title: Targeted glycemic reduction in critical care using closed-loop control.
abstract: BACKGROUND
  Critically ill patients are often hyperglycemic and extremely diverse in their dynamics. Consequently, fixed protocols and sliding scales can result in error and poor control. Tight glucose control has been shown to significantly reduce mortality in critical care. An improved physiological system model of the glucose-insulin dynamics of a critical care patient is used to develop an adaptive tight glucose control protocol that accounts for variable patient dynamics, and is verified in limited clinical trials.
  
  
  METHODS
  A physiologically based two-compartment system model that accounts for time-varying insulin sensitivity, time-varying endogenous glucose removal, and two saturation kinetics mechanisms is developed. A bolus-based adaptive control protocol is developed that monitors the physiological status of a critically ill patient, enabling tight glycemic regulation to preset glycemic targets. The model and protocol are verified in three, 5-h preliminary proof-of-concept clinical trials. Ethics approval was granted by the Canterbury Ethics Committee (Christchurch, New Zealand).
  
  
  RESULTS
  Preset glycemic targets are achieved with an average absolute error of 9%, with 75% of all targets achieved within the 7% measurement error. Absolute errors greater than 7% ranged from 17% to 21%.
  
  
  CONCLUSIONS
  Tight stepwise control was exhibited in all cases, and the adaptive system was able to match the model and observed patient dynamics. Most errors are associated with external perturbations such as drug therapies, or mismodeled parameters that can be easily adjusted with longer trials and/or more data per hour. The overall result is targeted stepwise tight glycemic regulation using insulin boluses.
authors.affiliation: Department of Mechanical Engineering, Centre for Bio-Engineering, University of Canterbury, Christchurch, New Zealand. geoff.chase@canterbury.ac.nz
authors.forename: J Geoffrey
authors.initials: JG
authors.lastname: Chase
authors.forename: Geoffrey M
authors.initials: GM
authors.lastname: Shaw
authors.forename: Jessica
authors.initials: J
authors.lastname: Lin
authors.forename: Carmen V
authors.initials: CV
authors.lastname: Doran
authors.forename: Chris
authors.initials: C
authors.lastname: Hann
authors.forename: Thomas
authors.initials: T
authors.lastname: Lotz
authors.forename: Graeme C
authors.initials: GC
authors.lastname: Wake
authors.forename: Bob
authors.initials: B
authors.lastname: Broughton
mesh_terms: D000368:Aged; D000465:Algorithms; D017544:Aortic Aneurysm, Abdominal / Q000150:complications / Q000601:surgery; D001786:Blood Glucose / Q000378:metabolism*; D001026:Coronary Artery Bypass; D003422:Critical Care / Q000379:methods*; D003924:Diabetes Mellitus, Type 2 / Q000188:drug therapy; D005260:Female; D006207:Half-Life; D006801:Humans; D006943:Hyperglycemia / Q000628:therapy; D007004:Hypoglycemic Agents / Q000097:blood / Q000494:pharmacology; D007328:Insulin / Q000097:blood / Q000494:pharmacology; D007333:Insulin Resistance; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D012128:Respiratory Distress Syndrome / Q000150:complications / Q000628:therapy; D018805:Sepsis / Q000150:complications / Q000628:therapy
chemical_list: D001786:Blood Glucose; D007004:Hypoglycemic Agents; D007328:Insulin