Evidence map›Paper›PMID 36830212›Full record

ArticleAntibiotics (Basel, Switzerland)2023

Implementing Vancomycin Population Pharmacokinetic Models: An App for Individualized Antibiotic Therapy in Critically Ill Patients.

Manuel Mena, Julio-Cesar Garcia, Rosa-Helena Bustos

Abstract read
In one paragraph

Article in Antibiotics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  3. Article
  4. Article
  5. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Manuel MenaDepartment of Clinical Pharmacology, Evidence-Based Therapeutics Group, Faculty of Medicine, Campus del Puente del Común, Universidad de La Sabana and Clínica Universidad de La Sabana, Km. 7, Autopista Norte de Bogotá, Chía 1400132, Cundinamarca, Colombia.
Julio-Cesar GarciaDepartment of Clinical Pharmacology, Evidence-Based Therapeutics Group, Faculty of Medicine, Campus del Puente del Común, Universidad de La Sabana and Clínica Universidad de La Sabana, Km. 7, Autopista Norte de Bogotá, Chía 1400132, Cundinamarca, Colombia.ORCID 0000-0002-1136-848X
Rosa-Helena BustosDepartment of Clinical Pharmacology, Evidence-Based Therapeutics Group, Faculty of Medicine, Campus del Puente del Común, Universidad de La Sabana and Clínica Universidad de La Sabana, Km. 7, Autopista Norte de Bogotá, Chía 1400132, Cundinamarca, Colombia.ORCID 0000-0003-0243-2299

Funding

University of La Sabana MinCiencias (grant number 123080763958, MED-228-2018)
6 · The paper itself

Abstract

In individualized therapy, the Bayesian approach integrated with population pharmacokinetic models (PopPK) for predictions together with therapeutic drug monitoring (TDM) to maintain adequate objectives is useful to maximize the efficacy and minimize the probability of toxicity of vancomycin in critically ill patients. Although there are limitations to implementation, model-informed precision dosing (MIPD) is an approach to integrate these elements, which has the potential to optimize the TDM process and maximize the success of antibacterial therapy. The objective of this work was to present an app for individualized therapy and perform a validation of the implemented vancomycin PopPK models. A pragmatic approach was used for selecting the models of Llopis, Goti and Revilla for developing a Shiny app with R. Through ordinary differential equation (ODE)-based mixed effects models from the mlxR package, the app simulates the concentrations' behavior, estimates whether the model was simulated without variability and predicts whether the model was simulated with variability. Moreover, we evaluated the predictive performance with retrospective trough concentration data from patients admitted to the adult critical care unit. Although there were no significant differences in the performance of the estimates, the Llopis model showed better accuracy (mean 80.88%; SD 46.5%); however, it had greater bias (mean -34.47%, SD 63.38%) compared to the Revilla et al. (mean 10.61%, SD 66.37%) and Goti et al. (mean of 13.54%, SD 64.93%) models. With respect to the RMSE (root mean square error), the Llopis (mean of 10.69 mg/L, SD 12.23 mg/L) and Revilla models (mean of 10.65 mg/L, SD 12.81 mg/L) were comparable, and the lowest RMSE was found in the Goti model (mean 9.06 mg/L, SD 9 mg/L). Regarding the predictions, this behavior did not change, and the results varied relatively little. Although our results are satisfactory, the predictive performance in recent studies with vancomycin is heterogeneous, and although these three models have proven to be useful for clinical application, further research and adaptation of PopPK models is required, as well as implementation in the clinical practice of MIPD and TDM in real time.

Indexed as

Bayesian predictioncritical ill patientsindividualized therapypersonalized dosingpopulation pharmacokineticsShiny applicationtherapeutic drug managementvancomycin

Identifiers

PMID36830212
PMCPMC9952184

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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.