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Neural-net Artificial Pancreas (NAP)

NCT05876273 · Status: COMPLETED · Phase: NA · Type: INTERVENTIONAL · Enrollment: 15

Last updated 2024-07-31

Study results available
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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

Interventions

DEVICE

Neural-net Artificial Pancreas

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

DEVICE

University of Virginia Model Predictive Control

A previously tested artificial pancreas control algorithm, based on a differential-equation model of the human metabolic system in diabetes.

Sponsors & Collaborators

  • National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)

    collaborator NIH

  • University of Virginia

    lead OTHER

Principal Investigators

  • Boris P Kovatchev, PhD · University of Virginia Center for Diabetes Technology

  • Sue A Brown, MD · University of Virginia Center for Diabetes Technology

Study Design

Allocation
RANDOMIZED
Purpose
TREATMENT
Masking
NONE
Model
CROSSOVER

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2023-05-30
Primary Completion
2023-09-08
Completion
2023-09-10
FDA Device
Yes

Countries

  • United States

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT05876273 on ClinicalTrials.gov