目的 对可根据血药浓度监测数据估算%T>MIC的方法进行比较评价,明确治疗药物监测实施方案和治疗目标。方法 收集25例重症感染患者给予亚胺培南达稳后多时间点的血药浓度数据,以NONMEM法建立群体药动学模型并以Bayesian反馈法结合给药后6、8 h血药浓度点估算%T>MIC,同时以已建立的%T>MIC计算模型方法结合相同血药浓度进行估算,与%T>MIC真实值进行比较。结果 建立的群体药动学模型可对数据进行较好拟合,协变量血清肌酐(CR)对中央室表观分布容积(Vc)有显著影响,最终模型为 Vc=18.8×(CR/70.9)ΘCR_VC。当MIC=2时,Bayesian法和%T>MIC计算模型法预测结果偏差在真实值±10%以内的比例分别为76.9%及84.6%。结论 两种方法在MIC折点≤2时预测准确性良好,但准确性随MIC增加而下降,针对不同的MIC水平可考虑采取不同治疗药物监测方案。
Abstract
OBJECTIVE To compare and evaluate different methods of estimating %T>MIC based on drug concentration monitoring data, and clarify the implementation plan and treatment objectives of therapeutic drug monitoring. METHODS The plasma drug concentrations of 25 patients with severe infections were collected at multiple time points after imipenem reached steady state. A population pharmacokinetic model was established by NONMEM method. The plasma drug concentrations were estimated by Bayesian feedback method at 6 and 8 h after imipenem administration. Meanwhile, the established calculation model of %T>MIC combined with the same drug concentrations was used to estimate the %T>MIC. The results of these two methods were compared with the true value of %T>MIC. RESULTS The established population pharmacokinetic model could fit the data well. The covariate serum creatinine (CR) had a significant effect on the apparent distribution volume (Vc) of central ventricle. The final model was Vc=18.8×(CR/70.9)ΘCR_VC. When MIC=2, the results of Bayesian method and %T>MIC calculation model method showed 76.9% and 84.6% deviation within±10% of the true values, respectively. CONCLUSION The two methods had good predictive accuracy when the MIC breakpoint was less than 2, but they decreased with the increase of MIC. Different therapeutic drug monitoring schemes should be considered for different levels of MIC.
关键词
亚胺培南 /
Bayesian反馈法 /
时间依赖型 /
f%T>MIC /
重症
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Key words
imipenem /
Bayesian feedback method /
time-dependent /
%T>MIC /
critical care
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中图分类号：
R969
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参考文献
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