Aminoglycosides in the Intensive Care Unit: What's New in Population PK Modeling?
Bibliographic record
Abstract
Background Although aminoglycosides are often used as treatment for Gram-Negative infections, optimal dosing regimens remains unclear, especially in ICU patients. This is due to a large between- and within-subject variability in the aminoglycosides’ pharmacokinetics in this population. Objective The review provides comprehensive data on the pharmacokinetics of aminoglycosides in patients hospitalized in ICU by summarizing all published PopPK models in ICU patients for amikacin, gentamicin, and tobramycin. The objective was to determine the presence of a consensus on the structural model used, significant covariates included, and therapeutic targets considered during dosing regimen simulations. Methods A literature search was conducted from the Medline/PubMed database, using the terms: ‘amikacin’, ’gentamicin’, ’tobramycin’, ‘pharmacokinetic(s)’, nonlinear mixed effect’, population’, ‘intensive care’ and ‘critically ill’. Results Nineteen articles were retained where amikacin, gentamicin and tobramycin pharmacokinetics were described in six, eleven and five models, respectively. Two-compartment model best described amikacin and tobramycin pharmacokinetics, whereas one-compartment model majorly described gentamicin pharmacokinetics. The most recurrent significant covariates were renal clearance and bodyweight. Across all aminoglycosides, mean interindividual variability in clearance and volume of distribution were 41.6% and 22.0%, respectively. A common consensus for an optimal dosing regimen for each aminoglycoside was not reached. Conclusion This review showed models developed for amikacin, from 2015 until now and for gentamicin and tobramycin from the past decades. Despite growing challenges of external evaluation, the latter should be more considered during model development. Further research including new covariates, additional simulated dosing regimens and external validation should be considered to better understand aminoglycosides pharmacokinetics in ICU patients.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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".