title: Intensive insulin therapy: enhanced Model Predictive Control algorithm versus standard care.
abstract: OBJECTIVE
  To investigate the effectiveness of an enhanced software Model Predictive Control (eMPC) algorithm for intravenous insulin infusion, targeted at tight glucose control in critically ill patients, over 72 h, in two intensive care units with different management protocols.
  
  
  METHODS
  Comparison with standard care in a two center open randomized clinical trial.
  
  
  METHODS
  Two adult intensive care units in University Hospitals.
  
  
  METHODS
  Thirty-four critically ill patients with hyperglycaemia (glucose >120 mg/dL) or already receiving insulin infusion.
  
  
  METHODS
  Patients were randomized, within each ICU, to intravenous insulin infusion advised by eMPC algorithm or the ICU's standard insulin infusion administration regimen.
  
  
  RESULTS
  Arterial glucose concentration was measured at study entry and when advised by eMPC or measured as part of standard care. Time-weighted average glucose concentrations in patients receiving eMPC advised insulin infusions were similar [104 mg/dL (5.8 mmol/L)] in both ICUs. eMPC advised glucose measurement interval was significantly different between ICUs (1.1 vs. 1.8 h, P < 0.01). The standard care insulin algorithms resulted in significantly different time-weighted average glucose concentrations between ICUs [128 vs. 99 mg/dL (7.1 vs. 5.5 mmol/L), P < 0.01].
  
  
  CONCLUSIONS
  In this feasibility study the eMPC algorithm provided similar, effective and safe tight glucose control over 72 h in critically ill patients in two different ICUs. Further development is required to reduce glucose sampling interval while maintaining a low risk of hypoglycaemia.
authors.affiliation: Adult Intensive Care Unit, Royal Brompton Hospital, Sydney Street, London, UK. j.cordingley@rbht.nhs.uk
authors.forename: Jeremy J
authors.initials: JJ
authors.lastname: Cordingley
authors.forename: Dirk
authors.initials: D
authors.lastname: Vlasselaers
authors.forename: Natalie C
authors.initials: NC
authors.lastname: Dormand
authors.forename: Pieter J
authors.initials: PJ
authors.lastname: Wouters
authors.forename: Stephen D
authors.initials: SD
authors.lastname: Squire
authors.forename: Ludovic J
authors.initials: LJ
authors.lastname: Chassin
authors.forename: Malgorzata E
authors.initials: ME
authors.lastname: Wilinska
authors.forename: Clifford J
authors.initials: CJ
authors.lastname: Morgan
authors.forename: Roman
authors.initials: R
authors.lastname: Hovorka
authors.forename: Greet
authors.initials: G
authors.lastname: Van den Berghe
mesh_terms: D000328:Adult; D000368:Aged; D000465:Algorithms; D001786:Blood Glucose / Q000032:analysis; D002241:Carbohydrates / Q000008:administration & dosage; D004360:Drug Therapy, Computer-Assisted; D005260:Female; D006801:Humans; D006943:Hyperglycemia / Q000097:blood / Q000188:drug therapy*; D007003:Hypoglycemia / Q000517:prevention & control*; D007004:Hypoglycemic Agents / Q000008:administration & dosage*; D007262:Infusions, Intravenous; D007328:Insulin / Q000008:administration & dosage*; D007362:Intensive Care Units; D008297:Male; D008875:Middle Aged; D019095:Point-of-Care Systems
chemical_list: D001786:Blood Glucose; D002241:Carbohydrates; D007004:Hypoglycemic Agents; D007328:Insulin