2. Academic Validation
  3. Full closed loop open-source algorithm performance comparison in pigs with diabetes

Full closed loop open-source algorithm performance comparison in pigs with diabetes

  • Clin Transl Med. 2021 Apr;11(4):e387. doi: 10.1002/ctm2.387.
Rayhan A Lal 1 2 3,  Caitlin L Maikawa 4,  Dana Lewis 5,  Sam W Baker 6,  Anton A A Smith 7,  Gillie A Roth 4,  Emily C Gale 8,  Lyndsay M Stapleton 4,  Joseph L Mann 7,  Anthony C Yu 7,  Santiago Correa 7,  Abigail K Grosskopf 9,  Celine S Liong 4,  Catherine M Meis 7,  Doreen Chan 10,  Joseph P Garner 6 11,  David M Maahs 2 3,  Bruce A Buckingham 2 3,  Eric A Appel 2 3 4 7
Affiliations

Affiliations

  • 1 Division of Endocrinology, Department of Medicine, Stanford University, Stanford, California, USA.
  • 2 Division of Endocrinology, Department of Pediatrics, Stanford University, Stanford, California, USA.
  • 3 Stanford Diabetes Research Center, Stanford University, Stanford, California, USA.
  • 4 Department of Bioengineering, Stanford University, Stanford, California, USA.
  • 5 OpenAPS, Seattle, Washington, USA.
  • 6 Department of Comparative Medicine, Stanford University, Stanford, California, USA.
  • 7 Department of Materials Science & Engineering, Stanford University, Stanford, California, USA.
  • 8 Department of Biochemistry, Stanford University, Stanford, California, USA.
  • 9 Department of Chemical Engineering, Stanford University, Stanford, California, USA.
  • 10 Department of Chemistry, Stanford University, Stanford, California, USA.
  • 11 Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, California, USA.
PMID: 33931977 DOI: 10.1002/ctm2.387
Abstract

Understanding how automated Insulin delivery (AID) algorithm features impact glucose control under full closed loop delivery represents a critical step toward reducing patient burden by eliminating the need for carbohydrate entries at mealtimes. Here, we use a pig model of diabetes to compare AndroidAPS and Loop open-source AID systems without meal announcements. Overall time-in-range (70-180 mg/dl) for AndroidAPS was 58% ± 5%, while time-in-range for Loop was 35% ± 5%. The effect of the algorithms on time-in-range differed between meals and overnight. During the overnight monitoring period, pigs had an average time-in-range of 90% ± 7% when on AndroidAPS compared to 22% ± 8% on Loop. Time-in-hypoglycemia also differed significantly during the lunch meal, whereby pigs running AndroidAPS spent an average of 1.4% (+0.4/-0.8)% in hypoglycemia compared to 10% (+3/-6)% for those using Loop. As algorithm design for closed loop systems continues to develop, the strategies employed in the OpenAPS algorithm (known as oref1) as implemented in AndroidAPS for unannounced meals may result in a better overall control for full closed loop systems.

Keywords

automated insulin delivery; diabetes; open-source closed loop.

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