At a glance
ClinicalIndex Comparison RecordStandardized by ClinicalIndex from the ClinicalTrials.gov record · verify against the source.
A Randomized, Two-way, Cross-over Study to Assess the Efficacy of an MPC Exercise-enabled Closed-loop System vs FMPD Exercise-enabled Closed-loop System
In Brief
A clinical study evaluating FMPD AP algorithm, MPC AP system, and 1 other intervention for Type 1 Diabetes. Completed, enrolled 25 participants across 1 site.
Detailed Summary
An artificial pancreas (AP) is a control system for automatic insulin delivery. Our group has implemented a fading memory proportional derivative controller (FMPD) for use within an AP control system which has been evaluated in clinical studies. However, the long action of insulin (90 minutes for peak action) makes it challenging to control insulin with a classical proportional derivative system. The study described within this protocol is designed to test the effectiveness of a new model-predictive control (MPC) AP that modulates insulin delivery based on estimated activity level. The potential benefit of this type of AP is that it handles exercise not as a discrete event, but it automatically adjusts insulin delivery based on estimated activity level calculated at every 5 minute cycle. This type of algorithm may significantly improve glucose control over our FMPD AP, which is designed only to detect exercise when activity level goes above a threshold for a specific duration of 45 minutes.
Study Details
Timeline
Interventions
The Fading Memory Proportional Derivative (FMPD) insulin infusion algorithm determines insulin delivery rates based on proportional error, defined as the difference between the current CGM level and the target CGM level, and the derivative error, defined as the rate of change of the CGM. The FMPD algorithm utilizes derivative and proportional glucose errors to determine delivery rates of insulin.
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 inputs. The algorithm uses heart rate and accelerometer data collected on the patient's body to calculate metabolic expenditure. The metabolic expenditure 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 Dexcom G6 CGM measures interstitial glucose through a sensor transmitter. This glucose value is reported to the MPC algorithm during this intervention.