title: The identifiable virtual patient model: comparison of simulation and clinical closed-loop study results.
abstract: BACKGROUND
  Optimizing a closed-loop insulin delivery algorithm for individuals with type 1 diabetes can be potentially facilitated by a mathematical model of the patient. However, model simulation studies that evaluate changes to the control algorithm need to produce conclusions similar to those that would be obtained from a clinical study evaluating the same modification. We evaluated the ability of a low-order identifiable virtual patient (IVP) model to achieve this goal.
  
  
  METHODS
  Ten adult subjects (42.5 ± 11.5 years of age; 18.0 ± 13.5 years diabetes; 6.9 ± 0.8% hemoglobin A1c) previously characterized with the IVP model were studied following the procedures independently reported in a pediatric study assessing proportional-integral-derivative control with and without a 50% meal insulin bolus. Peak postprandial glucose levels with and without the meal bolus and use of supplemental carbohydrate to treat hypoglycemia were compared using two-way analysis of variance and chi-square tests, respectively.
  
  
  RESULTS
  The meal bolus decreased the peak postprandial glucose levels in both the adult-simulation and pediatricclinical study (231 ± 38 standard deviation to 205 ± 33 mg/dl and 226 ± 51 to 194 ± 47 mg/dl, respectively; p = .0472). No differences were observed between the peak postprandial levels obtained in the two studies (clinical and simulation study not different, p = .57; interaction p = .83) or in the use of supplemental carbohydrate (3 occurrences in 17 patient days of closed-loop control in the clinical-pediatric study; 7 occurrences over 20 patient days in the adult-simulation study, p = .29).
  
  
  CONCLUSIONS
  Closed-loop simulations using an IVP model can predict clinical study outcomes in patients studied independently from those used to develop the model.
authors.affiliation: Yale University, New Haven, Connecticut, USA.
authors.forename: Sami S
authors.initials: SS
authors.lastname: Kanderian
authors.forename: Stuart A
authors.initials: SA
authors.lastname: Weinzimer
authors.forename: Garry M
authors.initials: GM
authors.lastname: Steil
mesh_terms: D000293:Adolescent; D000328:Adult; D000465:Algorithms; D000704:Analysis of Variance; D015415:Biomarkers / Q000097:blood; D001786:Blood Glucose / Q000187:drug effects / Q000378:metabolism; D016009:Chi-Square Distribution; D003198:Computer Simulation; D003922:Diabetes Mellitus, Type 1 / Q000097:blood / Q000188:drug therapy*; D004040:Dietary Carbohydrates / Q000008:administration & dosage; D004435:Eating; D005215:Fasting / Q000097:blood; D006442:Glycated Hemoglobin / Q000378:metabolism; D006801:Humans; D007003:Hypoglycemia / Q000139:chemically induced; D007004:Hypoglycemic Agents / Q000008:administration & dosage* / Q000009:adverse effects / Q000097:blood; D007328:Insulin / Q000008:administration & dosage* / Q000009:adverse effects / Q000097:blood; D007332:Insulin Infusion Systems; D008875:Middle Aged; D008954:Models, Biological; D016544:Patient Simulation; D019518:Postprandial Period; D015203:Reproducibility of Results; D013997:Time Factors
chemical_list: D015415:Biomarkers; D001786:Blood Glucose; D004040:Dietary Carbohydrates; D006442:Glycated Hemoglobin; D007004:Hypoglycemic Agents; D007328:Insulin; C517652:hemoglobin A1c protein, human