Feasibility Study of PID Versus MPC and HMS
|ClinicalTrials.gov Identifier: NCT01987206|
Recruitment Status : Completed
First Posted : November 19, 2013
Last Update Posted : July 22, 2016
The goal of this proposed study is to explore the feasibility of using a PID (Proportional-Integral-Derivative) controller versus an MPC (Model Predictive Control) controller algorithm in an artificial pancreas system, all other components and study design being equal.
The study consists of an evaluation of either type of control algorithm as a part of the Artificial Pancreas (AP) device during two periods of 27.5-hour closed-loop control in a clinic environment (Sansum Diabetes Research Institute, Santa Barbara, CA) separated by a minimum of 5 days and a maximum of 2 weeks. The 27.5-hour period includes: 2 announced meals (dinner and breakfast of 65g and 50g CHO respectively) preceded with a dose of rapid-acting insulin equivalent to 100% bolus based on each subject's Insulin to Carbohydrate (I:C) ratio and 1 unannounced meal (lunch of 65g carbohydrates, same meal content as dinner); complete night from 12:00 am to 7:00 am. The goal is to demonstrate that the AP device is able to maintain the subject blood glucose within a safe range at all times.
|Condition or disease||Intervention/treatment||Phase|
|Type 1 Diabetes Mellitus||Device: MPC control algorithm Device: PID control algorithm||Early Phase 1|
|Study Type :||Interventional (Clinical Trial)|
|Actual Enrollment :||10 participants|
|Intervention Model:||Crossover Assignment|
|Masking:||None (Open Label)|
|Official Title:||Feasibility Study of Using a PID (Proportional-Integral-Derivative) Controller Versus an MPC (Model Predictive Control) Controller Algorithm and Health Monitoring System (HMS)|
|Study Start Date :||July 2014|
|Actual Primary Completion Date :||August 2015|
|Actual Study Completion Date :||August 2015|
Active Comparator: PID algorithm with HMS
The control algorithm, at its core, is a Proportional-Integral-Derivative (PID)controller that incorporates an Internal Model Control (IMC) based tuning rule using an explicit model of human T1DM glucose-insulin dynamics. Parameters of the model are personalized based on a priori easily available subject parameters. This controller divides the control action into three components - the proportional distance between the current measurement and the target setpoint, the accumulated integral error as expressed by the area between the current state curve and the target set point over time, and the derivative rate of change of the current measurement.
The Health Monitoring System algorithm uses the same glucose monitoring (CGM) data as the PID control algorithm but utilizes a separate algorithm for trending and predictions of future glucose values. Using a redundant and independent algorithm is an important safety feature of the overall AP device.
Device: PID control algorithm
Experimental: MPC algorithm with HMS
The first control strategy is a flavor of Model Predictive Control (MPC) algorithm. MPC employs an explicit model of the process to be controlled when optimizing the input. Specifically, MPC controllers for glycemia control use a model of a human's T1DM insulin-glucose dynamics to predict the evolution of the blood glucose values over a so-called prediction horizon of controller steps, and optimize a predicted insulin input trajectory in order to optimize a specified cost objective that penalizes unsafe glycemic values, and also insulin usage.
The Health Monitoring System algorithm uses the same CGM data as the MPC control algorithm but utilizes a separate algorithm for trending and predictions of future glucose values. Using a redundant and independent algorithm is an important safety feature of the overall AP device.
Device: MPC control algorithm
- time spent in safe blood glucose range [ Time Frame: 24-hour closed loop ]The percentage of time spent in safe blood glucose range of [80-140] mg/dl will be the primary endpoint. More time spent inside the desired range will be considered successful. Expected levels are [70-180] mg/dl in the 5 hours after meals.
- glucose level extremes and need for outside intervention [ Time Frame: 24-hour closed loop ]The secondary endpoint measures glucose extremes and the need for outside intervention to prevent hypoglycemia or hyperglycemia. Interventions would be insulin injections or oral carbohydrates given to the subject by the physician. No need for physician intervention will be considered a successful outcome.
Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT01987206
|United States, California|
|Sansum Diabetes Research Institute|
|Santa Barbara, California, United States, 93105|