title: A novel, model-based insulin and nutrition delivery controller for glycemic regulation in critically ill patients.
abstract: BACKGROUND
  Critically ill patients are often hyperglycemic and insulin resistant, as well as highly dynamic. Tight glucose control has been shown to significantly reduce mortality in critical care. A physiological model of the glucose-insulin regulatory system is improved and used to develop an adaptive control protocol utilizing both nutritional and insulin inputs to control hyperglycemia. The approach is clinically verified in a critical care patient cohort.
  
  
  METHODS
  A simple two-compartment model for glucose rate of appearance in plasma due to stepwise enteral glucose fluxes is developed and incorporated into a previously validated system model. A control protocol modulating intravenous insulin infusion and bolus, with an enteral feed rate, is developed, enabling tight and predictive glycemic regulation to preset targets. The control protocol is adaptive to patient time-variant effective insulin resistance. The model and protocol are verified in seven 10-h and one 24-h proof-of-concept clinical trials. Ethics approval was granted by the Canterbury Ethics Committee.
  
  
  RESULTS
  Insulin requirements varied widely following acute changes in patient physiology. The algorithm developed successfully adapted to patient metabolic status and insulin sensitivity, achieving an average target acquisition error of 9.3% with 90.7% of all targets achieved within +/-20%. Prediction errors may not be distinguishable from sensor measurement errors. Large errors (>20%) are attributable to highly dynamic and unpredictable changes in patient condition.
  
  
  CONCLUSIONS
  Tight, targeted stepwise regulation was exhibited in all trials. Overall, tight glycemic regulation is achieved in a broad critical care cohort with optimized insulin and nutrition delivery, effectively managing glycemia even with high effective insulin resistance.
authors.affiliation: Department of Mechanical Engineering, University of Canterbury, Dunedin, New Zealand.
authors.forename: X W
authors.initials: XW
authors.lastname: Wong
authors.forename: I
authors.initials: I
authors.lastname: Singh-Levett
authors.forename: L J
authors.initials: LJ
authors.lastname: Hollingsworth
authors.forename: G M
authors.initials: GM
authors.lastname: Shaw
authors.forename: C E
authors.initials: CE
authors.lastname: Hann
authors.forename: T
authors.initials: T
authors.lastname: Lotz
authors.forename: J
authors.initials: J
authors.lastname: Lin
authors.forename: O S W
authors.initials: OS
authors.lastname: Wong
authors.forename: J G
authors.initials: JG
authors.lastname: Chase
mesh_terms: D000368:Aged; D000465:Algorithms; D001786:Blood Glucose; D002985:Clinical Protocols; D003422:Critical Care / Q000379:methods*; D016638:Critical Illness / Q000628:therapy; D004750:Enteral Nutrition / Q000009:adverse effects / Q000592:standards*; D005260:Female; D006801:Humans; D006943:Hyperglycemia / Q000188:drug therapy; D007004:Hypoglycemic Agents / Q000008:administration & dosage*; D007328:Insulin / Q000008:administration & dosage*; D007333:Insulin Resistance; D008297:Male; D008875:Middle Aged; D008954:Models, Biological
chemical_list: D001786:Blood Glucose; D007004:Hypoglycemic Agents; D007328:Insulin