Multivariable modeling and control approaches for anesthetic pharmacodynamics

November 1, 2017
In this presentation I will provide an overview of some robust and adaptive approaches to the problem of controlling patient response to sedative anesthetic agents in clinical settings. In current practice, anesthesiologists are responsible for monitoring and adjusting the delivery of anesthetic agents to the patient, with the main goal being to maintain a desired level of sedation, analgesia and muscle relaxation. At the same time, the attending anesthesiologist must ensure proper cardiovascular and respiratory functioning of the patient, for example maintaining appropriate heart rate (HR), blood pressure (BP), oxygen saturation and end-tidal (exhaled) carbon dioxide levels, amongst other patient indicators. That is, the anesthesiologist performs the role of a multivariable feedback controller for a highly complex process. Our goal is to incorporate partially automated anesthesia delivery into the process, allowing the anesthesiologist to concentrate on urgent safety-critical events that arise during surgery. Two necessary ingredients towards automating anesthesia delivery are (1) adequate means of sensing the patient's level of sedation, analgesia and muscle relaxation, and (2) mathematical models capturing the patient response to anesthetic agents. In this presentation, we focus on efforts aimed at modeling and controlling the level of sedation via automated feedback methods, while at the same time maintaining the patient's blood pressure in a given safe range. We discuss the development of a control design approach in which we use system identification to construct multivariable patient models and apply advanced control methods, so that patients’ sensed sedation levels track a desired reference trajectory.