Automatic Control of Propanidid and Propofol Anesthesia-Induced Unconsciousness

项目总结/摘要 当前麻醉学的临床实践涉及使用标准剂量施用麻醉剂 指南，监测给药期间的生理反应，并调整剂量 取决于病人的反应。重要的是，直接监测大脑不是标准的一部分， 麻醉学实践，尽管所需的效果发生在大脑中。如果大脑 是直接监测的，专有的，不依赖药物的，非个性化的无意识深度 指数通常用于从脑电图（EEG）信号推断意识状态。这 可能导致用药过量，因为神经反应存在显著的个体间差异 相同剂量的麻醉剂，麻醉的特征随年龄或选择而显著变化 麻醉剂。因此，老年患者经常被给予比必要更多的麻醉剂， 发生术后认知功能障碍（POCD）和谵妄的风险很高，可能会持续 几个月。随着人口的增长，老年患者手术的流行率将上升， 年龄，需要新的方法来确保安全，个性化的麻醉剂输送， 确保无意识，但也安全，并在手术后恢复正常的认知功能。 该项目旨在开发人体全身麻醉的闭环控制， 个性化麻醉护理。该项目的目标1旨在利用机器学习开发一种药物- 无意识深度的特定标记，反映了无意识的不同特征 随着年龄的增长，可以从临床上理解为脑功能。该项目的目标2是开发 模型和非线性模型预测控制（MPC）算法，用于调节 在全身麻醉期间失去意识。MPC是一种控制方案，其可以容易地修改以 结合了临床安全特征，并已广泛用于医疗控制系统。到 严格控制无意识的深度，麻醉剂的快速致动是至关重要的。目标3 该项目旨在描述propanidid的效果，propanidid是一种目前用于临床的速效麻醉剂， 在墨西哥练习。目标2的高潮将导致设计第一个封闭的临床试验- 袢麻醉控制和目标3的最终结果将导致设计临床药代动力学 研究propanidid。该项目的结果有可能改变临床实践， 麻醉并允许个性化麻醉护理。通过直接脑电图监测和控制大脑 国家，而不是次要标志物，个性化治疗开发的这个项目应该有助于 减少老年外科患者用药过量，降低POCD和谵妄的发生率。

Project Summary/Abstract Current clinical practice of anesthesiology involves administering anesthetics using standard dosing guidelines, monitoring physiological responses during administration, and adjusting the dose depending on the patient responses. Importantly, direct monitoring of the brain is not part of standard anesthesiology practice, despite the desired effect taking place in the brain. In cases where the brain is directly monitored, proprietary, drug-independent, non-personalized depth of unconsciousness indices are commonly used to infer conscious state from electroencephalography (EEG) signals. This may result in overdosing, as there are significant inter-individual differences in neurological response to identical doses of anesthetics and signatures of anesthesia vary significantly with age or with choice of anesthetic. Consequently, older patients are often given more anesthetic than necessary and are at high risk for developing post-operative cognitive dysfunction (POCD) and delirium, which may last up to several months. The prevalence of surgical procedures on older patients will rise as the population ages, necessitating new approaches for ensuring safe, personalized delivery of anesthetics that ensure unconsciousness, but also safety and return to normal cognitive function following surgery. This project seeks to develop closed-loop control of general anesthesia in humans as a means of personalizing anesthesia care. Aim 1 of this project seeks to use machine learning to develop a drug- specific marker of depth of unconsciousness that reflects the varying signatures of unconsciousness with age and may be understood clinically in terms of brain function. Aim 2 of this project is to develop models and nonlinear model predictive control (MPC) algorithms for regulating depth of unconsciousness during general anesthesia. MPC is a control scheme that may be easily modified to incorporate clinical safety features and has been used extensively in medical control systems. To tightly regulate depth of unconsciousness, fast actuation by the anesthetic is critical. Aim 3 of this project seeks to characterize the effect of propanidid, a fast-acting anesthetic currently used in clinical practice in Mexico. The culmination of Aim 2 will result in designing clinical trials for the first closed- loop anesthetic control and the culmination of Aim 3 will result in designing a clinical pharmacokinetic study of propanidid. The results of this project have the potential to transform clinical practice of anesthesia and allow individualized anesthesia care. By direct EEG monitoring and control of brain state rather than secondary markers, the personalized treatment developed by this project should help reduce overdosing of senior surgical patients and reduce incidence of POCD and delirium.