to guide antibiotic dosing in VAP and to determine the potential benefits of nebulised therapy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".