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| Name | Class |
|---|---|
| Juvenile Diabetes Research Foundation | OTHER |
| University of California, Santa Barbara | OTHER |
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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| PID algorithm with HMS | Active Comparator | 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. |
|
| MPC algorithm with HMS | Experimental | 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. |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| MPC control algorithm | Device |
| ||
| PID control algorithm |
| Measure | Description | Time Frame |
|---|---|---|
| time spent in safe blood glucose range | 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. | 24-hour closed loop |
| Measure | Description | Time Frame |
|---|---|---|
| glucose level extremes and need for outside intervention | 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. | 24-hour closed loop |
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Inclusion Criteria:
Exclusion Criteria:
Exhibit hypoglycemia unawareness.
Indications of cardiac arrhythmia.
Pregnancy (as determined by a positive blood pregnancy test performed in females of childbearing capacity during screening visit and urine test at time of admission for in-patient visit) or nursing mother.
Females who are sexually active and able to conceive that do not use contraception.
Diabetic ketoacidosis in the past 6 months prior to enrollment requiring emergency room visit or hospitalization
Severe hypoglycemia resulting in seizure or loss of consciousness in the 12 months prior to enrollment
Current treatment for a seizure disorder; Subjects with a history of seizures may be included in the study if they receive written clearance from their neurologist
Active infection
A known medical condition that in the judgment of the investigator might interfere with the completion of the protocol such as cognitive deficit.
Mental incapacity, unwillingness or language barriers precluding adequate understanding or co-operation, including subjects not able to read or write.
Coronary artery disease or heart failure.
Subjects with a history of coronary artery disease may be included in the study if they receive written clearance from their cardiologist
Presence of a known adrenal disorder
Active gastroparesis
If on antihypertensive, thyroid, anti-depressant or lipid lowering medication, lack of stability on the medication for the past 2 months prior to enrollment in the study
Uncontrolled thyroid disease; Adequately treated thyroid disease and celiac disease do not exclude subjects from enrollment
Abuse of alcohol
Current use of a beta blocker medication
Laboratory results:
Subject has skin conditions that, in the determination of the investigator, would preclude wearing the study devices (infusion set and sensor), in the abdomen. Examples include but are not limited to: psoriasis, burns, scaring, eczema, tattoos, and significant hypertrophy at sites of device wear; any known allergy to medical adhesives.
Currently on long-term treatment using prednisone. If subject had been on short term treatment of prednisone, defer enrollment until underlying condition and prednisone treatment have resolved.
Allergy to study drug, food or other study material.
Clinically significant screening ECG, physical examination, laboratory test, or vital sign abnormality.
Exposure to any investigational drug within 30 days.
History of malignancy within the 5 years before screening (other than basal cell carcinoma).
Currently smoking or discontinued smoking (including cigarettes, cigars, pipes) over the past 6 months.
Current participation in another investigational trial.
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| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Sansum Diabetes Research Institute | Santa Barbara | California | 93105 | United States |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 27289127 | Result | Pinsker JE, Lee JB, Dassau E, Seborg DE, Bradley PK, Gondhalekar R, Bevier WC, Huyett L, Zisser HC, Doyle FJ 3rd. Randomized Crossover Comparison of Personalized MPC and PID Control Algorithms for the Artificial Pancreas. Diabetes Care. 2016 Jul;39(7):1135-42. doi: 10.2337/dc15-2344. Epub 2016 Jun 11. |
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| ID | Term |
|---|---|
| D003922 | Diabetes Mellitus, Type 1 |
| ID | Term |
|---|---|
| D003920 | Diabetes Mellitus |
| D044882 | Glucose Metabolism Disorders |
| D008659 | Metabolic Diseases |
| D009750 | Nutritional and Metabolic Diseases |
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| Device |
|
| D004700 | Endocrine System Diseases |
| D001327 | Autoimmune Diseases |
| D007154 | Immune System Diseases |