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贝叶斯反馈预测利多卡因血清浓度的性能:预测误差和预测区间的评估。

Performance of Bayesian feedback to forecast lidocaine serum concentration: evaluation of the prediction error and the prediction interval.

作者信息

Vozeh S, Uematsu T, Hauf G F, Follath F

出版信息

J Pharmacokinet Biopharm. 1985 Apr;13(2):203-12. doi: 10.1007/BF01059399.

DOI:10.1007/BF01059399
PMID:4057058
Abstract

The prediction performance of the Bayesian feedback method was evaluated with respect to accuracy and precision, and efficacy and safety (width of the prediction interval) on the basis of 90 predictions in 30 patients treated with lidocaine. The mean of the prediction error (PE) and the root mean squared error (RMSE) served as a measure of accuracy and precision. The variance of the standardized prediction error (SPE) was used to evaluate the estimate of the standard deviation of the prediction error. SPE was defined as PE divided by the standard deviation of the predicted concentration. The standard error of RMSE and of the variance of SPE was determined by bootstrap. The results indicate that the lidocaine serum concentration at 12 hr (C2) after starting continuous infusion can be predicted with high accuracy and precision with a single feedback measurement obtained 2-4 hr (C1) after commencement of treatment: RMSE = 20.6%. Prediction at 24 hr (C3) was less accurate: RMSE = 31.4%. Using both C1 and C2 to predict C3 improved precision (RMSE = 23.4%). The evaluation of the prediction interval revealed that the current algorithm produces an upward biased estimate, probably due to a positive bias in the estimate of the covariance matrix of the parameter estimates. It is suggested that evaluation of prediction performance should include the estimate of the prediction interval.

摘要

基于对30例接受利多卡因治疗患者的90次预测,从准确性、精密度以及有效性和安全性(预测区间宽度)方面评估了贝叶斯反馈方法的预测性能。预测误差(PE)的均值和均方根误差(RMSE)用作准确性和精密度的度量。标准化预测误差(SPE)的方差用于评估预测误差标准差的估计值。SPE定义为PE除以预测浓度的标准差。RMSE和SPE方差的标准误差通过自举法确定。结果表明,在开始持续输注后12小时(C2)的利多卡因血清浓度,可通过治疗开始后2 - 4小时(C1)获得的单次反馈测量进行高精度预测:RMSE = 20.6%。24小时(C3)时的预测准确性较低:RMSE = 31.4%。使用C1和C2两者来预测C3提高了精密度(RMSE = 23.4%)。对预测区间的评估显示,当前算法产生了向上偏倚的估计,这可能是由于参数估计协方差矩阵估计中的正偏倚所致。建议预测性能评估应包括预测区间的估计。

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