University of Virginia Center for Diabetes Technology
Charlottesville, Virginia, 22903, United States
NCT Number: NCT05876273
This study is intended to assess a Neural-net Artificial Pancreas (NAP) implementation of an established AP controller - the University of Virginia Model Predictive Control Algorithm (UMPC). The health outcomes achieved on NAP will be compared to the health outcomes achieved on UMPC in a randomized crossover design. The investigators will consent up to 20 participants, ages ≥18.0, with a goal of completing 15 participants.
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Notify Me18 year and older
All sexes
Interventional
Not applicable
Charlottesville, Virginia, 22903, United States
The study will follow a randomized cross-over design assessing glycemic control on a Neural-net Artificial Pancreas (NAP), compared to the previously tested University of Virginia Model Predictive Control (UMPC) algorithm, in a supervised hotel setting:
The study will involve Tandem t:slim X2 Control-IQ (CIQ) users who will continue to use their CIQ systems, except during the hotel sessions, which will use the DiAs prototyping platform, connected to a Tandem t:AP research pump and a Dexcom G6 sensor, and implementing NAP or UMPC. The study sensor will be the same sensor used by CIQ - it will be disconnected from CIQ and connected to DiAs.
Following enrollment, one week of automated insulin delivery (AID) data will be downloaded from the participants' pumps or t:connect accounts and will be used to establish a baseline and initialize the control algorithms. Participants will be then studied at a local hotel for 20 hours, including an 18-hour experiment, randomly receiving either NAP or UMPC. Participants will then receive the opposite intervention either sequentially during the same hotel stay, or in a second hotel stay up to 28 days following the first hotel stay. During these 18-hour hotel sessions participants will be followed to compare blood glucose control on NAP vs. UMPC. The study meals and activities will be kept the same between study sessions.
The investigators will analyze non-inferiority of NAP compared to UMPC, but this pilot feasibility study is not powered to formally test noninferiority. The primary outcome is percent time in range (TIR) (70 to 180 mg/dL) on NAP vs UMPC. Secondary outcomes include frequency of hypoglycemia (time below range = TBR) and hyperglycemia (time above range = TAR), as well as other safety and control metrics.
Healthy volunteers accepted: No
Only the study team can determine whether someone qualifies for participation.
Inclusion criteria
Exclusion criteria
NAP is a neural-net implementation of the previously tested UMPC algorithm (below).
Other names: NAP
A previously tested artificial pancreas control algorithm, based on a differential-equation model of the human metabolic system in diabetes.
Other names: UMPC
Time frame: 36 hours (two 18-hour experiments)
The primary outcome is percent of time in 70 to 180 mg/dL range on NAP vs UMPC.
Time frame: 36 hours (two 18-hour experiments)
Percent CGM readings above 180 mg/dL.
Time frame: 36 hours (two 18-hour experiments)
Percent CGM readings below 70 mg/dL.
Time frame: 36 hours (two 18-hour experiments)
The investigator will observe, record, and tabulate any system malfunctions requiring study team intervention.
Time frame: 36 hours (two 18-hour experiments)
The investigator will obtain qualitative feedback form the participants regarding system functionality.
University of Virginia
Other
Adaptive Motif-Based Control (AMBC): Pilot 1 - Neural Net Implementation of Automated Insulin Delivery
Acronym: NAP
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View the official ClinicalTrials.gov record (opens in a new tab)This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.
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