title: Generalisability of a Virtual Trials Method for Glycaemic Control in Intensive Care.
abstract: Elevated blood glucose (BG) concentrations (Hyperglycaemia) are a common complication in critically ill patients. Insulin therapy is commonly used to treat hyperglycaemia, but metabolic variability often results in poor BG control and low BG (hypoglycaemia).
  
  
  
  This paper presents a model-based virtual trial method for glycaemic control protocol design, and evaluates its generalisability across different populations.
  
  
  
  Model-based insulin sensitivity (SI) was used to create virtual patients from clinical data from three different ICUs in New Zealand, Hungary, and Belgium. Glycaemic results from simulation of virtual patients under their original protocol (self-simulation) and protocols from other units (cross simulation) were compared.
  
  
  
  Differences were found between the three cohorts in median SI and inter-patient variability in SI. However, hour-to-hour intra-patient variability in SI was found to be consistent between cohorts. Self and cross-simulation results were found to have overall similarity and consistency, though results may differ in the first 24-48 h due to different cohort starting BG and underlying SI.
  
  
  
  Virtual patients and the virtual trial method were found to be generalisable across different ICUs. This virtual trial method is useful for in silico protocol design and testing, given an understanding of the underlying assumptions and limitations of this method.
authors.forename: Jennifer L
authors.initials: JL
authors.lastname: Dickson
authors.forename: Kent W
authors.initials: KW
authors.lastname: Stewart
authors.forename: Christopher G
authors.initials: CG
authors.lastname: Pretty
authors.forename: Marine
authors.initials: M
authors.lastname: Flechet
authors.forename: Thomas
authors.initials: T
authors.lastname: Desaive
authors.forename: Sophie
authors.initials: S
authors.lastname: Penning
authors.forename: Bernard C
authors.initials: BC
authors.lastname: Lambermont
authors.forename: Balazs
authors.initials: B
authors.lastname: Benyo
authors.forename: Geoffrey M
authors.initials: GM
authors.lastname: Shaw
authors.forename: J Geoffrey
authors.initials: JG
authors.lastname: Chase
mesh_terms: D000368:Aged; D001786:Blood Glucose / Q000032:analysis / Q000502:physiology; D003198:Computer Simulation; D016638:Critical Illness; D016208:Databases, Factual; D005260:Female; D006801:Humans; D006943:Hyperglycemia / Q000188:drug therapy / Q000503:physiopathology / Q000517:prevention & control; D007328:Insulin / Q000008:administration & dosage / Q000493:pharmacokinetics / Q000627:therapeutic use; D007333:Insulin Resistance / Q000502:physiology*; D008297:Male; D008875:Middle Aged; D008954:Models, Biological; D012189:Retrospective Studies
chemical_list: D001786:Blood Glucose; D007328:Insulin