Dataset Information

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 .

SUBMITTER: Olney KB

PROVIDER: S-EPMC9750735 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature

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Publications

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

Olney Katie B KB Wallace Katie L KL Mynatt Ryan P RP Burgess David S DS Grieves Kaitlyn K Willett Austin A Mani Johann J Flannery Alexander H AH

Pharmacotherapy 20220217 4

<h4>Introduction</h4>Consensus guidelines recommend targeting a vancomycin area under the curve to minimum inhibitory concentration (AUC<sub>24</sub> :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 AUC<sub>24</sub> , there are currently no large studies directly comparing these methods.<h4>Objective</h4>To compare calculated vancomycin AUC<sub>24</sub> using firs ...[more]

PMID: 35134264

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Project description:ObjectivesTo assess differences in vancomycin AUC estimates from two common, clinically applied first-order pharmacokinetic equation methods compared with Bayesian estimates.MethodsA cohort of patients who received vancomycin and therapeutic drug monitoring was studied. First-order population pharmacokinetic equations were used to guide initial empirical dosing. After receipt of the first dose, patients had peak and trough serum levels drawn and steady-state AUC was estimated using first-order pharmacokinetic equations as standard care. We subsequently created a Bayesian model and used individual Empirical Bayes Estimates to precisely calculate vancomycin AUC24-48, AUC48-72 and AUC72-96 in this cohort. AUC at steady state (AUCSS) differences from the first-order methods were compared numerically and categorically (i.e. below, within or above 400-600 mg·h/L) to Bayesian AUCs, which served as the gold standard.ResultsA total of 65 adult inpatients with 409 plasma samples were included in this analysis. A two-compartment intravenous infusion model with first-order elimination fit the data well. The mean of Bayesian AUC24-48 was not significantly different from AUC estimates from the two first-order pharmacokinetic equation methods (P = 0.68); however, Bayesian AUC48-72 and Bayesian AUC72-96 were both significantly different when compared with both first-order pharmacokinetic equation methods (P < 0.01 for each). At the patient level, categorical classifications of AUC estimates from the two first-order pharmacokinetic equation methods differed from categorizations derived from the Bayesian calculations. Categorical agreement was ∼50% between first-order and Bayesian calculations, with declining categorical agreement observed with longer treatment courses. Differences in categorical agreement between calculation methods could potentially result in different dose recommendations for the patient.ConclusionsBayesian-calculated AUCs between 48-72 and 72-96 h intervals were significantly different from first-order pharmacokinetic method-estimated AUCs at steady state. The various calculation methods resulted in different categorical classification, which could potentially lead to erroneous dosing adjustments in approximately half of the patients.

Project description:IntroductionGuidelines for vancomycin therapeutic monitoring recommend using a Bayesian approach with a population pharmacokinetic model to estimate the 24 h area under the concentration-time curve over first-order equations. Thus, we performed an external evaluation of population pharmacokinetic models of vancomycin in neonates and compared Bayesian results with those observed in clinical practice via pharmacokinetic equations to improve therapeutic monitoring by proposing optimized initial dosing nomograms and assessing the feasibility of reduced blood sampling strategies using the most predictive models.MethodsModels were identified from the literature and evaluated via an external neonatal population. A priori predictive performance was first assessed by prediction-based diagnostics, then by simulation-based diagnostics and a posteriori analyses only if deemed satisfactory; model-informed vancomycin exposure was also compared with reference first-order pharmacokinetic equations. The best-performing models were ultimately subjected to Monte Carlo simulations to develop new initial dosing nomograms offering the highest probability of achieving therapeutic target.ResultsA total of 28 population pharmacokinetic models were evaluated in the external dataset, which includes 72 neonates and 380 vancomycin concentrations. Eleven models had an adequate predictive performance with bias ≤ ± 15% and imprecision ≤ 30%, while the Bayesian approach yielded over 75% agreement with reference exposure values in most cases. Nonetheless, Capparelli et al. and Mehrotra et al. models performed the best overall, showing the lowest imprecisions of 16.8% and 16.9%, respectively; both models recommended higher dosage regimens than the theoretical nomogram currently applied to favor therapeutic target attainment.DiscussionWe externally evaluated numerous neonatal population pharmacokinetic models of vancomycin and used the most predictive ones to advocate new initial dosing nomograms. Clinical implementation of the Bayesian approach could reduce the time needed to reach therapeutic target and limit the number of blood samples in newborns compared with traditional pharmacokinetic equations.

Project description:Evidence suggests that maintenance of vancomycin trough concentrations at between 15 and 20 mg/liter, as currently recommended, is frequently unnecessary to achieve the daily area under the concentration-time curve (AUC24) target of ≥400 mg · h/liter. Many patients with trough concentrations in this range have AUC24 values in excess of the therapeutic threshold and within the exposure range associated with nephrotoxicity. On the basis of this, the Detroit Medical Center switched from trough concentration-guided dosing to AUC-guided dosing to minimize potentially unnecessary vancomycin exposure. The primary objective of this analysis was to assess the impact of this intervention on vancomycin-associated nephrotoxicity in a single-center, retrospective quasi-experiment of hospitalized adult patients receiving intravenous vancomycin from 2014 to 2015. The primary analysis compared the incidence of nephrotoxicity between patients monitored by assessment of the AUC24 and those monitored by assessment of the trough concentration. Multivariable logistic and Cox proportional hazards regression examined the independent association between the monitoring strategy and nephrotoxicity. Secondary analysis compared vancomycin exposures (total daily dose, AUC, and trough concentrations) between monitoring strategies. Overall, 1,280 patients were included in the analysis. After adjusting for severity of illness, comorbidity, duration of vancomycin therapy, and concomitant receipt of nephrotoxins, AUC-guided dosing was independently associated with lower nephrotoxicity by both logistic regression (odds ratio, 0.52; 95% confidence interval [CI], 0.34 to 0.80; P = 0.003) and Cox proportional hazards regression (hazard ratio, 0.53; 95% CI, 0.35 to 0.78; P = 0.002). AUC-guided dosing was associated with lower total daily vancomycin doses, AUC values, and trough concentrations. Vancomycin AUC-guided dosing was associated with reduced nephrotoxicity, which appeared to be a result of reduced vancomycin exposure.

Dataset Information

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

Introduction

Objective

Methods

Results

Conclusions

Publications

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

Similar Datasets

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