INTRODUCTION: Safe and effective closed-loop control (artificial pancreas) is the ultimate goal of insulin delivery. In this study, we examined the performance of a closed-loop control algorithm used for the overnight time period to safely achieve a narrow target range of blood glucose (BG) concentrations prior to breakfast. The primary goal was to compare the quality of algorithm control during repeated overnight experiments. MATERIALS AND METHODS: Twenty-three subjects with type 1 diabetes performed 2 overnight experiments on each of three visits at the study site, resulting in 138 overnight experiments. On the first evening, the subject's insulin therapy was applied; on the second, the insulin was delivered by an algorithm based on subcutaneous continuous glucose measurements (including meal control) until midnight. Overnight closed-loop control was applied between midnight and 6 a.m. based on hourly venous BG measurements during the first and second nights. RESULTS: The number of BG values within the target range (90-150 mg/dl) increased from 52.9% (219 out of 414 measurements) during the first nights to 72.2% (299 out of 414 measurements) during the second nights (p < .001, χ²-test). The occurrence of hypoglycemia interventions was reduced from 14 oral glucose interventions, the latest occurring at 2:36 a.m. during the first nights, to 1 intervention occurring at 1:02 a.m. during the second nights (p < .001, χ²-test). CONCLUSIONS: Overnight controller performance improved when optimized initial control was given; this was suggested by the better metabolic control during the second night. Adequate controller run-in time seems to be important for achieving good overnight control. In addition, the findings demonstrate that hourly BG data are sufficient for the closed-loop control algorithm tested to achieve appropriate glycemic control.
INTRODUCTION: Safe and effective closed-loop control (artificial pancreas) is the ultimate goal of insulin delivery. In this study, we examined the performance of a closed-loop control algorithm used for the overnight time period to safely achieve a narrow target range of blood glucose (BG) concentrations prior to breakfast. The primary goal was to compare the quality of algorithm control during repeated overnight experiments. MATERIALS AND METHODS: Twenty-three subjects with type 1 diabetes performed 2 overnight experiments on each of three visits at the study site, resulting in 138 overnight experiments. On the first evening, the subject's insulin therapy was applied; on the second, the insulin was delivered by an algorithm based on subcutaneous continuous glucose measurements (including meal control) until midnight. Overnight closed-loop control was applied between midnight and 6 a.m. based on hourly venous BG measurements during the first and second nights. RESULTS: The number of BG values within the target range (90-150 mg/dl) increased from 52.9% (219 out of 414 measurements) during the first nights to 72.2% (299 out of 414 measurements) during the second nights (p < .001, χ²-test). The occurrence of hypoglycemia interventions was reduced from 14 oral glucose interventions, the latest occurring at 2:36 a.m. during the first nights, to 1 intervention occurring at 1:02 a.m. during the second nights (p < .001, χ²-test). CONCLUSIONS: Overnight controller performance improved when optimized initial control was given; this was suggested by the better metabolic control during the second night. Adequate controller run-in time seems to be important for achieving good overnight control. In addition, the findings demonstrate that hourly BG data are sufficient for the closed-loop control algorithm tested to achieve appropriate glycemic control.
Authors: David M Nathan; Patricia A Cleary; Jye-Yu C Backlund; Saul M Genuth; John M Lachin; Trevor J Orchard; Philip Raskin; Bernard Zinman Journal: N Engl J Med Date: 2005-12-22 Impact factor: 91.245
Authors: Bruce Buckingham; H Peter Chase; Eyal Dassau; Erin Cobry; Paula Clinton; Victoria Gage; Kimberly Caswell; John Wilkinson; Fraser Cameron; Hyunjin Lee; B Wayne Bequette; Francis J Doyle Journal: Diabetes Care Date: 2010-03-03 Impact factor: 19.112
Authors: Eda Cengiz; Karena L Swan; William V Tamborlane; Garry M Steil; Amy T Steffen; Stuart A Weinzimer Journal: Diabetes Technol Ther Date: 2009-04 Impact factor: 6.118
Authors: William V Tamborlane; Roy W Beck; Bruce W Bode; Bruce Buckingham; H Peter Chase; Robert Clemons; Rosanna Fiallo-Scharer; Larry A Fox; Lisa K Gilliam; Irl B Hirsch; Elbert S Huang; Craig Kollman; Aaron J Kowalski; Lori Laffel; Jean M Lawrence; Joyce Lee; Nelly Mauras; Michael O'Grady; Katrina J Ruedy; Michael Tansey; Eva Tsalikian; Stuart Weinzimer; Darrell M Wilson; Howard Wolpert; Tim Wysocki; Dongyuan Xing Journal: N Engl J Med Date: 2008-09-08 Impact factor: 91.245
Authors: Stuart A Weinzimer; Garry M Steil; Karena L Swan; Jim Dziura; Natalie Kurtz; William V Tamborlane Journal: Diabetes Care Date: 2008-02-05 Impact factor: 19.112