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Implementing Vancomycin Population Pharmacokinetic Models: An App for Individualized Antibiotic Therapy in Critically Ill Patients.
Mena, Manuel; Garcia, Julio-Cesar; Bustos, Rosa-Helena.
Afiliação
  • Mena M; Department 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.
  • Garcia JC; Department 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.
  • Bustos RH; Department 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.
Antibiotics (Basel) ; 12(2)2023 Feb 02.
Article em En | MEDLINE | ID: mdl-36830212
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.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Revista: Antibiotics (Basel) Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Revista: Antibiotics (Basel) Ano de publicação: 2023 Tipo de documento: Article