ArticleBritish journal of clinical pharmacology2020
Prospective validation of a model-informed precision dosing tool for vancomycin in intensive care patients.
Article in British journal of clinical pharmacology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 2 of them syntheses that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
27 citing papers in PubMed, 2 syntheses or guidelines pooled it, 38 citations in OpenAlex.
- Precision Dosing of Vancomycin: A Systematic Review of Population Pharmacokinetics Models and Comparative Evaluation of Software Tools.Clinical pharmacokinetics · 2026Pooled it
- Clinical practice guidelines for therapeutic drug monitoring of teicoplanin: a consensus review by the Japanese Society of Chemotherapy and the Japanese Society of Therapeutic Drug Monitoring.The Journal of antimicrobial chemotherapy · 2022Guideline
- Model-Informed Precision Dosing of Protein Kinase Inhibitors: Evaluation of Model-Averaging and Flattened Priors Methods.Clinical pharmacokinetics · 2026Trial
- Evaluation of two model-based Bayesian approaches for vancomycin dosing in general hospitalized patients.Journal of pharmacokinetics and pharmacodynamics · 2026Article
- Safety Monitoring of High-Risk Antibiotics Using Artificial Intelligence: A Narrative Review with Focus on Real-World Evidence.Life (Basel, Switzerland) · 2026Review
- Evaluation and Improvement of Specialized Vancomycin Pharmacokinetic Models for Pediatric Cardiovascular Intensive Care Unit and Pediatric Oncology Patients.Clinical pharmacokinetics · 2026Article
- Predictive Performance of Bayesian Dosing Software for Vancomycin in Intensive Care Unit Patients.Therapeutic drug monitoring · 2025Article
- Machine Learning-Based Model Selection and Averaging Outperform Single-Model Approaches for a Priori Vancomycin Precision Dosing.CPT: pharmacometrics & systems 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
- Population Pharmacokinetics Model of Thioguanine in Patients with Inflammatory Bowel Disease.Clinical pharmacokinetics · 2025Article
- Prediction of Vancomycin Area Under the Curve With Trough Concentrations Only: Performance Evaluation of Pediatric Population Pharmacokinetic Models.The Journal of infectious diseases · 2025Article
- Review
- Model-informed precision dosing of vancomycin for rapid achievement of target area under the concentration-time curve: A simulation study.Clinical and translational science · 2023Article
- Early At-Home Measurement of Adalimumab Concentrations to Guide Anti-TNF Precision Dosing: A Pilot Study.European journal of drug metabolism and pharmacokinetics · 2023Article
- Model-informed precision dosing in vancomycin treatment.Frontiers in pharmacology · 2023Article
- Bayesian method application: Integrating mathematical modeling into clinical pharmacy through vancomycin therapeutic monitoring.Pharmacology research & perspectives · 2022Review
- Population pharmacokinetics of cisplatin in small cell lung cancer patients guided with informative priors.Cancer chemotherapy and pharmacology · 2022Observational
- Evaluating and Improving Neonatal Gentamicin Pharmacokinetic Models Using Aggregated Routine Clinical Care Data.Pharmaceutics · 2022Article
- Optimized sampling to estimate vancomycin drug exposure: Comparison of pharmacometric and equation-based approaches in a simulation-estimation study.CPT: pharmacometrics & systems pharmacology · 2022Article
- Pharmacokinetic equations versus Bayesian guided vancomycin monitoring: Pharmacokinetic model and model-informed precision dosing trial simulations.Clinical and translational science · 2022Article
Corrections and comments
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Authors and funding
9 authors at 5 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
aimsVancomycin is an important antibiotic for critically ill patients with Gram-positive bacterial infections. Critically ill patients typically have severely altered pathophysiology, which leads to inefficacy or toxicity. Model-informed precision dosing may aid in optimizing the dose, but prospectively validated tools are not available for this drug in these patients. We aimed to prospectively validate a population pharmacokinetic model for purpose model-informed precision dosing of vancomycin in critically ill patients.
methodsWe first performed a systematic evaluation of various models on retrospectively collected pharmacokinetic data in critically ill patients and then selected the best performing model. This model was implemented in the Insight Rx clinical decision support tool and prospectively validated in a multicentre study in critically ill patients. The predictive performance was obtained as mean prediction error and relative root mean squared error.
resultsWe identified 5 suitable population pharmacokinetic models. The most suitable model was carried forward to a prospective validation. We found in a prospective multicentre study that the selected model could accurately and precisely predict the vancomycin pharmacokinetics based on a previous measurement, with a mean prediction error and relative root mean squared error of respectively 8.84% (95% confidence interval 5.72-11.96%) and 19.8% (95% confidence interval 17.47-22.13%).
conclusionUsing a systematic approach, with a retrospective evaluation and prospective verification we showed the suitability of a model to predict vancomycin pharmacokinetics for purposes of model-informed precision dosing in clinical practice. The presented methodology may serve a generic approach for evaluation of pharmacometric models for the use of model-informed precision dosing in the clinic.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.