Safety and Feasibility of a Machine-Learning Bolus Priming Added to Existing Control Algorithm
Sue Brown
Summary
A randomized crossover trial assessing glycemic control using Reinforcement Learning trained Bolus Priming System (BPS\_RL) added to the the Automated Insulin Delivery as Adaptive NETwork (AIDANET algorithm) compared to the original AIDANET algorithm.
Description
After receiving training on the study equipment, participants will use the AIDANET system at home for 7 days/6 nights to establish a baseline and initialize the control algorithm. Participants will then be studied at a hotel session for 3 days/2 nights. Participants will transition to home use of AIDANET+ BPS\_RL for 7 days/6 nights.
Eligibility
- Age range
- 18+ years
- Sex
- All
- Healthy volunteers
- No
Inclusion Criteria: 1. Age ≥18.0 years old at time of consent 2. Clinical diagnosis, based on investigator assessment, of Type 1 Diabetes for at least one year. 3. Having used an AID system equipped with Dexcom G6 or G7 CGM within the last three months (does not need to be continuous use if CGM was unavailable for instance). 4. Currently using insulin for at least six months. 5. Willingness to switch to use a commercially approved personal insulin (e.g., lispro or aspart, or biosimilar approved products) within the study pump as directed by the study team. 6. Has one or more supportive compan…
Interventions
- DeviceAutomated Insulin Delivery Adaptive NETwork (AIDANET)
Group A participants will use the AIDANET system at home for 7 days/6 nights. They will continue use of AIDANET system for 18 hours during the hotel session and then use AIDANET+BPS\_RL for 18 hours during the hotel session.
- DeviceAIDANET+ BPS_RL→AIDANET
Group B participant will use the AIDANET+BPS\_RL system for 18 hours during the hotel session and will then use AIDANET system for 18 hours during the hotel session. They will continue to use AIDANET+BPS\_RL system at home for 7 days/6 night and then use the AIDANET system at home for 7 days/6 nights.
Location
- University of Virginia Center for Diabetes TechnologyCharlottesville, Virginia