A Retrospective Within-Subjects Analysis of Vancomycin Bayesian Modeling With Pre-steady-State vs Steady-State Concentrations

<b>Background:</b> Bayesian modeling of vancomycin can estimate 24-hour area under the curve (AUC<sub>24</sub>) using pre-steady-state concentrations. Limited literature exists comparing Bayesian AUC<sub>24</sub> calculations derived from steady-state versus pre-steady-state concentrations. <b>Objective:</b> To assess the agreement between vancomycin AUC<sub>24</sub> calculations using pre-steady-state versus steady-state concentrations, employing Bayesian modeling. <b>Methods:</b> This retrospective within-subjects cohort study included patients with at least 1 pre-steady-state and 1 steady-state vancomycin concentration. Patients with age >100 years, weight <40 kg, height <60 inches, or renal dysfunction were excluded. The steady-state AUC<sub>24</sub> from dosing software was documented with and without hiding steady-state levels from calculations. The primary outcome was agreement between AUC<sub>24</sub> without levels hidden compared with AUC<sub>24</sub> with steady-state levels hidden from analysis. Secondary outcomes included the agreement between AUC<sub>24</sub> with pre-steady-state levels hidden and the percentage of patients with matching AUC<sub>24</sub> categories. The AUC<sub>24</sub> agreement was evaluated via Bland-Altman plot and bias via linear regression. Statistical tests were performed using SPSS statistics software (IBM Corp). <b>Results:</b> A total of 93 patients were included. The mean difference in AUC<sub>24</sub> compared to AUC<sub>24</sub> with steady-state levels hidden was 8.8 mg*h/L, and with pre-steady-state levels hidden, it was -3.7 mg*h/L. Linear regression analysis indicated a proportional bias when steady-state levels were hidden (β = 0.22; <i>P</i> = 0.038) but not when pre-steady-state levels were hidden. Category mismatch occurred more often when steady-state levels were hidden vs when pre-steady-state levels were hidden (26% vs 8%; <i>P</i> < 0.001). <b>Conclusion and Relevance:</b> The study demonstrated overall agreement between AUC<sub>24</sub> compared to AUC<sub>24</sub> with steady-state levels hidden. The mean differences in AUC<sub>24</sub> estimates were small, no matter which level was hidden, although tighter limits of agreement were observed when steady-state levels were utilized in Bayesian calculations. Further research with larger sample sizes is necessary.

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Journal
Journal of Pharmacy Technology
Published
2025-08-21
DOI
https://doi.org/10.1177/87551225251362731
Citations
1
Primary Topic
Antimicrobial Resistance in Staphylococcus
Type
article
Field-Weighted Citation Impact
1.40
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article

A Retrospective Within-Subjects Analysis of Vancomycin Bayesian Modeling With Pre-steady-State vs Steady-State Concentrations

Elizabeth W. Covington, Sarah Grace Gunter, Jihyun L. Chae
1 citations
Journal of Pharmacy Technology
Antimicrobial Resistance in Staphylococcus
1.40
article

A Retrospective Within-Subjects Analysis of Vancomycin Bayesian Modeling With Pre-steady-State vs Steady-State Concentrations

Elizabeth W. Covington, Sarah Grace Gunter, Jihyun L. Chae
article en
1 citations

Abstract

<b>Background:</b> Bayesian modeling of vancomycin can estimate 24-hour area under the curve (AUC<sub>24</sub>) using pre-steady-state concentrations. Limited literature exists comparing Bayesian AUC<sub>24</sub> calculations derived from steady-state versus pre-steady-state concentrations. <b>Objective:</b> To assess the agreement between vancomycin AUC<sub>24</sub> calculations using pre-steady-state versus steady-state concentrations, employing Bayesian modeling. <b>Methods:</b> This retrospective within-subjects cohort study included patients with at least 1 pre-steady-state and 1 steady-state vancomycin concentration. Patients with age >100 years, weight <40 kg, height <60 inches, or renal dysfunction were excluded. The steady-state AUC<sub>24</sub> from dosing software was documented with and without hiding steady-state levels from calculations. The primary outcome was agreement between AUC<sub>24</sub> without levels hidden compared with AUC<sub>24</sub> with steady-state levels hidden from analysis. Secondary outcomes included the agreement between AUC<sub>24</sub> with pre-steady-state levels hidden and the percentage of patients with matching AUC<sub>24</sub> categories. The AUC<sub>24</sub> agreement was evaluated via Bland-Altman plot and bias via linear regression. Statistical tests were performed using SPSS statistics software (IBM Corp). <b>Results:</b> A total of 93 patients were included. The mean difference in AUC<sub>24</sub> compared to AUC<sub>24</sub> with steady-state levels hidden was 8.8 mg*h/L, and with pre-steady-state levels hidden, it was -3.7 mg*h/L. Linear regression analysis indicated a proportional bias when steady-state levels were hidden (β = 0.22; <i>P</i> = 0.038) but not when pre-steady-state levels were hidden. Category mismatch occurred more often when steady-state levels were hidden vs when pre-steady-state levels were hidden (26% vs 8%; <i>P</i> < 0.001). <b>Conclusion and Relevance:</b> The study demonstrated overall agreement between AUC<sub>24</sub> compared to AUC<sub>24</sub> with steady-state levels hidden. The mean differences in AUC<sub>24</sub> estimates were small, no matter which level was hidden, although tighter limits of agreement were observed when steady-state levels were utilized in Bayesian calculations. Further research with larger sample sizes is necessary.

Journal of Pharmacy TechnologyVol. 41(6)
University of South Alabama Medical Center (US), Auburn University (US)
Openalex Percentile: Top 17%
Antimicrobial Resistance in Staphylococcus
1.40
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