Bayesian forecasting improves the prediction of intraoperative plasma concentrations of XXXX .

@entity1 @entity680
To achieve therapeutic plasma concentrations of the opioid @entity680 , one must administer the drug as a variable rate continuous infusion. For most @entity1 , using population pharmacokinetic parameters of @entity680 for dosing regimen allows accurate prediction of the plasma concentration of the drug over time. However, for some @entity1 , using such parameters results in systematic over- or underprediction of the concentration. Retrospectively studying a data set (dosage history and measured concentrations) for 34 @entity1 , the authors examined how Bayesian forecasting could improve the precision of prediction. For each @entity1 , a Bayesian regression was performed to estimate "individualized" pharmacokinetic parameters, using population pharmacokinetic values for @entity680 and the measurement of @entity680 in one or more plasma samples from each @entity1 . These individualized parameters were then used to predict the subsequent plasma concentrations of @entity680 over time. By comparing the value of each measured point with its corresponding predicted value, the authors calculated the prediction error as a percentage of the measured value. The precision of the prediction was assessed by the percent mean absolute prediction error. After Bayesian forecasting using a single point sampled at 80 min after start of anesthesia, the average precision of the prediction was 13.8 +/- 6.1% (SD). Using no Bayesian forecasting and only population values of the pharmacokinetic parameters for the prediction of the concentration, the precision was 24.3 +/- 16.9%. The improvement in precision brought by Bayesian forecasting was especially noticeable for those @entity1 whose prediction of @entity680 was poor using population pharmacokinetic values (i.e., "outlier" @entity1 ).(ABSTRACT TRUNCATED AT 250 WORDS)
