ArticleEuropean journal of hospital pharmacy : science and practice2022
Comparison of area under the curve for vancomycin from one- and two-compartment models using sparse data.
Article in European journal of hospital pharmacy : science and practice, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Article
- Use of nomograms based on the one-compartment model in the estimation of area under the blood concentration-time curve of vancomycin: a retrospective cohort study.European journal of clinical pharmacology · 2025Article
- A pilot study evaluating mid-point vancomycin concentrations to estimate pharmacokinetics and area under the curve: a prospective study.BMC infectious diseases · 2025Article
- Subgroup-based model selection to improve the prediction of vancomycin concentrations.Antimicrobial agents and chemotherapy · 2025Article
- A Reinforcement Learning (RL)-Motivated Simulation Framework for Evaluating Vancomycin Dosing Strategies.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
BACKGROUND AND
objectiveVancomycin pharmacokinetics have been described by both one- and two-compartment models. One-compartment models are widely used to predict the area under the curve (AUC), a useful parameter for determining the efficacy and safety of vancomycin, based on sparse data collected during therapeutic drug monitoring. It is uncertain whether AUCs from one-compartment models with sparsely sampled data can sufficiently represent the true AUC. This study aimed to compare AUC estimates from one- and two-compartment models using sparse data. The reliability of AUCs from models constructed with trough-only data was also assessed.
methodsA previously published robust model was used to simulate vancomycin concentration points at 15 min intervals in 100 patients. From these simulated data, the reference AUC (AUC
resultsWhen assessing the deviation of each AUC from the AUC
conclusionRegardless of statistically significant differences between AUCs from one- and two-compartment models, the level of difference was acceptable from the clinical perspective, being <17% in models from peak-trough data. Therefore, both one- and two-compartment models with sparse data having at least a pair of peak-trough data per patient could be reliable for predicting AUC. Furthermore, AUCs of the one-compartment model from trough-only data did not show a significant difference from the AUC
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