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目标万古霉素药时曲线下面积时,计算方法是否重要?

Does calculation method matter for targeting vancomycin area under the curve?

机构信息

Midwestern University College of Pharmacy, Department of Pharmacy Practice, Downers Grove, IL, USA.

Midwestern University College of Pharmacy, Pharmacometrics Center of Excellence, Downers Grove, IL, USA.

出版信息

J Antimicrob Chemother. 2022 Jul 28;77(8):2245-2250. doi: 10.1093/jac/dkac151.

Abstract

OBJECTIVES

To assess differences in vancomycin AUC estimates from two common, clinically applied first-order pharmacokinetic equation methods compared with Bayesian estimates.

METHODS

A 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.

RESULTS

A 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.

CONCLUSIONS

Bayesian-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.

摘要

目的

评估两种常用的一阶药代动力学方程方法与贝叶斯估计相比,在万古霉素 AUC 估计值方面的差异。

方法

本研究纳入了接受万古霉素治疗并进行治疗药物监测的患者队列。使用一阶群体药代动力学方程指导初始经验性给药。首次给药后,患者抽取峰浓度和谷浓度,并用一阶药代动力学方程作为标准方法估算稳态 AUC。随后,我们建立了一个贝叶斯模型,并使用个体经验贝叶斯估计值精确计算该队列中万古霉素 AUC24-48、AUC48-72 和 AUC72-96。通过数值和分类(即低于、等于或高于 400-600mg·h/L)比较稳态时一阶方法 AUC 差异与贝叶斯 AUC,后者作为金标准。

结果

本分析共纳入 65 例成人住院患者,共 409 个血浆样本。具有一阶消除的两室静脉输注模型很好地拟合了数据。贝叶斯 AUC24-48 的平均值与两种一阶药代动力学方程方法的 AUC 估计值无显著差异(P=0.68);然而,与两种一阶药代动力学方程方法相比,贝叶斯 AUC48-72 和贝叶斯 AUC72-96 均有显著差异(P<0.01)。在患者水平上,两种一阶药代动力学方程方法的 AUC 估计值的分类分类与贝叶斯计算得出的分类不同。两种一阶药代动力学方程方法与贝叶斯计算之间的分类一致性约为 50%,随着治疗过程的延长,分类一致性逐渐下降。计算方法之间的分类一致性差异可能导致对患者的不同剂量建议。

结论

48-72 和 72-96h 间隔的贝叶斯计算 AUC 与稳态时的一阶药代动力学方法估计 AUC 有显著差异。各种计算方法导致不同的分类,这可能导致大约一半的患者剂量调整错误。

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Making the change to area under the curve-based vancomycin dosing.改为基于曲线下面积的万古霉素给药方案。
Am J Health Syst Pharm. 2018 Dec 15;75(24):1986-1995. doi: 10.2146/ajhp180034. Epub 2018 Oct 17.

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