Assessment of a Fully-closed Loop AID System With a Context-aware Hyperglycemia Pattern Detection and Dosing Algorithm in People With Type 1 Diabetes
Oregon Health and Science University
Summary
An artificial pancreas (AP) is a control system for automatic insulin delivery. The investigators have implemented a high blood sugar detection and dosing algorithm for use within an AP control system. If a high blood sugar pattern is detected, correction insulin will be calculated and delivered. The investigators will test how well the new algorithm manages glucose compared to the AP control system without high blood sugar detection and dosing. This type of algorithm may improve glucose control for high risk patient populations.
Description
Participants will be on study for approximately 4 weeks. During the study, participants will wear an Omnipod to deliver insulin. Participants will also wear a Dexcom G6 CGM. The CGM system will send sensed glucose data every 5 minutes wirelessly via Bluetooth Low Energy (BTLE) to an Android smartphone running the iPancreas app. The closed-loop system will receive activity data through an activity watch worn by the participant. Participants will complete system training on Day 1 in clinic and then spend the rest of the 4 weeks under free-living conditions. The first 3 weeks of the study will be…
Eligibility
- Age range
- 18–100 years
- Sex
- All
- Healthy volunteers
- No
Inclusion Criteria: * Diagnosis of type 1 diabetes mellitus for at least 1 year. * Male or female participants 18 and older. * HbA1c or GMI ≥ 7.0% at screening. * Physically willing and able to perform 30 min of exercise (as determined by the investigator after reviewing the participant's activity level). * Current use of an FDA-approved hybrid closed loop system for ≥3 months. * Lives with another person age 18 or older who will sleep in the house at night and that can attend the training on using the system. * Lives within 40 miles of OHSU * Total daily insulin requirement is less than 139…
Interventions
- DeviceiPancreas automated insulin delivery system
The Model Predictive Control (MPC) insulin infusion algorithm contains a model within the controller that takes as an input the aerobic metabolic expenditure in addition to the CGM and meal in puts. The algorithm uses heart rate and accelerometer data collected on the patient's body to calculate metabolic expenditure (METs). The METs then acts on the model for the insulin dynamics, whereby more energy expenditure and longer duration exercise can lead to a more substantial effect of insulin on the CGM. The MPC also has missed meal insulin bolus detection where the system will calculate the amount of insulin that was missed for a meal. The missed meal boluses can be delivered automatically without any input from the user. This feature can also be disabled. The MPC has a new feature called hyperglycemia pattern detection and dosing algorithm that will analyze problem patterns associated with high blood sugar and automatically calculate and deliver a correction dose.
Location
- Oregon Health and Science UniversityPortland, Oregon