Vancomycin area under the curve/minimum inhibitory concentration and trough level concordance–evaluation on an urban health unit
Bibliographic record
Abstract
Background: A vancomycin AUC/MIC (area under the curve/minimum inhibitory concentration) of 400–600 mg•h/L is associated with improved clinical outcomes for the treatment of methicillin resistant Staphylococcus aureus (MRSA) infections. Currently, there are still limited studies evaluating the relationship between vancomycin trough and AUC. Objectives: To evaluate the correlation between vancomycin trough and AUC/MIC and determine if trough-guided monitoring is an adequate predictor of AUC/MIC in the Urban Health population at St Paul’s Hospital. Methods: This was a retrospective chart review of 29 patients from November 2019 to February 2021. Patient demographics and laboratory data were collected from electronic medical records. The two-level equation-based approach was used to calculate AUC/MIC. The proportion of AUC/MIC values within target (400–600 mg•h/L) despite subtherapeutic troughs, and the proportion of AUC/MIC values supratherapeutic when trough is within target (15–20 mg/L) were the primary endpoints. Main Results: Fifty-seven sets of levels were collected and 75% of vancomycin troughs and AUC 24 were found to be discordant. When trough was 10–14.9 mg/L, AUC 24 was > 400 mg•h/L in 94% of cases and when trough was 15–20 mg/L, AUC 24 was > 600 mg•h/L in 69% of cases. There was a moderate correlation between vancomycin trough and AUC 24h ( R 2 = 0.57; p < 0.001). Conclusion: A vancomycin trough between 15 and 20 mg/L may result in an AUC/MIC greater than necessary for clinical efficacy. Considering these findings and the practical concerns of AUC-guided monitoring, a modest reduction in target troughs to prevent vancomycin toxicity warrants clinical consideration and further evaluation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".