Modeling and control of the agitation-sedation cycle for critical care patients.

Agitation-sedation cycling in critically ill patients, characterized by oscillations between states of agitation and over-sedation, is damaging to patient health, and increases length of stay and healthcare costs. The mathematical model presented captures the essential dynamics of the agitation-sedation system for the first time, and is statistically validated using recorded infusion data for 37 patients. Constant patient-specific patient parameters are used, illustrating the commonality of these fundamental dynamics over a broad range of patients. The validated model serves as a basis for comparison of sedation administration methods, devices, therapeutics and protocols. Heavy derivative feedback control is shown to be an effective means of managing agitation, given consistent agitation measurement. The improved agitation management reduces the modeled mean and peak agitation levels 68.4% and 52.9% on average, respectively. Some patients showed over 90% reduction in mean agitation level through increased control gains. This improved agitation management is achieved via heavy derivative feedback control of sedation administration, which provides an essentially bolus-driven management approach, aligned with recent sedation practices.

[1]  B. Silverman Density estimation for statistics and data analysis , 1986 .

[2]  Koch Sigmund Ed,et al.  Psychology: A Study of A Science , 1962 .

[3]  J. Vender,et al.  Anxiety, delirium, and pain in the intensive care unit. , 2001, Critical care clinics.

[4]  T. Hastie,et al.  Local Regression: Automatic Kernel Carpentry , 1993 .

[5]  Geoffrey M Shaw,et al.  Derivative weighted active insulin control modelling and clinical trials for ICU patients. , 2004, Medical engineering & physics.

[6]  T. Hettmansperger,et al.  Robust Nonparametric Statistical Methods , 1998 .

[7]  S. T. Buckland,et al.  An Introduction to the Bootstrap. , 1994 .

[8]  H E Hulshoff Pol,et al.  Effects of context on judgements of odor intensities in humans. , 1998, Chemical senses.

[9]  G. Fraser,et al.  Prospective evaluation of the Sedation-Agitation Scale for adult critically ill patients. , 1999, Critical care medicine.

[10]  M. A. Hughes,et al.  Context-sensitive half-time in multicompartment pharmacokinetic models for intravenous anesthetic drugs. , 1992, Anesthesiology.

[11]  Z-H Lam,et al.  Active insulin infusion using optimal and derivative-weighted control. , 2002, Medical engineering & physics.

[12]  G. Brattebø,et al.  Effect of a scoring system and protocol for sedation on duration of patients' need for ventilator support in a surgical intensive care unit , 2002, BMJ : British Medical Journal.

[13]  J. Kress,et al.  Daily interruption of sedative infusions in critically ill patients undergoing mechanical ventilation. , 2000, The New England journal of medicine.

[14]  D. O'Hara,et al.  Pharmacokinetics and Pharmacodynamics of Sedatives and Analgesics in the Treatment of Agitated Critically Ill Patients , 1997, Clinical pharmacokinetics.

[15]  H. Wallach,et al.  The perception of neutral colors. , 1963, Scientific American.

[16]  A. Donner,et al.  Optimal intravenous dosing strategies for sedatives and analgesics in the intensive care unit. , 1995, Critical care clinics.

[17]  Geoffrey M. Shaw,et al.  Derivative weighted active insulin control algorithms and trials , 2003 .

[18]  D. Ariely,et al.  The effect of past-injury on pain threshold and tolerance , 1995, Pain.

[19]  C. T. French,et al.  Effects of a multifaceted, multidisciplinary, hospital-wide quality improvement program on weaning from mechanical ventilation , 2002, Critical care medicine.

[20]  Y. Dodge on Statistical data analysis based on the L1-norm and related methods , 1987 .

[21]  Edward Abraham,et al.  Management of the agitated intensive care unit patient. , 2002, Critical care medicine.

[22]  A. Wood,et al.  Drugs and Anesthesia: Pharmacology for Anesthesiologists , 1989 .