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Discrepancies Between Bayesian Vancomycin Models Can Affect Clinical Decisions in the Critically Ill

PURPOSE: To assess the agreement in 24-hour area under the curve (AUC(24)) value estimates between commonly used vancomycin population pharmacokinetic models in the critically ill. MATERIALS AND METHODS: Adults admitted to intensive care who received intravenous vancomycin and had a serum vancomycin...

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Autores principales: Patanwala, Asad E., Spremo, Danijela, Jeon, Minji, Thoma, Yann, C. Alffenaar, Jan-Willem, Stocker, Sophie
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9767744/
https://www.ncbi.nlm.nih.gov/pubmed/36561549
http://dx.doi.org/10.1155/2022/7011376
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author Patanwala, Asad E.
Spremo, Danijela
Jeon, Minji
Thoma, Yann
C. Alffenaar, Jan-Willem
Stocker, Sophie
author_facet Patanwala, Asad E.
Spremo, Danijela
Jeon, Minji
Thoma, Yann
C. Alffenaar, Jan-Willem
Stocker, Sophie
author_sort Patanwala, Asad E.
collection PubMed
description PURPOSE: To assess the agreement in 24-hour area under the curve (AUC(24)) value estimates between commonly used vancomycin population pharmacokinetic models in the critically ill. MATERIALS AND METHODS: Adults admitted to intensive care who received intravenous vancomycin and had a serum vancomycin concentration available were included. AUC(24) values were determined using Tucuxi (revision cd7bd7a8) for dosing intervals with a vancomycin concentration using three models (Goti 2018, Colin 2019, and Thomson 2009) previously evaluated in the critically ill. AUC(24) values were categorized as subtherapeutic (<400 mg·h/L), therapeutic (400–600 mg·h/L), or toxic (>600 mg·h/L), assuming a minimum inhibitory concentration of 1 mg/L. AUC(24) value categorization was compared across the three models and reported as percent agreement. RESULTS: Overall, 466 AUC(24) values were estimated in 188 patients. Overall, 52%, 42%, and 47% of the AUC(24) values were therapeutic for the Goti, Colin, and Thomson models, respectively. The agreement of AUC(24) values between all three models was 48% (223/466), Goti-Colin 59% (193/466), Goti-Thomson 68% (318/466), and Colin-Thomson 67% (314/466). CONCLUSION: In critically ill patients, vancomycin AUC(24) values obtained from different pharmacokinetic models are often discordant, potentially contributing to differences in dosing decisions. This highlights the importance of selecting the optimal model.
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spelling pubmed-97677442022-12-21 Discrepancies Between Bayesian Vancomycin Models Can Affect Clinical Decisions in the Critically Ill Patanwala, Asad E. Spremo, Danijela Jeon, Minji Thoma, Yann C. Alffenaar, Jan-Willem Stocker, Sophie Crit Care Res Pract Research Article PURPOSE: To assess the agreement in 24-hour area under the curve (AUC(24)) value estimates between commonly used vancomycin population pharmacokinetic models in the critically ill. MATERIALS AND METHODS: Adults admitted to intensive care who received intravenous vancomycin and had a serum vancomycin concentration available were included. AUC(24) values were determined using Tucuxi (revision cd7bd7a8) for dosing intervals with a vancomycin concentration using three models (Goti 2018, Colin 2019, and Thomson 2009) previously evaluated in the critically ill. AUC(24) values were categorized as subtherapeutic (<400 mg·h/L), therapeutic (400–600 mg·h/L), or toxic (>600 mg·h/L), assuming a minimum inhibitory concentration of 1 mg/L. AUC(24) value categorization was compared across the three models and reported as percent agreement. RESULTS: Overall, 466 AUC(24) values were estimated in 188 patients. Overall, 52%, 42%, and 47% of the AUC(24) values were therapeutic for the Goti, Colin, and Thomson models, respectively. The agreement of AUC(24) values between all three models was 48% (223/466), Goti-Colin 59% (193/466), Goti-Thomson 68% (318/466), and Colin-Thomson 67% (314/466). CONCLUSION: In critically ill patients, vancomycin AUC(24) values obtained from different pharmacokinetic models are often discordant, potentially contributing to differences in dosing decisions. This highlights the importance of selecting the optimal model. Hindawi 2022-11-17 /pmc/articles/PMC9767744/ /pubmed/36561549 http://dx.doi.org/10.1155/2022/7011376 Text en Copyright © 2022 Asad E. Patanwala et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Patanwala, Asad E.
Spremo, Danijela
Jeon, Minji
Thoma, Yann
C. Alffenaar, Jan-Willem
Stocker, Sophie
Discrepancies Between Bayesian Vancomycin Models Can Affect Clinical Decisions in the Critically Ill
title Discrepancies Between Bayesian Vancomycin Models Can Affect Clinical Decisions in the Critically Ill
title_full Discrepancies Between Bayesian Vancomycin Models Can Affect Clinical Decisions in the Critically Ill
title_fullStr Discrepancies Between Bayesian Vancomycin Models Can Affect Clinical Decisions in the Critically Ill
title_full_unstemmed Discrepancies Between Bayesian Vancomycin Models Can Affect Clinical Decisions in the Critically Ill
title_short Discrepancies Between Bayesian Vancomycin Models Can Affect Clinical Decisions in the Critically Ill
title_sort discrepancies between bayesian vancomycin models can affect clinical decisions in the critically ill
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9767744/
https://www.ncbi.nlm.nih.gov/pubmed/36561549
http://dx.doi.org/10.1155/2022/7011376
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