title: Modeling the glucose regulatory system in extreme preterm infants.
abstract: BACKGROUND
  Premature infants represent a significant proportion of the neonatal intensive care population. Blood glucose homeostasis in this group is often disturbed by immaturity of endogenous regulatory systems and the stress of their condition. Hypo- and hyperglycemia are frequently reported in very low birth weight infants, and more mature infants often experience low levels of glycemia. A model capturing the unique fundamental dynamics of the neonatal glucose regulatory system could be used to develop better blood glucose control methods.
  
  
  METHODS
  A metabolic system model is adapted from adult critical care to the unique physiological case of the neonate. Integral-based fitting methods were used to identify time-varying insulin sensitivity and non-insulin mediated glucose uptake profiles. The clinically important predictive ability of the model was assessed by assuming insulin sensitivity was constant over prediction intervals of 1, 2 and 4h forward and comparing model-simulated versus actual clinical glucose values for all recorded interventions. The clinical data included 1091 glucose measurements over 3567 total patient hours, along with all associated insulin and nutritional infusion data, for N=25 total cases. Ethics approval was obtained from the Upper South A Regional Ethics Committee for this study.
  
  
  RESULTS
  The identified model had a median absolute percentage error of 2.4% [IQR: 0.9-4.8%] between model-fitted and clinical glucose values. Median absolute prediction errors at 1-, 2- and 4-h intervals were 5.2% [IQR: 2.5-10.3%], 9.4% [IQR: 4.5-18.4%] and 13.6% [IQR: 6.3-27.6%] respectively.
  
  
  CONCLUSIONS
  The model accurately captures and predicts the fundamental dynamic behaviors of the neonatal metabolism well enough for effective clinical decision support in glycemic control. The adaptation from adult to a neonatal case is based on the data from the literature. Low prediction errors and very low fitting errors indicate that the fundamental dynamics of glucose metabolism in both premature neonates and critical care adults can be described by similar mathematical models.
authors.affiliation: Department of Mechanical Engineering, University of Canterbury, New Zealand.
authors.forename: Aaron
authors.initials: A
authors.lastname: Le Compte
authors.forename: J Geoffrey
authors.initials: JG
authors.lastname: Chase
authors.forename: Glynn
authors.initials: G
authors.lastname: Russell
authors.forename: Adrienne
authors.initials: A
authors.lastname: Lynn
authors.forename: Chris
authors.initials: C
authors.lastname: Hann
authors.forename: Geoffrey
authors.initials: G
authors.lastname: Shaw
authors.forename: Xing-Wei
authors.initials: XW
authors.lastname: Wong
authors.forename: Amy
authors.initials: A
authors.lastname: Blakemore
authors.forename: Jessica
authors.initials: J
authors.lastname: Lin
mesh_terms: D001786:Blood Glucose / Q000378:metabolism*; D015331:Cohort Studies; D005260:Female; D006801:Humans; D006943:Hyperglycemia / Q000378:metabolism; D007231:Infant, Newborn; D007234:Infant, Premature / Q000097:blood* / Q000378:metabolism*; D007328:Insulin / Q000097:blood; D008297:Male; D008954:Models, Biological; D012189:Retrospective Studies
chemical_list: D001786:Blood Glucose; D007328:Insulin