Bayesian-estimated vancomycin area under the curve using a single trough concentration vs 2 concentrations

Ho, Dan Liang; Arquiette, Jered; Kim, Myo-Kyoung

doi:10.1093/ajhp/zxab233

Cited by 5 publications

(4 citation statements)

References 2 publications

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“…In their study, InsightRX ® had a median accuracy (Q 1 –Q 3 ) of 0.84 (0.77–0.88), which was average compared to the other models assessed. A similar finding was published using PrecisePK Bayesian modeling software for 65 AUC calculations [ 17 ].…”

Section: Discussionsupporting

confidence: 80%

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

et al. 2022

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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 Q1–Q3 7.8–12.4) compared to 6.8 min (Q1–Q3 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 (Q1–Q3 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.

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Section: Discussionsupporting

confidence: 80%

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

et al. 2022

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“…Within the work by Olney et al, 6 89% of therapy classifications matched between Bayesian one-concentration and two-concentration AUC24 estimates. Ho et al 9 also observed an 89% agreement in therapy classifications when using a single trough concentration versus two concentrations. This compares with the 87% AUC24 category matching found within our study.…”

Section: Discussionmentioning

confidence: 89%

Agreement Between Two-Concentration and One-Concentration Area Under the Curve (AUC) Estimates When Using Bayesian Modeling to Dose Vancomycin in Patients With Obesity

Covington,

Watkins

2023

Ann Pharmacother

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Background: Vancomycin Bayesian modeling provides 24-hour area under the curve (AUC24) estimations. However, the agreement between two-concentration and one-concentration Bayesian estimates in patients with obesity is unknown. Objective: The purpose of this study was to determine the agreement between two-concentration and one-concentration Bayesian AUC24 estimates in patients with obesity receiving vancomycin. Methods: This retrospective within-subjects cohort study included patients with obesity and two vancomycin concentrations. The first concentration was hidden from dosing software to record the one-concentration AUC24. AUC24 estimates were categorized into 1 of 3 groups: <400, 400 to 600, or >600 mg*h/L. Patients were excluded for vancomycin duration less than 48 hours or renal dysfunction. The primary outcome was AUC24 agreement with two versus one concentration. Secondary outcomes included the AUC24 category, matching of AUC24 categorization, and correlation between two-concentration versus one-concentration AUC24. AUC24 estimate agreement was assessed by Bland Altman plot and bias via linear regression. Statistical analyses were performed using SPSS (version 20.0). Results: A total of 31 patients were included. The mean difference in AUC24 between two versus one concentration was 11.4 mg*h/L (95% limits of agreement = −72 to 95 mg*h/L). Linear regression indicated the presence of proportional bias at higher AUC24 values (β = 0.16; P = 0.015). Matching of AUC24 categorization with two versus one concentration was 87% (27/31 patients). Conclusion and Relevance: This study demonstrated overall agreement between AUC24 estimates when using two versus one vancomycin concentration in patients with obesity, though proportional bias was detected at higher AUC24. Future studies with larger sample sizes are needed to confirm these results.

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“…10 However, a recent retrospective study including 239 patients provided additional support to the findings of Neely et al for trough-only sampling to provide accurate AUC estimation. 12,13 Therefore, an advantage of Bayesian software is the need for only one trough concentration of which can be collected within the first 24 to 48 hours rather than waiting for the patient to reach steady-state. 5,12 Bayesian programs also provide treatment schemes and the ability to integrate covariates to account for patients with rapidly changing physiology such as that seen with variations in renal clearance.…”

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confidence: 99%

“…12,13 Therefore, an advantage of Bayesian software is the need for only one trough concentration of which can be collected within the first 24 to 48 hours rather than waiting for the patient to reach steady-state. 5,12 Bayesian programs also provide treatment schemes and the ability to integrate covariates to account for patients with rapidly changing physiology such as that seen with variations in renal clearance. 5 However, Bayesian programs are very costly and comes as its biggest disadvantage to integrate into practice.…”

mentioning

confidence: 99%

Out With the Old, in With the New: What Rising Pharmacists Need to Know About Vancomycin Therapeutic Drug Monitoring in Adults

Cook

Pouliot

Parker

2023

Journal of Pharmacy Practice

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Bayesian-estimated vancomycin area under the curve using a single trough concentration vs 2 concentrations

Cited by 5 publications

References 2 publications

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

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

Agreement Between Two-Concentration and One-Concentration Area Under the Curve (AUC) Estimates When Using Bayesian Modeling to Dose Vancomycin in Patients With Obesity

Out With the Old, in With the New: What Rising Pharmacists Need to Know About Vancomycin Therapeutic Drug Monitoring in Adults

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