Literature DB >> 19449619

Closed-loop control for intensive care unit sedation.

Wassim M Haddad1, James M Bailey.   

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.

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Year:  2009        PMID: 19449619     DOI: 10.1016/j.bpa.2008.07.007

Source DB:  PubMed          Journal:  Best Pract Res Clin Anaesthesiol        ISSN: 1521-6896


  5 in total

1.  Dynamic behavior of BIS, M-entropy and neuroSENSE brain function monitors.

Authors:  Stéphane Bibian; Guy A Dumont; Tatjana Zikov
Journal:  J Clin Monit Comput       Date:  2010-12-05       Impact factor: 2.502

2.  Relevance vector machine learning for neonate pain intensity assessment using digital imaging.

Authors:  Behnood Gholami; Wassim M Haddad; Allen R Tannenbaum
Journal:  IEEE Trans Biomed Eng       Date:  2010-02-17       Impact factor: 4.538

3.  Multimodal noninvasive monitoring of soft tissue wound healing.

Authors:  Michael Bodo; Timothy Settle; Joseph Royal; Eric Lombardini; Evelyn Sawyer; Stephen W Rothwell
Journal:  J Clin Monit Comput       Date:  2013-07-06       Impact factor: 2.502

4.  Clinical Decision Support and Closed-Loop Control for Cardiopulmonary Management and Intensive Care Unit Sedation Using Expert Systems.

Authors:  Behnood Gholami; James M Bailey; Wassim M Haddad; Allen R Tannenbaum
Journal:  IEEE Trans Control Syst Technol       Date:  2012-03       Impact factor: 5.485

5.  Agitation and pain assessment using digital imaging.

Authors:  Behnood Gholami; Wassim M Haddad; Allen R Tannenbaum
Journal:  Conf Proc IEEE Eng Med Biol Soc       Date:  2009
  5 in total

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