Back to Search
Start Over
76-OR: In-Depth Review of Glycemic Control and Glycemic Variability in People with Type 1 Diabetes Using Open Source Artificial Pancreas Systems
- Source :
- Diabetes. 68
- Publication Year :
- 2019
- Publisher :
- American Diabetes Association, 2019.
-
Abstract
- Background: Thousands with type 1 diabetes are estimated to be using open source Artificial Pancreas Systems (APS) with commercially available insulin pumps, continuous glucose monitors (CGM), and an open source control algorithm to process glucose readings and adjust insulin delivery. OpenAPS and similar do-it-yourself (DIY) closed loop systems gained considerable interest in the online diabetes community. Many using DIY closed loop systems have chosen to donate their data to a shared, anonymized data repository called the “OpenAPS Data Commons.” The present study evaluated glycemic control and glycemic variability of CGM readings of 80 DIY closed loop users. Methods: We analyzed 19251 days (53 years) of CGM readings with a mean duration of 134 days per patient (min. 3 days, max. 917 days) after the patient started looping. Results: Mean glucose was 137 ± 20mg/dl and estimated glycated hemogloblin A1c (eA1c) was 6.40 ± 0.70%. Time in target range (70-180mg/dL) was 77.5 ± 10.5%, 4.3% of CGM readings were below 70mg/dL, 1.3% were below 54mg/dL, 18.2% were above 180mg/dL, and 4.1% of CGM readings were above 250mg/dL, respectively. A total of 6474 hypoglycemic events (CGM reading < 54mg/dL) was observed (daytime: 5004 [73.9%]; nighttime: 1765 [26.1%]), which corresponds to 0.34 hypoglycemic events per day. The mean duration of each hypoglycemia event was 65.4 ± 41.4 minutes, and 1484 events were prolonged (duration > 120 minutes; daytime: 1043 [76.30%]; nighttime: 324 [23.7%]). Coefficient of variation (CV) was 35.5 ± 5.9% (daytime: 35.4%; nighttime: 33.9%) and mean of daily differences (MODD) was 50.1 ± 13.5 mg/dL. Conclusion: Open source AP systems show potential to support stable glycemic control in people with T1D. This is the largest descriptive analysis of open source APS data to date. The results are promising, but open source APS should be investigated in additional detail before a conclusion about their safety and efficacy can be drawn. Disclosure A. Melmer: None. T. Züger: None. D.M. Lewis: Consultant; Self; Diabeloop SA, Roche Diabetes Care. S.M. Leibrand: Consultant; Self; Diabeloop SA, Roche Diabetes Care. M. Laimer: None.
- Subjects :
- 0301 basic medicine
Type 1 diabetes
business.industry
Endocrinology, Diabetes and Metabolism
Coefficient of variation
Insulin
medicine.medical_treatment
030209 endocrinology & metabolism
medicine.disease
Artificial pancreas
03 medical and health sciences
030104 developmental biology
0302 clinical medicine
Open source
Anesthesia
Diabetes mellitus
Internal Medicine
medicine
Glucose monitors
business
Glycemic
Subjects
Details
- ISSN :
- 1939327X and 00121797
- Volume :
- 68
- Database :
- OpenAIRE
- Journal :
- Diabetes
- Accession number :
- edsair.doi...........75928adda835b7429b7154fefe3f14fd
- Full Text :
- https://doi.org/10.2337/db19-76-or