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dc.contributor.authorMena, Manuel
dc.contributor.authorGarcia, Julio Cesar
dc.contributor.authorBustos, Rosa Helena
dc.date.accessioned2024-01-12T13:17:10Z
dc.date.available2024-01-12T13:17:10Z
dc.date.issued2023-08-09
dc.identifier.urihttp://hdl.handle.net/10818/59036
dc.description13 páginases_CO
dc.description.abstractIn 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.es_CO
dc.formatapplication/pdfes_CO
dc.language.isoenges_CO
dc.publisherUniversidad de La Sabanaes_CO
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subject.otherVancomycin
dc.subject.otherBayesian prediction
dc.subject.otherPopulation pharmacokinetics
dc.subject.otherPersonalized dosing
dc.subject.otherIndividualized therapy
dc.subject.otherCritical ill patients
dc.subject.otherTherapeutic drug management
dc.subject.otherShiny application
dc.titleImplementing Vancomycin Population Pharmacokinetic Models: An App for Individualized Antibiotic Therapy in Critically Ill Patientses_CO
dc.typebachelor thesises_CO
dc.type.hasVersionpublishedVersiones_CO
dc.rights.accessRightsopenAccesses_CO
dc.identifier.doi10.3390/antibiotics12020301
thesis.degree.disciplineFacultad de Medicinaes_CO
thesis.degree.levelEspecialización en Farmacología Clínicaes_CO
thesis.degree.nameEspecialista en Farmacología Clínicaes_CO


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Attribution-NonCommercial-NoDerivatives 4.0 InternacionalExcepto si se señala otra cosa, la licencia del ítem se describe como Attribution-NonCommercial-NoDerivatives 4.0 Internacional