Neural-net Artificial Pancreas (NAP)

NCT05876273 | Completed Results FDA Device

• 2 other identifiers: 230058R01DK133148
• Study Type: interventional
• Target: 15
• Locations: 1 country
• Sites: 1

Brief 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. The investigators will consent up to 20 participants, ages ≥18.0, with a goal of completing 15 participants.

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)

Outcome Measures

Primary Outcomes (1)

• 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)

Secondary Outcomes (4)

• Percent of Time in Hyperglycemia.

  36 hours (two 18-hour experiments)

• Percent of Time in Hypoglycemia.

  36 hours (two 18-hour experiments)

• System Functionality

  36 hours (two 18-hour experiments)

• Participant Feedback

  36 hours (two 18-hour experiments)

Study Arms (2)

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.

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.

Device: Neural-net Artificial Pancreas; Device: University of Virginia Model Predictive Control

Interventions

Neural-net Artificial Pancreas DEVICE

NAP is a neural-net implementation of the previously tested UMPC algorithm (below).

Also known as: NAP

NAP first, then UMPC; UMPC first, then NAP

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.

Also known as: UMPC

NAP first, then UMPC; UMPC first, then NAP

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• 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.
• 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.

You may not qualify if:

• 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:
• 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.

Sponsors & Collaborators

• University of Virginia: lead
• National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK): collaborator

Study Sites (1)

University of Virginia Center for Diabetes Technology

Charlottesville, Virginia, 22903, United States

Location

Related Publications (1)

• 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.

  PMID: 38277161 RESULT

Related Links

• Related Info

MeSH Terms

Conditions

Diabetes Mellitus, Type 1

Condition Hierarchy (Ancestors)

Diabetes Mellitus; Glucose Metabolism Disorders; Metabolic Diseases; Nutritional and Metabolic Diseases; Endocrine System Diseases; Autoimmune Diseases; Immune System Diseases

Results Point of Contact

• Title: Boris Kovatchev, PhD
• Organization: Center for Diabetes Technology, University of Virginia School of Medicine

boris@virginia.edu

434-924-5592

Study Officials

• 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

Publication Agreements

• PI is Sponsor Employee: No
• Restrictive Agreement: No

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: RANDOMIZED
• Masking: NONE
• Purpose: TREATMENT
• Intervention Model: CROSSOVER
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Principal Investigator

Model Details: 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.

Study Record Dates

• First Submitted: March 24, 2023
• First Posted: May 25, 2023
• Study Start: May 30, 2023
• Primary Completion: September 8, 2023
• Study Completion: September 10, 2023
• Last Updated: July 31, 2024
• Results First Posted: July 31, 2024

Record last verified: 2024-07

Data Sharing

• IPD Sharing: Will share
• Shared Documents: STUDY PROTOCOL, SAP, ICF
• Time Frame: Generally, data will be made available after the primary publications of each study.
• Access Criteria: The Data Sharing Agreements will be formulated by the study team.

Locations

US (1)