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评估预测阿米卡星清除率的肾功能方程。

Evaluation of renal function equations to predict amikacin clearance.

机构信息

a Pharmacy Service, University Hospital of Salamanca , Salamanca , Spain.

b Department of Pharmaceutical Sciences, Pharmacy Faculty, University of Salamanca, Campus Miguel de Unamuno , Salamanca , Spain.

出版信息

Expert Rev Clin Pharmacol. 2019 Aug;12(8):805-813. doi: 10.1080/17512433.2019.1637253. Epub 2019 Jul 22.

Abstract

: To evaluate the predictive performance of eight renal function equations to describe amikacin elimination in a large standard population with a wide range of age. : Retrospective study of adult hospitalized patients treated with amikacin and monitored in the clinical pharmacokinetics laboratory of a pharmacy service. Renal function was calculated as Cockcroft-Gault with total, adjusted and ideal body weight, MDRD-4, CKD-EPI, rLM, BIS1, and FAS. One compartment model with first-order elimination, including interindividual variability on clearance and volume of distribution and combined residual error model was selected as a base structural model. A pharmaco-statistical analysis was performed following a non-linear mixed effects modeling approach (NONMEM 7.3 software). : 198 patients (61 years [18-93]) and 566 measured amikacin plasma concentrations were included. All the estimated glomerular filtration rate and creatinine clearance equations evaluated described properly the data. The linear relationship between clearance and glomerular filtration rate based on rLM showed a statistically significant improvement in the fit of the data. rLM must be evaluated carefully in renal failure for amikacin dose adjustment. : Revised Lund-Malmö (rLM) and CKD-EPI showed the superior predictive performance of amikacin drug elimination comparing to all the alternative metrics evaluated.

摘要

目的

评估 8 种肾功能方程在年龄范围广泛的大标准人群中描述阿米卡星清除率的预测性能。

方法

回顾性研究了在药学服务临床药代动力学实验室接受阿米卡星治疗并监测的成年住院患者。肾功能通过 Cockcroft-Gault 公式计算,包括总体重、调整体重和理想体重、MDRD-4、CKD-EPI、rLM、BIS1 和 FAS。选择具有一级消除的单室模型,包括清除率和分布容积的个体间变异性以及组合残差模型作为基本结构模型。采用非线性混合效应建模方法(NONMEM 7.3 软件)进行药物统计分析。

结果

共纳入 198 例患者(61 岁[18-93])和 566 份测定的阿米卡星血浆浓度。所有评估的估计肾小球滤过率和肌酐清除率方程均能正确描述数据。基于 rLM 的清除率与肾小球滤过率的线性关系显示,数据拟合有统计学意义的改善。在调整阿米卡星剂量时,必须仔细评估 rLM 在肾衰竭中的应用。

结论

与评估的所有替代指标相比,修订的隆德-马尔默(rLM)和 CKD-EPI 显示出对阿米卡星药物清除率的预测性能更优。

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