title: Evaluating glycemic control algorithms by computer simulations.
abstract: BACKGROUND
  Numerous guidelines and algorithms exist to achieve glycemic control. Their strengths and weaknesses are difficult to assess without head-to-head comparison in time-consuming clinical trials. We hypothesized that computer simulations may be useful.
  
  
  METHODS
  Two open-label randomized clinical trials were replicated using computer simulations. One study compared performance of the enhanced model predictive control (eMPC) algorithm at two intensive care units in the United Kingdom and Belgium. The other study compared three glucose control algorithms-eMPC, Matias (the absolute glucose protocol), and Bath (the relative glucose change protocol)-in a single intensive care unit. Computer simulations utilized a virtual population of 56 critically ill subjects derived from routine data collected at four European surgical and medical intensive care units.
  
  
  RESULTS
  In agreement with the first clinical study, computer simulations reproduced the main finding and discriminated between the two intensive care units in terms of the sampling interval (1.3 h vs. 1.8 h, United Kingdom vs. Belgium; P < 0.01). Other glucose control metrics were comparable between simulations and clinical results. The principal outcome of the second study was also reproduced. The eMPC demonstrated better performance compared with the Matias and Bath algorithms as assessed by the time when plasma glucose was in the target range between 4.4 and 6.1 mmol/L (65% vs. 43% vs. 42% [P < 0.001], eMPC vs. Matias vs. Bath) without increasing the risk of severe hypoglycemia.
  
  
  CONCLUSIONS
  Computer simulations may provide resource-efficient means for preclinical evaluation of algorithms for glycemic control in the critically ill.
authors.affiliation: Institute of Metabolic Science, University of Cambridge, Cambridge, United Kingdom. mew37@cam.ac.uk
authors.forename: Malgorzata E
authors.initials: ME
authors.lastname: Wilinska
authors.forename: Jan
authors.initials: J
authors.lastname: Blaha
authors.forename: Ludovic J
authors.initials: LJ
authors.lastname: Chassin
authors.forename: Jeremy J
authors.initials: JJ
authors.lastname: Cordingley
authors.forename: Natalie C
authors.initials: NC
authors.lastname: Dormand
authors.forename: Martin
authors.initials: M
authors.lastname: Ellmerer
authors.forename: Martin
authors.initials: M
authors.lastname: Haluzik
authors.forename: Johannes
authors.initials: J
authors.lastname: Plank
authors.forename: Dirk
authors.initials: D
authors.lastname: Vlasselaers
authors.forename: Pieter J
authors.initials: PJ
authors.lastname: Wouters
authors.forename: Roman
authors.initials: R
authors.lastname: Hovorka
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D035843:Biomedical Research / Q000379:methods; D001786:Blood Glucose / Q000032:analysis; D003198:Computer Simulation; D016638:Critical Illness / Q000628:therapy*; D048909:Diabetes Complications / Q000628:therapy*; D003920:Diabetes Mellitus / Q000178:diet therapy / Q000188:drug therapy*; D005260:Female; D006801:Humans; D006943:Hyperglycemia / Q000517:prevention & control*; D007003:Hypoglycemia / Q000517:prevention & control*; D007004:Hypoglycemic Agents / Q000008:administration & dosage / Q000009:adverse effects / Q000627:therapeutic use; D007328:Insulin / Q000008:administration & dosage / Q000009:adverse effects / Q000627:therapeutic use; D007362:Intensive Care Units; D008297:Male; D008875:Middle Aged; D016032:Randomized Controlled Trials as Topic; D012189:Retrospective Studies; D018570:Risk Assessment / Q000379:methods
chemical_list: D001786:Blood Glucose; D007004:Hypoglycemic Agents; D007328:Insulin