Cargando…

Vancomycin Area under the Concentration-Time Curve Estimation Using Bayesian Modeling versus First-Order Pharmacokinetic Equations: A Quasi-Experimental Study

Aim: To evaluate the efficiency of Bayesian modeling software and first-order pharmacokinetic (PK) equations to calculate vancomycin area under the concentration-time curve (AUC) estimations. Methods: Unblinded, crossover, quasi-experimental study at a tertiary care hospital for patients receiving i...

Descripción completa

Detalles Bibliográficos
Autores principales: Alsowaida, Yazed Saleh, Kubiak, David W., Dionne, Brandon, Kovacevic, Mary P., Pearson, Jeffrey C.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9495010/
https://www.ncbi.nlm.nih.gov/pubmed/36140021
http://dx.doi.org/10.3390/antibiotics11091239
_version_ 1784793917968875520
author Alsowaida, Yazed Saleh
Kubiak, David W.
Dionne, Brandon
Kovacevic, Mary P.
Pearson, Jeffrey C.
author_facet Alsowaida, Yazed Saleh
Kubiak, David W.
Dionne, Brandon
Kovacevic, Mary P.
Pearson, Jeffrey C.
author_sort Alsowaida, Yazed Saleh
collection PubMed
description Aim: To evaluate the efficiency of Bayesian modeling software and first-order pharmacokinetic (PK) equations to calculate vancomycin area under the concentration-time curve (AUC) estimations. Methods: Unblinded, crossover, quasi-experimental study at a tertiary care hospital for patients receiving intravenous vancomycin. Vancomycin AUC monitoring was compared using Bayesian modeling software or first-order PK equations. The primary endpoint was the time taken to estimate the AUC and determine regimen adjustments. Secondary endpoints included the percentage of vancomycin concentrations usable for AUC calculations and acute kidney injury (AKI). Results: Of the 124 patients screened, 34 patients had usable vancomycin concentrations that led to 44 AUC estimations. Without electronic health record (EHR) integration, the time from assessment to intervention in the Bayesian modeling platform was a median of 9.3 min (quartiles Q(1)–Q(3) 7.8–12.4) compared to 6.8 min (Q(1)–Q(3) 4.8–8.0) in the PK equations group (p = 0.004). With simulated Bayesian software integration into the EHR, however, the median time was 3.8 min (Q(1)–Q(3) 2.3–6.9, p = 0.019). Vancomycin concentrations were usable in 88.2% in the Bayesian group compared to 48.3% in the PK equation group and there were no cases of AKI. Conclusion: Without EHR integration, Bayesian software was more time-consuming to assess vancomycin dosing than PK equations. With simulated integration, however, Bayesian software was more time efficient. In addition, vancomycin concentrations were more likely to be usable for calculations in the Bayesian group.
format Online
Article
Text
id pubmed-9495010
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-94950102022-09-23 Vancomycin Area under the Concentration-Time Curve Estimation Using Bayesian Modeling versus First-Order Pharmacokinetic Equations: A Quasi-Experimental Study Alsowaida, Yazed Saleh Kubiak, David W. Dionne, Brandon Kovacevic, Mary P. Pearson, Jeffrey C. Antibiotics (Basel) Article Aim: To evaluate the efficiency of Bayesian modeling software and first-order pharmacokinetic (PK) equations to calculate vancomycin area under the concentration-time curve (AUC) estimations. Methods: Unblinded, crossover, quasi-experimental study at a tertiary care hospital for patients receiving intravenous vancomycin. Vancomycin AUC monitoring was compared using Bayesian modeling software or first-order PK equations. The primary endpoint was the time taken to estimate the AUC and determine regimen adjustments. Secondary endpoints included the percentage of vancomycin concentrations usable for AUC calculations and acute kidney injury (AKI). Results: Of the 124 patients screened, 34 patients had usable vancomycin concentrations that led to 44 AUC estimations. Without electronic health record (EHR) integration, the time from assessment to intervention in the Bayesian modeling platform was a median of 9.3 min (quartiles Q(1)–Q(3) 7.8–12.4) compared to 6.8 min (Q(1)–Q(3) 4.8–8.0) in the PK equations group (p = 0.004). With simulated Bayesian software integration into the EHR, however, the median time was 3.8 min (Q(1)–Q(3) 2.3–6.9, p = 0.019). Vancomycin concentrations were usable in 88.2% in the Bayesian group compared to 48.3% in the PK equation group and there were no cases of AKI. Conclusion: Without EHR integration, Bayesian software was more time-consuming to assess vancomycin dosing than PK equations. With simulated integration, however, Bayesian software was more time efficient. In addition, vancomycin concentrations were more likely to be usable for calculations in the Bayesian group. MDPI 2022-09-13 /pmc/articles/PMC9495010/ /pubmed/36140021 http://dx.doi.org/10.3390/antibiotics11091239 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Alsowaida, Yazed Saleh
Kubiak, David W.
Dionne, Brandon
Kovacevic, Mary P.
Pearson, Jeffrey C.
Vancomycin Area under the Concentration-Time Curve Estimation Using Bayesian Modeling versus First-Order Pharmacokinetic Equations: A Quasi-Experimental Study
title Vancomycin Area under the Concentration-Time Curve Estimation Using Bayesian Modeling versus First-Order Pharmacokinetic Equations: A Quasi-Experimental Study
title_full Vancomycin Area under the Concentration-Time Curve Estimation Using Bayesian Modeling versus First-Order Pharmacokinetic Equations: A Quasi-Experimental Study
title_fullStr Vancomycin Area under the Concentration-Time Curve Estimation Using Bayesian Modeling versus First-Order Pharmacokinetic Equations: A Quasi-Experimental Study
title_full_unstemmed Vancomycin Area under the Concentration-Time Curve Estimation Using Bayesian Modeling versus First-Order Pharmacokinetic Equations: A Quasi-Experimental Study
title_short Vancomycin Area under the Concentration-Time Curve Estimation Using Bayesian Modeling versus First-Order Pharmacokinetic Equations: A Quasi-Experimental Study
title_sort vancomycin area under the concentration-time curve estimation using bayesian modeling versus first-order pharmacokinetic equations: a quasi-experimental study
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9495010/
https://www.ncbi.nlm.nih.gov/pubmed/36140021
http://dx.doi.org/10.3390/antibiotics11091239
work_keys_str_mv AT alsowaidayazedsaleh vancomycinareaundertheconcentrationtimecurveestimationusingbayesianmodelingversusfirstorderpharmacokineticequationsaquasiexperimentalstudy
AT kubiakdavidw vancomycinareaundertheconcentrationtimecurveestimationusingbayesianmodelingversusfirstorderpharmacokineticequationsaquasiexperimentalstudy
AT dionnebrandon vancomycinareaundertheconcentrationtimecurveestimationusingbayesianmodelingversusfirstorderpharmacokineticequationsaquasiexperimentalstudy
AT kovacevicmaryp vancomycinareaundertheconcentrationtimecurveestimationusingbayesianmodelingversusfirstorderpharmacokineticequationsaquasiexperimentalstudy
AT pearsonjeffreyc vancomycinareaundertheconcentrationtimecurveestimationusingbayesianmodelingversusfirstorderpharmacokineticequationsaquasiexperimentalstudy