Colin Pieter, Eleveld Douglas J, van den Berg Johannes P, Vereecke Hugo E M, Struys Michel M R F, Schelling Gustav, Apfel Christian C, Hornuss Cyrill
Department of Anesthesiology, University Medical Center Groningen, University of Groningen, Hanzeplein 1, Postbus 30 001, Groningen, 9700 RB, The Netherlands.
Laboratory of Medical Biochemistry and Clinical Analysis, Faculty of Pharmaceutical Sciences, Ghent University, Ghent, Belgium.
Clin Pharmacokinet. 2016 Jul;55(7):849-859. doi: 10.1007/s40262-015-0358-z.
Monitoring of drug concentrations in breathing gas is routinely being used to individualize drug dosing for the inhalation anesthetics. For intravenous anesthetics however, no decisive evidence in favor of breath concentration monitoring has been presented up until now. At the same time, questions remain with respect to the performance of currently used plasma pharmacokinetic models implemented in target-controlled infusion systems. In this study, we investigate whether breath monitoring of propofol could improve the predictive performance of currently applied, target-controlled infusion models.
Based on data from a healthy volunteer study, we developed an addition to the current state-of-the-art pharmacokinetic model for propofol, to accommodate breath concentration measurements. The potential of using this pharmacokinetic (PK) model in a Bayesian forecasting setting was studied using a simulation study. Finally, by introducing bispectral index monitor (BIS) measurements and the accompanying BIS models into our PK model, we investigated the relationship between BIS and predicted breath concentrations.
We show that the current state-of-the-art pharmacokinetic model is easily extended to reliably describe propofol kinetics in exhaled breath. Furthermore, we show that the predictive performance of the a priori model is improved by Bayesian adaptation based on the measured breath concentrations, thereby allowing further treatment individualization and a more stringent control on the targeted plasma concentrations during general anesthesia. Finally, we demonstrated concordance between currently advocated BIS models, relying on predicted effect-site concentrations, and our new approach in which BIS measurements are derived from predicted breath concentrations.
监测呼吸气体中的药物浓度通常用于吸入麻醉药的个体化给药。然而，对于静脉麻醉药，迄今为止尚未有支持呼气浓度监测的确凿证据。同时，关于目标控制输注系统中目前使用的血浆药代动力学模型的性能仍存在疑问。在本研究中，我们调查丙泊酚的呼气监测是否能提高当前应用的目标控制输注模型的预测性能。
基于一项健康志愿者研究的数据，我们在当前最先进的丙泊酚药代动力学模型基础上进行扩展，以纳入呼气浓度测量值。通过模拟研究探讨了在贝叶斯预测环境中使用该药代动力学（PK）模型的潜力。最后，通过将脑电双频指数监测（BIS）测量值及相关的BIS模型引入我们的PK模型，我们研究了BIS与预测呼气浓度之间的关系。
我们表明，当前最先进的药代动力学模型可轻松扩展，以可靠地描述呼出气中丙泊酚的动力学。此外，我们表明基于测量的呼气浓度通过贝叶斯调整可提高先验模型的预测性能，从而在全身麻醉期间实现进一步的个体化治疗，并对目标血浆浓度进行更严格的控制。最后，我们证明了目前基于预测效应室浓度的BIS模型与我们从预测呼气浓度得出BIS测量值的新方法之间的一致性。
