title: Closed-loop control for intensive care unit sedation.
abstract: The potential clinical applications of active control for pharmacology in general, and anesthesia and critical care unit medicine in particular, are clearly apparent. Specifically, monitoring and controlling the depth of anesthesia in surgery and the intensive care unit is of particular importance. Nonnegative and compartmental models provide a broad framework for biological and physiological systems, including clinical pharmacology, and are well suited for developing models for closed-loop control for drug administration. These models are derived from mass and energy balance considerations that involve dynamic states whose values are nonnegative and are characterized by conservation laws (e.g., mass, energy, fluid, etc.) capturing the exchange of material between kinetically homogenous entities called compartments. Compartmental models have been particularly important for understanding pharmacokinetics and pharmacodynamics. One of the basic motivations for pharmacokinetic/pharmacodynamic research is to improve drug delivery. In critical care medicine it is current clinical practice to administer potent drugs that profoundly influence levels of consciousness, respiratory, and cardiovascular function by manual control based on the clinician's experience and intuition. Open-loop control (manual control) by clinical personnel can be tedious, imprecise, time-consuming, and sometimes of poor quality, depending on the skills and judgement of the clinician. Closed-loop control based on appropriate dynamical systems models merits investigation as a means of improving drug delivery in the intensive care unit. In this article, we discuss the challenges and opportunities of feedback control using nonnegative and compartmental system theory for the specific problem of closed-loop control of intensive care unit sedation. Several closed-loop control paradigms are investigated including adaptive control, neural network adaptive control, optimal control, and hybrid adaptive control algorithms for intensive care unit sedation.
authors.affiliation: School of Aerospace Engineering, Georgia Institute of Technology, Atlanta, GA 30332-0150, USA. wm.haddad@aerospace.gatech.edu
authors.forename: Wassim M
authors.initials: WM
authors.lastname: Haddad
authors.forename: James M
authors.initials: JM
authors.lastname: Bailey
mesh_terms: D000465:Algorithms; D000758:Anesthesia / Q000379:methods; D003422:Critical Care / Q000379:methods*; D004360:Drug Therapy, Computer-Assisted / Q000379:methods*; D005246:Feedback; D006801:Humans; D006993:Hypnotics and Sedatives / Q000008:administration & dosage* / Q000493:pharmacokinetics; D008954:Models, Biological; D016343:Monitoring, Intraoperative / Q000379:methods; D016571:Neural Networks, Computer
chemical_list: D006993:Hypnotics and Sedatives