title: Closed-Loop Control Performance of the Hypoglycemia-Hyperglycemia Minimizer (HHM) System in a Feasibility Study.
abstract: BACKGROUND
  This feasibility study investigated the insulin-delivery characteristics of the Hypoglycemia-Hyperglycemia Minimizer (HHM) System-an automated insulin delivery device-in participants with type 1 diabetes.
  
  
  METHODS
  Thirteen adults with type 1 diabetes were enrolled in this nonrandomized, uncontrolled, clinical-research-center-based feasibility study. The HHM System comprised a continuous subcutaneous insulin infusion pump, a continuous glucose monitor (CGM), and a model predictive control algorithm with a safety module, run on a laptop platform. Closed-loop control lasted approximately 20 hours, including an overnight period and two meals.
  
  
  RESULTS
  When attempting to minimize glucose excursions outside of a prespecified target zone, the predictive HHM System decreased insulin infusion rates below the participants' preset basal rates in advance of below-zone excursions (CGM < 90 mg/dl), and delivered 80.4% less insulin than basal during those excursions. Similarly, the HHM System increased infusion rates above basal during above-zone excursions (CGM > 140 mg/dl), delivering 39.9% more insulin than basal during those excursions. Based on YSI, participants spent a mean ± standard deviation (SD) of 0.2 ± 0.5% of the closed-loop control time at glucose levels < 70 mg/dl, including 0.3 ± 0.9% for the overnight period. The mean ± SD glucose based on YSI for all participants was 164.5 ± 23.5 mg/dl. There were nine instances of algorithm-recommended supplemental carbohydrate administrations, and there was no severe hypoglycemia or diabetic ketoacidosis.
  
  
  CONCLUSIONS
  Results of this study indicate that the current HHM System is a feasible foundation for development of a closed-loop insulin delivery device.
authors.affiliation: Animas Corporation, West Chester, PA, USA dfinan@its.jnj.com.
authors.forename: Daniel A
authors.initials: DA
authors.lastname: Finan
authors.affiliation: Animas Corporation, West Chester, PA, USA.
authors.forename: Thomas W
authors.initials: TW
authors.lastname: McCann
authors.affiliation: Animas Corporation, West Chester, PA, USA.
authors.forename: Linda
authors.initials: L
authors.lastname: Mackowiak
authors.affiliation: University of California, Santa Barbara, Santa Barbara, CA, USA Sansum Diabetes Research Institute, Santa Barbara, CA, USA.
authors.forename: Eyal
authors.initials: E
authors.lastname: Dassau
authors.affiliation: University of Virginia, Charlottesville, VA, USA.
authors.forename: Stephen D
authors.initials: SD
authors.lastname: Patek
authors.affiliation: University of Virginia, Charlottesville, VA, USA.
authors.forename: Boris P
authors.initials: BP
authors.lastname: Kovatchev
authors.affiliation: University of California, Santa Barbara, Santa Barbara, CA, USA Sansum Diabetes Research Institute, Santa Barbara, CA, USA.
authors.forename: Francis J
authors.initials: FJ
authors.lastname: Doyle
authors.affiliation: University of California, Santa Barbara, Santa Barbara, CA, USA Sansum Diabetes Research Institute, Santa Barbara, CA, USA.
authors.forename: Howard
authors.initials: H
authors.lastname: Zisser
authors.affiliation: Animas Corporation, West Chester, PA, USA.
authors.forename: Henry
authors.initials: H
authors.lastname: Anhalt
authors.affiliation: Animas Corporation, West Chester, PA, USA.
authors.forename: Ramakrishna
authors.initials: R
authors.lastname: Venugopalan
keywords: algorithm; artificial pancreas; closed-loop control; model predictive control; type 1 diabetes