Asad E Patanwala, Danijela Spremo, Minji Jeon, Yann Thoma, Jan-Willem C Alffenaar, Sophie Stocker
{"title":"Discrepancies Between Bayesian Vancomycin Models Can Affect Clinical Decisions in the Critically Ill.","authors":"Asad E Patanwala, Danijela Spremo, Minji Jeon, Yann Thoma, Jan-Willem C Alffenaar, Sophie Stocker","doi":"10.1155/2022/7011376","DOIUrl":null,"url":null,"abstract":"<p><strong>Purpose: </strong>To assess the agreement in 24-hour area under the curve (AUC<sub>24</sub>) value estimates between commonly used vancomycin population pharmacokinetic models in the critically ill.</p><p><strong>Materials and methods: </strong>Adults admitted to intensive care who received intravenous vancomycin and had a serum vancomycin concentration available were included. AUC<sub>24</sub> 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<sub>24</sub> 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<sub>24</sub> value categorization was compared across the three models and reported as percent agreement.</p><p><strong>Results: </strong>Overall, 466 AUC<sub>24</sub> values were estimated in 188 patients. Overall, 52%, 42%, and 47% of the AUC<sub>24</sub> values were therapeutic for the Goti, Colin, and Thomson models, respectively. The agreement of AUC<sub>24</sub> 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).</p><p><strong>Conclusion: </strong>In critically ill patients, vancomycin AUC<sub>24</sub> 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.</p>","PeriodicalId":46583,"journal":{"name":"Critical Care Research and Practice","volume":null,"pages":null},"PeriodicalIF":1.8000,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9767744/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Critical Care Research and Practice","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1155/2022/7011376","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"CRITICAL CARE MEDICINE","Score":null,"Total":0}
引用次数: 0
Abstract
Purpose: To assess the agreement in 24-hour area under the curve (AUC24) 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. AUC24 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. AUC24 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. AUC24 value categorization was compared across the three models and reported as percent agreement.
Results: Overall, 466 AUC24 values were estimated in 188 patients. Overall, 52%, 42%, and 47% of the AUC24 values were therapeutic for the Goti, Colin, and Thomson models, respectively. The agreement of AUC24 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 AUC24 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.