AUCs and 123s: a critical appraisal of vancomycin therapeutic drug monitoring in paediatrics
Bibliographic record
Abstract
The revised vancomycin guidelines recommend implementing AUC24-based therapeutic drug monitoring (TDM) using Bayesian methods in both adults and paediatrics. The motivation for this change was accumulating evidence showing aggressive dosing to achieve high troughs, as recommended in the first guidelines for adults and extrapolated to paediatrics, is associated with increased nephrotoxicity without improving clinical outcomes. AUC24-based TDM requires substantial resources that may need to be diverted from other valuable interventions. It can therefore be justified only after certain assumptions are shown to be true: (i) there is a clear relationship between vancomycin efficacy and/or toxicity and the proposed therapeutic range; and (ii) maintaining exposure within the target range with AUC24-based TDM improves clinical outcomes and/or decreases toxicity. In this review, we critically appraise the scientific basis for these assumptions. We find studies evaluating the relationship between vancomycin AUC24/MIC and efficacy in adults and children do not offer strong support for the recommended lower limit of the proposed therapeutic range (i.e. AUC24/MIC ≥400). Nephrotoxicity in children increases in a stepwise manner along the vancomycin exposure continuum but it is unclear if one parameter (AUC24 versus trough) is a superior predictor. Overall, evidence in children suggests good-to-excellent correlation between AUC24 and trough. Most importantly, there is no convincing evidence that the method of vancomycin TDM has a causal role in improving efficacy or reducing toxicity. These findings question the need to transition to resource-intensive AUC24-based TDM over retaining trough-based TDM with lower targets to minimize nephrotoxicity in paediatrics.
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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 | 0.053 | 0.186 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".