Collaborative Research Multivariable Modeling and Control of Clinical Pharmacodynamics

智力优势：在手术过程中，麻醉师不断调整给病人的麻醉剂的输送，以保持足够的麻醉深度。同时，麻醉师维持通气参数，监测心血管和呼吸功能。决定何时对患者进行适当的麻醉是基于麻醉师的专业知识，并结合他或她对患者输出的观察，如血压、心率、呼出气体和基于脑电图的测量。也就是说，麻醉师在手术过程中扮演多变量反馈控制器的角色。麻醉药管理的自动化将允许给病人的麻醉药量的最佳裁剪，并使麻醉师专注于病人的手术需求所必需的关键任务，无论是预期的还是意外的。实施闭环给药的直接好处是降低药品成本，缩短恢复时间，改善患者的长期预后。为了设计反馈控制方案，需要适合控制目的的患者/药物输送系统的数学模型。在本研究项目中，我们专注于(1)发展与控制相关的多输入、多输出模型，以描述患者对麻醉剂、通气控制和外部刺激的反应；(2)制定和实施控制策略，以提高患者的安全性和术后结果。我们的目标是发展系统先进的多变量控制和识别技术，我们认为这是充分解决这个问题所必需的。具体工作将包括开发多变量切换线性系统的控制综合、分析和系统识别算法，以及直接针对多变量间隔系统的经验建模的模型结构规范和子空间系统识别算法。更广泛的影响：在手术室中越来越多地使用计算机，加上最近发展的非侵入性但有效的测量麻醉目标的手段，使控制技术纳入麻醉输送过程迫在眉睫。本项目的研究结果将对主动控制在临床药理学中的应用状况产生重大影响，特别是对手术过程中麻醉深度的临床监测和控制。pi已经与通用电气医疗保健公司和ASPECT医疗系统公司的临床监测和围手术期研发小组建立了联系，通过这种联系可以确保技术转移和与行业实施的相关性。

Intellectual Merit:During surgery, anesthesiologists continuously adjust the delivery of anesthetic agents given to the patient in order to maintain an adequate level of anesthetic depth. Simultaneously, anesthesiologists maintain ventilation parameters and monitor cardiovascular and respiratory functions. The determination of when a patient is properly anesthetized is made based on the anesthesiologist's expertise combined with his or her observations of patient outputs such as blood pressure, heart rate, exhaled gases, and EEG-based measures. That is, anesthesiologists perform the role of multivariable feedback controllers during surgery. Automating the administration of anesthetics will allow both for optimal tailoring of the amount of anesthetics given to patients and for the anesthesiologist to focus on critical tasks necessitated by surgical demands on the patient that are both expected and unexpected. The direct advantages of implementing closed-loop drug delivery would be reduced pharmaceutical costs, reduced recovery time and improved long-term patient outcomes. In order to design feedback control schemes, mathematical models of the patient/drug delivery system that are suitable for control purposes are required.In this research project we focus on (1) the development of control-relevant multi-input, multi-output models to describe patient response to anesthetic agents, ventilation controls and external stimuli, and (2) the development and implementation of control strategies for which patient safety and postoperative outcomes are improved. We target the development of systematic advanced multivariable control and identification techniques, which we posit are required to adequately address this problem. Specific efforts will include the development of control synthesis, analysis and system identification algorithms for multivariable switched-linear systems, and model structure specification and subspace system identification algorithms directly aimed at empirical modeling of multivariable compartmental systems.Broader Impact:The increasing use of computers in the operating room combined with the recent development of non-invasive yet effective means of measuring a number of the goals of anesthesia promise to make the incorporation of control techniques into the anesthetic delivery process imminent. The results of this project will have a dramatic and significant impact on the state of active control applications in clinical pharmacology, in general, and clinical monitoring and control of anesthetic depth during surgery, more specifically. The PIs have established connections with clinical monitoring and perioperative research and development groups at General Electric Healthcare, and ASPECT Medical Systems, Inc., through which technology transfer and relevance to industry implementation is assured.