title: Model-based sensor-augmented pump therapy.
abstract: BACKGROUND
  In insulin pump therapy, optimization of bolus and basal insulin dose settings is a challenge. We introduce a new algorithm that provides individualized basal rates and new carbohydrate ratio and correction factor recommendations. The algorithm utilizes a mathematical model of blood glucose (BG) as a function of carbohydrate intake and delivered insulin, which includes individualized parameters derived from sensor BG and insulin delivery data downloaded from a patient's pump.
  
  
  METHODS
  A mathematical model of BG as a function of carbohydrate intake and delivered insulin was developed. The model includes fixed parameters and several individualized parameters derived from the subject's BG measurements and pump data. Performance of the new algorithm was assessed using n = 4 diabetic canine experiments over a 32 h duration. In addition, 10 in silico adults from the University of Virginia/Padova type 1 diabetes mellitus metabolic simulator were tested.
  
  
  RESULTS
  The percentage of time in glucose range 80-180 mg/dl was 86%, 85%, 61%, and 30% using model-based therapy and [78%, 100%] (brackets denote multiple experiments conducted under the same therapy and animal model), [75%, 67%], 47%, and 86% for the control experiments for dogs 1 to 4, respectively. The BG measurements obtained in the simulation using our individualized algorithm were in 61-231 mg/dl min-max envelope, whereas use of the simulator's default treatment resulted in BG measurements 90-210 mg/dl min-max envelope.
  
  
  CONCLUSIONS
  The study results demonstrate the potential of this method, which could serve as a platform for improving, facilitating, and standardizing insulin pump therapy based on a single download of data.
authors.affiliation: Medtronic Minimed Inc., 18000 Devonshire St., Northridge, CA 91325, USA. benyamin.grosman@medtronic.com
authors.forename: Benyamin
authors.initials: B
authors.lastname: Grosman
authors.forename: Gayane
authors.initials: G
authors.lastname: Voskanyan
authors.forename: Mikhail
authors.initials: M
authors.lastname: Loutseiko
authors.forename: Anirban
authors.initials: A
authors.lastname: Roy
authors.forename: Aloke
authors.initials: A
authors.lastname: Mehta
authors.forename: Natalie
authors.initials: N
authors.lastname: Kurtz
authors.forename: Neha
authors.initials: N
authors.lastname: Parikh
authors.forename: Francine R
authors.initials: FR
authors.lastname: Kaufman
authors.forename: John J
authors.initials: JJ
authors.lastname: Mastrototaro
authors.forename: Barry
authors.initials: B
authors.lastname: Keenan
mesh_terms: D000465:Algorithms; D000818:Animals; D015374:Biosensing Techniques / Q000295:instrumentation / Q000379:methods; D001786:Blood Glucose / Q000032:analysis; D015190:Blood Glucose Self-Monitoring / Q000295:instrumentation / Q000379:methods; D003198:Computer Simulation; D003922:Diabetes Mellitus, Type 1 / Q000097:blood / Q000662:veterinary; D004283:Dog Diseases / Q000097:blood; D004285:Dogs; D007004:Hypoglycemic Agents / Q000008:administration & dosage / Q000493:pharmacokinetics; D007328:Insulin / Q000008:administration & dosage* / Q000493:pharmacokinetics; D007332:Insulin Infusion Systems; D008962:Models, Theoretical
chemical_list: D001786:Blood Glucose; D007004:Hypoglycemic Agents; D007328:Insulin