Type1 DiabetesNAPNCT05876273
Neural-net Artificial Pancreas (NAP)
A study testing Neural-net Artificial Pancreas in people with Type1 Diabetes, run by University of Virginia at 1 site in United States.
Trial summary
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. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Type1 Diabetes
- What is being tested?
- Neural-net Artificial Pancreas
- Type of treatment
- Medical device
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 15 participants
- Where
- 1 siteUnited States
- Study dates
- 30 May 2023to 10 Sep 2023
- Phase
- Interventional
- Are there results?
- Yes, posted in the registrySummary in the doctors' view
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Who may be able to join
- Age ≥18.0 at time of consent.
- Clinical diagnosis, based on investigator assessment, of type 1 diabetes for at least one year.
- Currently using insulin for at least six months.
- Currently using the Control-IQ automated insulin delivery system for at least one mont.
- Hemoglobin A1c of ≤9%.
- Using insulin parameters such as insulin to carb ratio and correction factor consistently in order to dose insulin for meals or corrections.
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- Access to internet and willingness to upload data during the study as needed.
- If female of childbearing potential and sexually active, must agree to use a form of contraception to prevent pregnancy while a participant in the study. A negative serum or urine pregnancy test will be required for all females of childbearing potential within 24 hours prior to initiating the experimental algorithms. Participants who become pregnant will be discontinued from the study. Also, participants who during the study develop and express the intention to become pregnant within the timespan of the study will be discontinued.
- Willingness to use the University of Virginia Diabetes Assistant system throughout study session.
- Willingness to use personal Lispro (Humalog) or aspart (Novolog) during the study session.
- Willingness not to start any new non-insulin glucose-lowering agent during the course of the trial (including Sodium-glucose cotransporter-2 inhibitors, metformin/biguanides, glucagon-like peptide-1 receptor agonists, Pramlintide, Dipeptidyl peptidase-4 inhibitors, Sulfonylureas and nutraceuticals).
- Willingness to reschedule the hotel portion of the study if placed on systemic steroids (e.g. intravenous injection, intramuscular injection, intra-articular or oral routes).
- An understanding and willingness to follow the protocol and signed informed consent.
Who cannot join
- History of Diabetic Ketoacidosis (DKA) in the 12 months prior to enrollment.
- Severe hypoglycemia resulting in seizure or loss of consciousness in the 12 months prior to enrollment.
- Currently pregnant or intent to become pregnant during the trial.
- Currently breastfeeding.
- Currently being treated for a seizure disorder.
- Treatment with Meglitinides/Sulfonylureas at the time of hotel study.
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- Use of metformin/biguanides, glucagon-like peptide-1 agonists, Pramlintide, Dipeptidyl peptidase-4 inhibitors, Sodium-glucose cotransporter-2 inhibitors, or nutraceuticals intended for glycemic control with a change in dose in the past month.
- History of significant cardiac arrhythmia (except for benign premature atrial contractions and benign premature ventricular contractions which are permitted or previous ablation of arrhythmia without recurrence which may be permitted) or active cardiovascular disease.
- A known medical condition that in the judgment of the investigator might interfere with the completion of the protocol such as the following examples:
- Inpatient psychiatric treatment in the past 6 months.
- Presence of a known adrenal disorder.
- Uncontrolled thyroid disease.
- A known medical condition that in the judgment of the investigator might interfere with the completion of the protocol.
Trial locations and contacts
1 site in 1 country.
United States1 site
University of Virginia Center for Diabetes Technology
Charlottesville, Virginia, 22903
About this study
Official title: Adaptive Motif-Based Control (AMBC): Pilot 1 - Neural Net Implementation of Automated Insulin Delivery
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.
Read the detailed description
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.
- Conditions
- Type1 Diabetes
- Being tested
- Neural-net Artificial Pancreas, University of Virginia Model Predictive Control
- Keywords
- Artificial Pancreas (AP), Diabetes Mellitus, Type 1, Insulin Pump, Continuous Glucose Monitor (CGM), Model Predictive Control (MPC), Automated Insulin Delivery (AID), Adaptive Motif-based Control (AMBC)
- Medical subject headings
- Diabetes Mellitus, Type 1
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Neural-net Artificial PancreasDevice
Also called NAP
NAP is a neural-net implementation of the previously tested UMPC algorithm (below).
University of Virginia Model Predictive ControlDevice
Also called UMPC
A previously tested artificial pancreas control algorithm, based on a differential-equation model of the human metabolic system in diabetes.
| Study group | Role | What they receive |
|---|---|---|
| NAP first, then UMPC | Experimental | Participants will use the Neural Net Artificial Pancreas (NAP) algorithm for 18 hours. Then switch to the University of Virginia Model-Predictive Control (UMPC) for 18 hours. Receives: Device: Neural-net Artificial Pancreas; Device: University of Virginia Model Predictive Control |
| UMPC first, then NAP | Experimental | Participants will use the UMPC for 18 hours, then switch to NAP for 18 hours. Receives: Device: Neural-net Artificial Pancreas; Device: University of Virginia Model Predictive Control |
What is being measured
The primary outcome is the main question the study is designed to answer.
Percent of Time-in-Range (TIR) on NAP Versus UMPC.
The primary outcome is percent of time in 70 to 180 mg/dL range on NAP vs UMPC.
Measured: 36 hours (two 18-hour experiments)
Secondary outcomes (4)
Percent of Time in Hyperglycemia.
Percent CGM readings above 180 mg/dL.
Measured: 36 hours (two 18-hour experiments)
Percent of Time in Hypoglycemia.
Percent CGM readings below 70 mg/dL.
Measured: 36 hours (two 18-hour experiments)
System Functionality
The investigator will observe, record, and tabulate any system malfunctions requiring study team intervention.
Measured: 36 hours (two 18-hour experiments)
Participant Feedback
The investigator will obtain qualitative feedback form the participants regarding system functionality.
Measured: 36 hours (two 18-hour experiments)
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted25 May 2023
- Study start30 May 2023
- Primary completion8 Sep 2023
- Study completion10 Sep 2023
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Posted resultsParticipant flow, outcome measures and adverse events reported by the sponsor.
- Publications (1)Papers the sponsor has linked to this study.
- Other trials for Type1 DiabetesStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Treatment
- Allocation
- Randomised
- Intervention model
- Crossover
- Model description
- Randomized crossover: Participants will be randomized to two groups differing by the order of controller use: Group A: NAP, followed by UMPC; Group B: UMPC, followed by NAP.
- Masking
- None (open label)
- Enrolment
- 15 participants (actual)
Sponsor and investigators
- Lead sponsor
- University of Virginia
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Boris Kovatchev, PhD, University of Virginia
- Investigators
- Boris P Kovatchev, PhD, Study director, University of Virginia Center for Diabetes TechnologySue A Brown, MD, Principal investigator, University of Virginia Center for Diabetes Technology
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05876273
- Sponsor's protocol ID
- 230058
- Other IDs
- R01DK133148
- Acronym
- NAP
- Status last verified
- Jul 2024
- First submitted
- 24 Mar 2023
- Last updated
- 31 Jul 2024
- Results posted
- Yes, on ClinicalTrials.gov
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- Yes
- Individual data sharing
- Yes: Will follow the NIH Data Sharing Policy and Implementation Guidance on sharing research resources for research purposes to qualified individuals in the scientific community.
Publications
Kovatchev B, Castillo A, Pryor E, Kollar LL, Barnett CL, DeBoer MD, Brown SA. Neural-Net Artificial Pancreas: A Randomized Crossover Trial of a First-in-Class Automated Insulin Delivery Algorithm. Diabetes Technol Ther. 2024 Jun;26(6):375-382. doi: 10.1089/dia.2023.0469.
PubMed 38277161
Study at a glance
From the registry record, last updated by the sponsor on 31 Jul 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Adaptive Motif-Based Control (AMBC): Pilot 1 - Neural Net Implementation of Automated Insulin Delivery
- ClinicalTrials.gov ID
- NCT05876273
- Acronym / sponsor ID
- NAP / 230058
- Conditions
- Type1 Diabetes
- Keywords
- Artificial Pancreas (AP), Diabetes Mellitus, Type 1, Insulin Pump, Continuous Glucose Monitor (CGM), Model Predictive Control (MPC), Automated Insulin Delivery (AID), Adaptive Motif-based Control (AMBC)
- Study type
- Interventional
- Recruitment status
- Completed
- Enrolment
- 15 participants (actual)
- Lead sponsor
- University of VirginiaUniversity, hospital or other
- Collaborators
- National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)
- Study start
- 30 May 2023 (actual)
- Primary completion
- 8 Sep 2023 (actual)
- Study completion
- 10 Sep 2023 (actual)
- First posted
- 25 May 2023 (actual)
- Results
- Posted 31 Jul 2024
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Detailed description
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.
Study design
- Primary purpose
- Treatment
- Allocation
- Randomised
- Intervention model
- Crossover
- Masking
- None (open label)
- Number of arms
- 2
- Model description
- Randomized crossover: Participants will be randomized to two groups differing by the order of controller use: Group A: NAP, followed by UMPC; Group B: UMPC, followed by NAP.
| Arm | Type | Receives |
|---|---|---|
| NAP first, then UMPC | Experimental | Device: Neural-net Artificial Pancreas, Device: University of Virginia Model Predictive ControlParticipants will use the Neural Net Artificial Pancreas (NAP) algorithm for 18 hours. Then switch to the University of Virginia Model-Predictive Control (UMPC) for 18 hours. |
| UMPC first, then NAP | Experimental | Device: Neural-net Artificial Pancreas, Device: University of Virginia Model Predictive ControlParticipants will use the UMPC for 18 hours, then switch to NAP for 18 hours. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (13 listed)
- Age ≥18.0 at time of consent.
- Clinical diagnosis, based on investigator assessment, of type 1 diabetes for at least one year.
- Currently using insulin for at least six months.
- Currently using the Control-IQ automated insulin delivery system for at least one mont.
- Hemoglobin A1c of ≤9%.
Key exclusion criteria (13 listed)
- History of Diabetic Ketoacidosis (DKA) in the 12 months prior to enrollment.
- Severe hypoglycemia resulting in seizure or loss of consciousness in the 12 months prior to enrollment.
- Currently pregnant or intent to become pregnant during the trial.
- Currently breastfeeding.
- Currently being treated for a seizure disorder.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Neural-net Artificial PancreasAlso: NAPNAP is a neural-net implementation of the previously tested UMPC algorithm (below). | Device | NAP first, then UMPC, UMPC first, then NAP |
| University of Virginia Model Predictive ControlAlso: UMPCA previously tested artificial pancreas control algorithm, based on a differential-equation model of the human metabolic system in diabetes. | Device | NAP first, then UMPC, UMPC first, then NAP |
Primary outcome
Percent of Time-in-Range (TIR) on NAP Versus 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.
4 secondary outcomes
Percent of Time in Hyperglycemia.
Time frame: 36 hours (two 18-hour experiments)
Percent of Time in Hypoglycemia.
Time frame: 36 hours (two 18-hour experiments)
System Functionality
Time frame: 36 hours (two 18-hour experiments)
Participant Feedback
Time frame: 36 hours (two 18-hour experiments)
Results and safety
Loading the posted results from ClinicalTrials.gov…
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 31 Jul 2024.
- Posted resultsParticipant flow, baseline, all outcome measures and adverse events.
- Results publication: PubMed 38277161Kovatchev B, Castillo A, Pryor E, Kollar LL, Barnett CL, DeBoer MD, Brown SA. Neural-Net Artificial Pancreas: A Randomized Crossover Trial of a First-in-Class Automated Insulin Delivery Algorithm. Diabetes Technol Ther. 2024 Jun;26(6):375-382. doi: 10.1089/dia.2023.0469.
- Related InfoLink provided by the sponsor.
- Individual participant dataSharing planned: Yes. Will follow the NIH Data Sharing Policy and Implementation Guidance on sharing research resources for research purposes to qualified individuals in the scientific community.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Boris P Kovatchev, PhD study director
University of Virginia Center for Diabetes Technology
Sue A Brown, MD principal investigator
University of Virginia Center for Diabetes Technology
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing Neural-net Artificial Pancreas in people with Type1 Diabetes, run by University of Virginia at 1 site in United States.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 13 inclusion and 13 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 31 Jul 2024. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in United States. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Yes. Results are posted in the registry (first posted 31 Jul 2024). A summary is in the For doctors view, and the full results are on ClinicalTrials.gov.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Percent of Time-in-Range (TIR) on NAP Versus UMPC. (time frame: 36 hours (two 18-hour experiments)). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT05876273, which also lists 1 linked publication.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 31 Jul 2024.