用药代动力学软件预测氨基糖苷类抗生素血浓度的准确性

Accuracy of prediction on aminoglycosides using Abbottbase pharmacokinetics systems program

目的 评价药代动力学软件(PKS)预测氨基糖苷类抗生素血浓度的准确性。方法 严重感染病人53例,静脉点滴氨基糖苷类抗生素q 6 h共3天。血药浓度测定用偏振荧光免疫测定法。用PKS软件Bayesian模式,以首次给药后峰和低浓度为反馈值,对以后每剂量的峰和谷浓度作预测。预测准确性和精确性用平均预测误差(ME)、均方预测误差(MSE)、均方预测误差平方根(RMSE)及其95％可信限表示。将24,48,72h峰、谷浓度实测值与预测值做配对t检验。结果 303次血药浓度实测值供预测。ME,MSE,RMSE及其95％可信限均在临床可接受范围内。24和48h峰浓度预测值和观察值间无显著差异(P>0.05),72 h组问差异有显著性(P<0.05)。上述3个时间点的谷浓度预测值和观察值间均有显著性差异(P<0.01)。结论 用PKS可准确预测氨基糖苷类静脉给药后4 8 h内每一剂量的峰浓度,但谷浓度预测受限。

Objective To investigate the accuracy of Bayesian fitting for predicting aminoglycoside concentrations using Abbottbase pharmacokinetic systems program (PKS). Methods Data come from 53 patients treated with gentamicin and tobramycin iv injection in Goettingen University Hospital. Bayesian fitting of PKS was used to predict the peak and trough levels afterwards for 3 days. The prediction bias and precision were evaluated by mean prediction error (ME), mean squared error (MSB), root mean squared error (RMSE) and their 95% confidence intervals: All the predicted blood levels were compared to the measured levels using student paired Mest during the 24, 48 and 72 h of treatment. Results Three hundreds three aminoglycosides concentrations were available to predict, ME was -0.05 and - 0.28 mg·mL-1 for peak of 24 and 48 hours post-infusion, respectively. Their 95% confidence intervals included 0, showing no bias. All MSE, RMSE for peak of 24 and 48 h were small and within the clinical accepted range. No significant difference were found for peak levels between predicted and measured levels on 24 and 48 h (P>0.05). However, there are significant difference in 72h and all trough levels (P<0.05). Conclusion predicting peak concentration of aminoglycosides using B ayesian fitting of PKS will be acceptable with two feedback concentrations. It seems able to help clinician adjust individually aminoglycosides dosage easily. However, it may be limited for prediction of trough concentration.