Comparison of Bayesian‐derived and first‐order analytic equations for calculation of vancomycin area under the curve

Abstract

Introduction Consensus guidelines recommend targeting a vancomycin area under the curve to minimum inhibitory concentration (AUC24:MIC) ratio of 400–600 to improve therapeutic success and reduce nephrotoxicity. Although guidelines specify either Bayesian software or first‐order equations may be used to estimate AUC24, there are currently no large studies directly comparing these methods. Objective To compare calculated vancomycin AUC24 using first‐order equations with two‐drug concentrations at steady state to Bayesian two‐ and one‐concentration estimations. Methods This was a single‐center, retrospective cohort study of 978 adult hospitalized patients receiving intravenous vancomycin between 2017 and 2019. Patients were included if they received at least 72 h of vancomycin and had two‐serum drug concentrations obtained. AUC24 was calculated using first‐order analytic (linear), Bayesian two‐concentration, and Bayesian one‐concentration methods for each patient. The InsightRx™ software platform was used to calculate Bayesian AUC24. Pearson's correlation and clinical agreement (based on AUC24 classified as subtherapeutic, therapeutic, or supratherapeutic) were used to assess agreement between methods. Bland–Altman plots were used to assess mean difference (MD) and 95% limits of agreement (LOA). Results Excellent agreement was observed between linear and Bayesian two‐concentration methods (r = 0.963, clinical agreement = 87.4%) and Bayesian two‐concentration and one‐concentration methods (r = 0.931, clinical agreement = 88.5%); however, a degree of variability was noted with 95% LOA −99 to 76 (MD = −11.5 mg*h/L) and −92 to 113 (MD = −10.4 mg*h/L), for the respective comparisons. The agreement between linear and Bayesian one‐concentration approaches was less than prior comparisons (r = 0.823, clinical agreement = 76.8%) and demonstrated the greatest amount of variability with 95% LOA −197 to 153 (MD = −21.9 mg*h/L). Conclusions Linear and Bayesian two‐concentration methods demonstrated high‐level agreement with acceptable variability and may be considered comparable to estimate vancomycin AUC24. As linear and Bayesian one‐concentration methods demonstrated significant variability and suboptimal agreement, concerns exist surrounding the interchangeability of these methods in clinical practice, particularly at higher extremes of AUC24.

Vancomycin area under the curve versus trough only guided dosing and the risk of acute kidney injury: Systematic review and meta‐analysis

“…Out of the 10 studies included in this meta‐analysis, only three utilized Bayesian software for AUC dosing. Bayesian software programs use an established pharmacokinetic model and patient parameters to optimize vancomycin dosing and account for dynamic changes such as renal function 13–15 . Bayesian software programs are the preferred method of AUC monitoring since concentrations do not need to be drawn at steady‐state, and thus, therapeutic drug monitoring can be initiated as early as the first dosing interval.…”

“…Area under the curve‐guided dosing can be performed using Bayesian dose optimization software or traditional pharmacokinetic (PK) equations. An advantage of Bayesian dosing is that it accounts for dynamic covariates such as renal function and calculates individualized patient dosing based on Bayesian priors, patients' drug concentrations, and creatinine clearance 13–15 . On the other hand, PK equations provide an estimation of AUC solely based on two vancomycin levels within the same dosing period 13,15 .…”

“…One factor to consider is that these equations rely on serum creatinine (SCr); however, SCr is a delayed marker of renal injury and may take 24–36 h to display a notable change in renal function 9 . Additionally, the Bayesian method concentrations do not need to be drawn at steady‐state and can provide an estimation of AUC prior to reaching steady‐state 13–15 . Disadvantages of the Bayesian dose optimization are the logistics associated with implementation, including cost and education 9,14 .…”

“…An advantage of Bayesian dosing is that it accounts for dynamic covariates such as renal function and calculates individualized patient dosing based on Bayesian priors, patients' drug concentrations, and creatinine clearance. [13][14][15] On the other hand, PK equations provide an estimation of AUC solely based on two vancomycin levels within the same dosing period. 13,15 One factor to consider is that these equations rely on serum creatinine (SCr); however, SCr is a delayed marker of renal injury and may take 24-36 h to display a notable change in renal function.…”

“…[13][14][15] On the other hand, PK equations provide an estimation of AUC solely based on two vancomycin levels within the same dosing period. 13,15 One factor to consider is that these equations rely on serum creatinine (SCr); however, SCr is a delayed marker of renal injury and may take 24-36 h to display a notable change in renal function. 9 Additionally, the Bayesian method concentrations do not need to be drawn at steadystate and can provide an estimation of AUC prior to reaching steadystate.…”