Correlation of Calculated Vancomycin Trough Concentrations and Exposure: A Monte Carlo Simulation
Bibliographic record
Abstract
BACKGROUND: Current recommendations are to dose vancomycin to target 24-hour area under the curve (AUC) of 400-600 mg·h/L to optimize efficacy and safety. Limited data support AUC monitoring, and some centers continue to use trough concentrations. A target of 10-20 mg/L has been proposed to reduce nephrotoxicity risk. OBJECTIVE: To use previously published pharmacokinetic equations in a Monte Carlo simulation relating AUC exposure to trough concentrations when targeting an AUC between 400 and 600 mg·h/L. METHODS: Previously published pharmacokinetic data were used as input parameters for a Monte Carlo simulation using previously published formulae to correlate AUC to simulated trough concentrations. Pharmacokinetic parameters were assumed to occur in a normal distribution pattern. We excluded irrelevant simulated cases. Maintenance doses of 15 mg/kg were rounded to the nearest 250 mg. Calculated trough concentrations for AUCs of both 400 and 600 mg·h/L were evaluated in each simulation. RESULTS: A total of 10 000 Monte Carlo simulations were performed. Targeting an AUC of 400 mg·h/L resulted in a mean trough concentration of 10.3 ± 0.8 mg/L. Targeting an AUC of 600 mg·h/L resulted in a mean trough concentration of 15.4 ± 1.2 mg/L. CONCLUSION AND RELEVANCE: We demonstrate that a lower trough concentration range may be supported by an AUC of 400-600 mg·h/L, which may reduce risk and rates of nephrotoxicity without compromising previously established efficacious target trough concentrations.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".