Open-Source Automated Insulin Delivery Systems May Lead to Glycemic Control Improvement in Type 1 Diabetes

Research indicates that using an open-source automated insulin delivery systems can lead to greater glycemic control.

Results of an open-label trial in people with type 1 diabetes provide data supporting use of open-source automated insulin delivery (AID) systems in children and adults.

Named the Community Derived Automated Insulin Delivery (CREATE) trial, results of the multicenter, randomized trial, which included nearly 100 participants between 7 and 70 years of age from 4 study sites in New Zealand, provide insight into the safety and efficacy of an open-source AID system for use in people with type 1 diabetes, including a 14-percentage point difference in time spent in target glucose range in favor of the trial’s AID and control group.

“In children and adults with type 1 diabetes, the use of an open-source AID system resulted in a significantly higher percentage of time in the target glucose range than the use of a sensor-augmented insulin pump at 24 weeks,” wrote investigators.

With the rapid rate of advancement in diabetes technology and more and more data detailing the efficacy of open-source AID systems, the CREATE trial was designed and conducted by investigators representing multiple institutions across New Zealand with the intent of adding to this growing knowledge base. With enrollment beginning in September 2020 and lasting through May 2021, the trial’s aim was to compare use of an open-source AID system against a sensor-augmented insulin pump, which served as the control group for the trial. For the purpose of analysis, percentage of time in the target glucose range of 70-180 mg/dL (3.9-10.0 mmol/L between days 155-168, which was the final 2 weeks of the 24-week trial.

The AID system used in the study was a modified version of the AndroidAPS 2.8, with a standard OpenAPS 0.7.0 algorithm paired with a preproduction DANA-i insulin pump and Dexcom G6 CGM. Those randomized to the control arm of the study received either a Dexcom G6 CGM with high and low glucose alerts and their usual insulin pump or a preproduction DANA-i insulin pump to administer bolus insulin doses. Investigators noted the sensor-augmented insulin pump therapy was not designed to predict low-glucose levels or to suspend insulin administration.

A total 48 children and 49 adults underwent randomization, yielding a final cohort of 97 individuals, with 44 randomized to open-source AID and 53 to the control group. Investigators pointed out the unbalanced group size was exacerbated by the number of strata used during randomization. Among the 48 children, 21 were randomized to AID and 27 were randomized to the control group. Overall, this cohort had a median age of 13 (IQR, 9.0-15.0) years, 50% were female, and had a mean time in range of 56.1±11.7% during the run-in period. Among the 49 adults, 23 were randomized to open-source AID and 26 were randomized to the control group. Overall, this cohort had a mean age of 40.0 (29.0-45.0) years, 61% were female, and had a mean time in range of 62.4±14.4% during the run-in period.

At 24 weeks, the mean time in range among the open-source AID group increased from 61.2±12.3% to 71.2±12.1% compared to a decrease from 57.7±14.3% to 54.5±16.0% observed in the control group (adjusted difference, 14 percentage points [95% CI, 9.2-18.8]; P <.001), with no treatment effect according to age (P=.56). Further analysis suggested individuals in the open-source AID group spent 3 hours and 21 minutes more in the target range per day than those in the control group of the trial. Investigators highlighted no severe hypoglycemia or diabetic ketoacidosis occurred among either group during the trial. However, investigators noted 2 individuals in the AID group withdrew from the trial due to connectivity issues.

In an editorial, titled “On the Path toward Expanding Treatment Options for Diabetes,”, Sue Brown, MD, of the Center for Diabetes Technology at the University of Virginia, wrote in support of open-source AID systems based on the results of the current study and previous endeavors. Brown also pointed out the advantages of open-source AID systems against other diabetes technologies currently in use.

“This trial of an open-source configuration showed the superior performance of an AID system in yet another clinical trial, in keeping with remarkably consistent percentages of time in the target glucose range of approximately 70 to 75% across studies of different device types and algorithmic solutions. Investigators in this field strive to improve on these clinical targets by incorporating adjunctive therapies and new insulin formulations along with other innovations that increase ease of use by requiring less user input,” Brown wrote.

This article originally appeared on Endocrinology Network.


Burnside MJ, Lewis DM, Meier RA, et al. Open-source automated insulin delivery in type 1 diabetes. N Engl J Med 2022; 387:869-881
doi: 10.1056/NEJMoa2203913