BACKGROUND: Vancomycin area under the concentration-time curve (AUC) has been linked to efficacy and safety. An accurate method of calculating the AUC is needed. METHODS: Bayesian dose-optimizing software programs available for clinician use and first-order pharmacokinetic equations were evaluated for their ability to estimate vancomycin AUC. A previously published rich pharmacokinetic data set of 19 critically ill patients was used for validation of the AUC estimation. The AUC estimated using subsets of the full data set by Bayesian software and equations was compared with the reference AUC. Accuracy (ratio of estimated AUC to the reference AUC) and bias (percentage difference of estimated AUC to reference AUC) were calculated. RESULTS: Five Bayesian dose-optimizing software programs (Adult and Pediatric Kinetics [APK], BestDose, DoseMe, InsightRx, and Precise PK) and two first-order pharmacokinetic equations were included. Of the Bayesian programs, InsightRx was the most adaptable, visually appealing, easiest to use, and had the most company support. Utilizing only the trough, accuracy (range 0.79-1.03) and bias (range 5.1-21.2%) of the Bayesian dose-optimizing software were variable. Precise PK and BestDose had the most accurate estimates with the accuracy values of BestDose exhibiting the most variability of all the programs; however, both programs were more difficult to use. Precise PK was the least biased (median 5.1%). Using a single nontrough value produced similar results to that of the trough for most programs. The addition of a second level to the trough improved the accuracy and bias for DoseMe and InsightRx but not Precise PK and BestDose. APK did not reliably estimate the AUC with input of two levels. Using two levels, the pharmacokinetic equations produced similar or better accuracy and bias as compared with Bayesian software. CONCLUSION: Bayesian dose-optimizing software using one or more vancomycin levels and pharmacokinetic equations utilizing two vancomycin levels produce similar estimates of the AUC.
BACKGROUND:Vancomycin area under the concentration-time curve (AUC) has been linked to efficacy and safety. An accurate method of calculating the AUC is needed. METHODS: Bayesian dose-optimizing software programs available for clinician use and first-order pharmacokinetic equations were evaluated for their ability to estimate vancomycin AUC. A previously published rich pharmacokinetic data set of 19 critically ill patients was used for validation of the AUC estimation. The AUC estimated using subsets of the full data set by Bayesian software and equations was compared with the reference AUC. Accuracy (ratio of estimated AUC to the reference AUC) and bias (percentage difference of estimated AUC to reference AUC) were calculated. RESULTS: Five Bayesian dose-optimizing software programs (Adult and Pediatric Kinetics [APK], BestDose, DoseMe, InsightRx, and Precise PK) and two first-order pharmacokinetic equations were included. Of the Bayesian programs, InsightRx was the most adaptable, visually appealing, easiest to use, and had the most company support. Utilizing only the trough, accuracy (range 0.79-1.03) and bias (range 5.1-21.2%) of the Bayesian dose-optimizing software were variable. Precise PK and BestDose had the most accurate estimates with the accuracy values of BestDose exhibiting the most variability of all the programs; however, both programs were more difficult to use. Precise PK was the least biased (median 5.1%). Using a single nontrough value produced similar results to that of the trough for most programs. The addition of a second level to the trough improved the accuracy and bias for DoseMe and InsightRx but not Precise PK and BestDose. APK did not reliably estimate the AUC with input of two levels. Using two levels, the pharmacokinetic equations produced similar or better accuracy and bias as compared with Bayesian software. CONCLUSION: Bayesian dose-optimizing software using one or more vancomycin levels and pharmacokinetic equations utilizing two vancomycin levels produce similar estimates of the AUC.
Authors: Laura Evans; Andrew Rhodes; Waleed Alhazzani; Massimo Antonelli; Craig M Coopersmith; Craig French; Flávia R Machado; Lauralyn Mcintyre; Marlies Ostermann; Hallie C Prescott; Christa Schorr; Steven Simpson; W Joost Wiersinga; Fayez Alshamsi; Derek C Angus; Yaseen Arabi; Luciano Azevedo; Richard Beale; Gregory Beilman; Emilie Belley-Cote; Lisa Burry; Maurizio Cecconi; John Centofanti; Angel Coz Yataco; Jan De Waele; R Phillip Dellinger; Kent Doi; Bin Du; Elisa Estenssoro; Ricard Ferrer; Charles Gomersall; Carol Hodgson; Morten Hylander Møller; Theodore Iwashyna; Shevin Jacob; Ruth Kleinpell; Michael Klompas; Younsuck Koh; Anand Kumar; Arthur Kwizera; Suzana Lobo; Henry Masur; Steven McGloughlin; Sangeeta Mehta; Yatin Mehta; Mervyn Mer; Mark Nunnally; Simon Oczkowski; Tiffany Osborn; Elizabeth Papathanassoglou; Anders Perner; Michael Puskarich; Jason Roberts; William Schweickert; Maureen Seckel; Jonathan Sevransky; Charles L Sprung; Tobias Welte; Janice Zimmerman; Mitchell Levy Journal: Intensive Care Med Date: 2021-10-02 Impact factor: 17.440