Open-source automated insulin delivery systems AndroidAPS and Loop have been designed to incorporate mealtime carbohydrate entry into blood glucose (BG) predictions, and thus control of insulin delivery. The systems use models to predict future glucose and then alter insulin delivery to optimize glucose to a target range specified by the user (Figure 1).Īlgorithm schematic. 10 Without conventional regulatory oversight, design changes can occur more rapidly. As compared to commercial systems they offer significant transparency and personalization. 14, 15 Each has an emphasis on safety but are unregulated, as they are user-designed and user-driven. 10- 13 The three most common systems include OpenAPS, Loop, and AndroidAPS. Open-source algorithms for closed loop or automated insulin delivery (AID) predate commercial availability of such systems and have been developed by members of the diabetes community. 9 Improvements in meal-detection algorithms may present an avenue for improvement in completely autonomous control.
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7 A full closed loop system would enable entirely autonomous insulin delivery without user input, but these systems have yet to be successfully implemented commercially primarily due to a lack of efficient control in the post prandial period. Unfortunately, these are not fully automatic systems and users are still required to enter the quantity of carbohydrates they plan to eat into the pump for mealtime insulin boluses.
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8 Both devices use glucose measurements from a CGM to alter basal insulin delivery from an insulin pump through software running on an embedded microcontroller in the pump. 6, 7 In December 2019, a second HCL system by Tandem Diabetes Care (San Diego, CA) dubbed Control-IQ received FDA approval. 4, 5 In September 2016, the FDA approved the first hybrid closed loop (HCL) device, the MiniMed 670G (Medtronic, Dublin, Ireland). 1, 2, 4 Automated insulin delivery systems have been associated with improved glycemic outcomes and reduced patient burden. The recent advent of accurate continuous glucose monitors (CGM) has allowed the development of automated insulin delivery through continuous subcutaneous insulin infusion pumps.
1- 3 The result is that individuals with type 1 diabetes must carefully deliver insulin in relationship to their meals and exercise to manage their blood glucose. Accurate insulin dosing is critical to maintain glucose levels within the normal glycemic range and to prevent acutely dangerous hypoglycemic episodes as well as the long-term consequences of hyperglycemia. Type 1 diabetes management is highly burdensome, requiring regular monitoring of blood glucose levels and careful insulin delivery. 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. 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. During the overnight monitoring period, pigs had an average time-in-range of 90% ± 7% when on AndroidAPS compared to 22% ± 8% on Loop.
The effect of the algorithms on time-in-range differed between meals and overnight. Overall time-in-range (70–180 mg/dl) for AndroidAPS was 58% ± 5%, while time-in-range for Loop was 35% ± 5%. Here, we use a pig model of diabetes to compare AndroidAPS and Loop open-source AID systems without meal announcements. 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.
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