Department of Anesthesiology and Pain Medicine, Busan Paik Hospital, Inje University, Busan, Korea.
Department of Anesthesiology and Pain Medicine, Hallym University Sacred Heart Hospital, Hallym University of College of Medicine, Anyang, Korea.
Anaesthesia. 2019 Aug;74(8):1033-1040. doi: 10.1111/anae.14704. Epub 2019 May 20.
Phase lag entropy, an electro-encephalography-based hypnotic depth indicator, calculates diversity in temporal patterns of phase relationship. We compared the performance of phase lag entropy with the bispectral index™ in 30 patients scheduled for elective surgery. We initiated a target-controlled infusion of propofol using the Schnider model, and assessed sedation levels using the Modified Observer's Assessment of Alertness/Sedation scale every 30 s with each stepwise increase in the effect-site propofol concentration. Phase lag entropy and bispectral index values were recorded. The correlation coefficient and prediction probability between phase lag entropy or bispectral index and the sedation level or effect-site propofol concentration were analysed. We calculated baseline variabilities of phase lag entropy and bispectral index. In addition, we applied a non-linear mixed-effects model to obtain the pharmacodynamic relationships among the effect-site propofol concentration, phase lag entropy or bispectral index and sedation level. As sedation increased, phase lag entropy and bispectral index both decreased. The prediction probability values of phase lag entropy and bispectral index for sedation levels were 0.697 and 0.700 (p = 0.261) and for the effect-site concentration of propofol were 0.646 and 0.630 (p = 0.091), respectively. Baseline variability in phase lag entropy and bispectral index was 3.3 and 5.7, respectively. The predicted propofol concentrations, using the Schnider pharmacokinetic model, producing a 50% probability of moderate and deep sedation were 1.96 and 3.01 μg.ml , respectively. Phase lag entropy was found to be useful as a hypnotic depth indicator in patients receiving propofol sedation.
相位滞后熵是一种基于脑电图的催眠深度指标,用于计算相位关系时间模式的多样性。我们比较了相位滞后熵与双频谱指数在 30 例择期手术患者中的性能。我们使用 Schnider 模型启动丙泊酚的靶控输注,并使用改良的观察者评估警觉/镇静评分每隔 30 秒评估镇静水平,每次增加效应部位丙泊酚浓度一个步长。记录相位滞后熵和双频谱指数值。分析相位滞后熵或双频谱指数与镇静水平或效应部位丙泊酚浓度之间的相关系数和预测概率。我们计算了相位滞后熵和双频谱指数的基线变异性。此外,我们应用非线性混合效应模型来获得效应部位丙泊酚浓度、相位滞后熵或双频谱指数与镇静水平之间的药效学关系。随着镇静程度的增加,相位滞后熵和双频谱指数均降低。相位滞后熵和双频谱指数预测镇静水平的概率值分别为 0.697 和 0.700(p=0.261),预测效应部位丙泊酚浓度的概率值分别为 0.646 和 0.630(p=0.091)。相位滞后熵和双频谱指数的基线变异性分别为 3.3 和 5.7。使用 Schnider 药代动力学模型预测产生 50%中度和深度镇静的丙泊酚浓度分别为 1.96 和 3.01μg.ml-1。在接受丙泊酚镇静的患者中,相位滞后熵被发现是一种有用的催眠深度指标。