Collaborative Research Multivariable Modeling and Control of Clinical Pharmacodynamics

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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.