title: Tight glycaemic control by an automated algorithm with time-variant sampling in medical ICU patients.
abstract: OBJECTIVE
  Tight glycaemic control (TGC) in critically ill patients improves clinical outcome, but is difficult to establish The primary objective of the present study was to compare glucose control in medical ICU patients applying a computer-based enhanced model predictive control algorithm (eMPC) extended to include time-variant sampling against an implemented glucose management protocol.
  
  
  METHODS
  Open randomised controlled trial.
  
  
  METHODS
  Nine-bed medical intensive care unit (ICU) in a tertiary teaching hospital.
  
  
  METHODS
  Fifty mechanically ventilated medical ICU patients.
  
  
  METHODS
  Patients were included for a study period of up to 72 h. Patients were randomised to the control group (n = 25), treated by an implemented insulin algorithm, or to the eMPC group (n = 25), using the laptop-based algorithm. Target range for blood glucose (BG) was 4.4-6.1 mM. Efficacy was assessed by mean BG, hyperglycaemic index (HGI) and BG sampling interval. Safety was assessed by the number of hypoglycaemic-episodes < 2.2 mM. Each participating nurse filled-in a questionnaire regarding the usability of the algorithm.
  
  
  RESULTS
  BG and HGI were significantly lower in the eMPC group [BG 5.9 mM (5.5-6.3), median (IQR); HGI 0.4 mM (0.2-0.9)] than in control patients [BG 7.4 mM (6.9-8.6), p < 0.001; HGI 1.6 mM (1.1-2.4), p < 0.001]. One hypoglycaemic episode was detected in the eMPC group; no such episodes in the control group. Sampling interval was significantly shorter in the eMPC group [eMPC 117[Symbol: see text]min (+/- 34), mean (+/- SD), vs 174 min (+/- 27); p < 0.001]. Thirty-four nurses filled-in the questionnaire. Thirty answered the question of whether the algorithm could be applied in daily routine in the affirmative.
  
  
  CONCLUSIONS
  The eMPC algorithm was effective in maintaining tight glycaemic control in severely ill medical ICU patients.
authors.affiliation: Department of Internal Medicine, Medical University Graz, Stiftingtalstrasse 24, 8010, Graz, Austria. christoph.pachler@healthsite.at
authors.forename: Christoph
authors.initials: C
authors.lastname: Pachler
authors.forename: Johannes
authors.initials: J
authors.lastname: Plank
authors.forename: Heinz
authors.initials: H
authors.lastname: Weinhandl
authors.forename: Ludovic J
authors.initials: LJ
authors.lastname: Chassin
authors.forename: Malgorzata E
authors.initials: ME
authors.lastname: Wilinska
authors.forename: Roman
authors.initials: R
authors.lastname: Kulnik
authors.forename: Peter
authors.initials: P
authors.lastname: Kaufmann
authors.forename: Karl-Heinz
authors.initials: KH
authors.lastname: Smolle
authors.forename: Ernst
authors.initials: E
authors.lastname: Pilger
authors.forename: Thomas R
authors.initials: TR
authors.lastname: Pieber
authors.forename: Martin
authors.initials: M
authors.lastname: Ellmerer
authors.forename: Roman
authors.initials: R
authors.lastname: Hovorka
mesh_terms: D018806:APACHE; D000465:Algorithms; D001786:Blood Glucose / Q000187:drug effects*; D003422:Critical Care / Q000379:methods*; D003922:Diabetes Mellitus, Type 1 / Q000097:blood / Q000145:classification / Q000188:drug therapy*; D004360:Drug Therapy, Computer-Assisted / Q000379:methods*; D005260:Female; D038321:Glycemic Index; D006801:Humans; D007004:Hypoglycemic Agents / Q000008:administration & dosage / Q000627:therapeutic use*; D007328:Insulin / Q000008:administration & dosage / Q000627:therapeutic use*; D007333:Insulin Resistance; D007362:Intensive Care Units; D008297:Male; D008875:Middle Aged; D013997:Time Factors
chemical_list: D001786:Blood Glucose; D007004:Hypoglycemic Agents; D007328:Insulin