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| ID | Type | Description | Link |
|---|---|---|---|
| R01DK133148 | U.S. NIH Grant/Contract | View source |
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| Name | Class |
|---|---|
| National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) | NIH |
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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.
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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| 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. |
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| UMPC first, then NAP | Experimental | Participants will use the UMPC for 18 hours, then switch to NAP for 18 hours. |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Neural-net Artificial Pancreas | Device | NAP is a neural-net implementation of the previously tested UMPC algorithm (below). |
|
| Measure | Description | Time Frame |
|---|---|---|
| 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. | 36 hours (two 18-hour experiments) |
| Measure | Description | Time Frame |
|---|---|---|
| Percent of Time in Hyperglycemia. | Percent CGM readings above 180 mg/dL. | 36 hours (two 18-hour experiments) |
| Percent of Time in Hypoglycemia. | Percent CGM readings below 70 mg/dL. |
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Inclusion Criteria:
Exclusion Criteria:
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.
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:
A known medical condition that in the judgment of the investigator might interfere with the completion of the protocol.
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| Name | Affiliation | Role |
|---|---|---|
| Boris P Kovatchev, PhD | University of Virginia Center for Diabetes Technology | Study Director |
| Sue A Brown, MD | University of Virginia Center for Diabetes Technology | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| University of Virginia Center for Diabetes Technology | Charlottesville | Virginia | 22903 | United States |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 38277161 | Result | 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. |
| Label | URL |
|---|---|
| Related Info | View source |
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Will follow the NIH Data Sharing Policy and Implementation Guidance on sharing research resources for research purposes to qualified individuals in the scientific community.
Generally, data will be made available after the primary publications of each study.
The Data Sharing Agreements will be formulated by the study team.
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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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| ID | Title | Description |
|---|---|---|
| FG000 | NAP First, Then UMPC | 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. |
| FG001 | UMPC First, Then NAP | Participants will use the UMPC for 18 hours, then switch to NAP for 18 hours. |
| Title | Milestones | Reasons Not Completed | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Overall Study |
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| ID | Title | Description |
|---|---|---|
| BG000 | All Participants | Participants completed two consecutive 20-hour hotel sessions, receiving in random order either NAP or UMPC |
| Units | Counts |
|---|---|
| Participants |
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| Title | Description | Population Description | Parameter Type | Dispersion Type | Unit of Measure | Calculate Percentage | Denominator Units Selected | Denominators | Classes |
|---|---|---|---|---|---|---|---|---|---|
| Age, Categorical | Count of Participants |
| Type | Title | Description | Population Description | Reporting Status | Anticipated Posting Date | Parameter Type | Dispersion Type | Unit of Measure | Calculate Percentage | Time Frame | Units Analyzed | Denominator Units Selected | Arm/Group Information | Denominators | Classes | Analyses | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Primary | 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. | Posted | Mean | Standard Deviation | Percent of time between 70-180 mg/dl | 36 hours (two 18-hour experiments) |
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Adverse event data were collected while participants were enrolled in the study (approximately 2 months)
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| ID | Title | Description | Deaths (Affected) | Deaths (At Risk) | Serious Events (Affected) | Serious Events (At Risk) | Other Events (Affected) | Other Events (At Risk) |
|---|---|---|---|---|---|---|---|---|
| EG000 | NAP First, Then UMPC | 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. |
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| Title | Organization | Phone | Extension | |
|---|---|---|---|---|
| Boris Kovatchev, PhD | Center for Diabetes Technology, University of Virginia School of Medicine | 434-924-5592 | boris@virginia.edu |
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| Type | Includes Protocol | Includes SAP | Includes ICF | Document Label | Document Date | Document Uploaded Date | Document File Name |
|---|---|---|---|---|---|---|---|
| Prot_SAP | Yes | Yes | No | Study Protocol and Statistical Analysis Plan | Jun 19, 2023 | Sep 13, 2023 | Prot_SAP_000.pdf |
| ICF | No | No | Yes | Informed Consent Form | Jun 19, 2023 | Oct 23, 2023 | ICF_002.pdf |
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| ID | Term |
|---|---|
| D003922 | Diabetes Mellitus, Type 1 |
| ID | Term |
|---|---|
| D003920 | Diabetes Mellitus |
| D044882 | Glucose Metabolism Disorders |
| D008659 | Metabolic Diseases |
| D009750 | Nutritional and Metabolic Diseases |
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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.
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| University of Virginia Model Predictive Control | Device | A previously tested artificial pancreas control algorithm, based on a differential-equation model of the human metabolic system in diabetes. |
|
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| 36 hours (two 18-hour experiments) |
| System Functionality | The investigator will observe, record, and tabulate any system malfunctions requiring study team intervention. | 36 hours (two 18-hour experiments) |
| Participant Feedback | The investigator will obtain qualitative feedback form the participants regarding system functionality. | 36 hours (two 18-hour experiments) |
| Participants |
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| Age, Continuous | Mean | Full Range | years |
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| Sex: Female, Male | Count of Participants | Participants |
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| Ethnicity (NIH/OMB) | Count of Participants | Participants |
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| Race (NIH/OMB) | Count of Participants | Participants |
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| Region of Enrollment | Number | participants |
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| Percent Time in Range (70-180 mg/dL) | Mean | Standard Deviation | Percent of time between 70-180 mg/dl |
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|
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| Secondary | Percent of Time in Hyperglycemia. | Percent CGM readings above 180 mg/dL. | Posted | Mean | Standard Deviation | Percent of time above 180 mg/dl | 36 hours (two 18-hour experiments) |
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| Secondary | Percent of Time in Hypoglycemia. | Percent CGM readings below 70 mg/dL. | Posted | Mean | Standard Deviation | Percent of time below 70 mg/dl | 36 hours (two 18-hour experiments) |
|
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| Secondary | System Functionality | The investigator will observe, record, and tabulate any system malfunctions requiring study team intervention. | Posted | Number | Number of reportable device malfunctions | 36 hours (two 18-hour experiments) |
|
|
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| Secondary | Participant Feedback | The investigator will obtain qualitative feedback form the participants regarding system functionality. | Data were not collected | Posted | 36 hours (two 18-hour experiments) |
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|
| 0 |
| 8 |
| 0 |
| 8 |
| 0 |
| 8 |
| EG001 | UMPC First, Then NAP | Participants will use the UMPC for 18 hours, then switch to NAP for 18 hours. | 0 | 7 | 0 | 7 | 0 | 7 |
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| D004700 | Endocrine System Diseases |
| D001327 | Autoimmune Diseases |
| D007154 | Immune System Diseases |