Chakravarty Sourish, Nikolaeva Ksenia, Kishnan Devika, Flores Francisco J, Purdon Patrick L, Brown Emery N
The Picower Inst. for Learning and Memory, MIT, Cambridge, MA.
Inst. of Med. Engin. and Sci. , MIT, Cambridge, MA.
Health Innov Point Care Conf. 2017 Nov;2017:44-47. doi: 10.1109/hic.2017.8227580. Epub 2017 Dec 21.
Target controlled infusion (TCI) of intraveneous anesthetics can assist clinical practitioners to provide improved care for General Anesthesia (GA). Pharmacokinetic/Pharmacodynamic (PK/PD) models help in relating the anesthetic drug infusion to observed brain activity inferred from electroencephalogram (EEG) signals. The parameters in popular population PK/PD models for propofol-induced GA (Marsh and Schnider models) are either verified based on proprietary functions of the EEG signal which are difficult to correlate with the neurophysiological models of anesthesia, or the marker itself needs to be estimated simultaneously with the PD model. Both these factors make these existing paradigms challenging to apply in real-time context where a patient-specific tuning of parameters is desired. In this work, we propose a simpler EEG marker from frequency domain description of EEG and develop two corresponding PK/PD modeling approaches which differ in whether they use existing population-level PK models (approach 1) or not (approach 2). We use a simple deterministic parameter estimation approach to identify the unknown PK/PD model parameters from an existing human EEG data-set. We infer that both approaches 1 and 2 yield similar and reasonably good fits to the marker data. This work can be useful in developing patient-specific TCI strategies to induce GA.
静脉麻醉药的靶控输注(TCI)可协助临床医生为全身麻醉(GA)提供更好的护理。药代动力学/药效学(PK/PD)模型有助于将麻醉药物输注与从脑电图(EEG)信号推断出的观察到的脑活动联系起来。用于丙泊酚诱导的全身麻醉的流行群体PK/PD模型(Marsh模型和Schnider模型)中的参数,要么基于难以与麻醉神经生理模型相关联的EEG信号专有函数进行验证,要么标记本身需要与PD模型同时估计。这两个因素使得这些现有范式在需要针对患者进行参数调整的实时环境中难以应用。在这项工作中,我们从EEG的频域描述中提出了一种更简单的EEG标记,并开发了两种相应的PK/PD建模方法,这两种方法的区别在于是否使用现有的群体水平PK模型(方法1)或不使用(方法2)。我们使用一种简单的确定性参数估计方法从现有的人类EEG数据集中识别未知的PK/PD模型参数。我们推断方法1和方法2对标记数据都产生了相似且相当好的拟合。这项工作对于开发诱导全身麻醉的患者特异性TCI策略可能有用。