Feasibility Study of a Modular Control to Range System in... | NCT01418703

NCT01418703

Protocol Section

Identification Module

Results Section

Participant Flow Module

Annotation Section

No data available

No data is available for this block.

Document Section

No data available

No data is available for this block.

Derived Section

Miscellaneous Info Module

Status Module

Name	Class
Abbott Diabetes Care	INDUSTRY
Insulet Corporation	INDUSTRY
DexCom, Inc.	INDUSTRY
University of California, Santa Barbara

Description Module

Arms/Interventions Module

Arm Groups

Label	Type	Description	Intervention Names
Closed Loop Control (CLC)	Experimental	The CLC used a computer to make recommendations for their insulin treatment. This study arm was designed to demonstrate management of glucose using a modular insulin management system based on continuous glucose monitoring and targeted towards the avoidance of hypoglycemic and prolonged hyperglycemic episodes (i.e. control to range). This system was designed to both: monitor the meal boluses of the patient and correct it in case of observed/predicted under insulinization (avoidance of prolonged hyperglycemia), based on a coarse and subjective knowledge of the meal amount, a precise understanding of the subject's day to day insulin treatment, continuous glucose monitoring, and past insulin injections; predict and avoid hypoglycemic events, based on continuous glucose reading and past insulin injection.	Device: Closed Loop Control (CLC)
Open Loop	Placebo Comparator	The subject were in charge of their insulin treatment.	Device: Open Loop

Interventions

Name	Type	Description	Arm Group Labels	Other Names
Closed Loop Control (CLC)	Device	In this study, the researchers compared the management of diabetes during physical activity and meals with the closed-loop system developed at the University of Virginia. This Control to Range System uses two DexCom Seven Continuous Glucose Monitor, a computer containing an investigational control algorithm (well-defined instructions that are expressed in mathematical equations), and an OmniPod Insulin Management System, a portable subcutaneous (under the skin) insulin pump. During the closed-loop admission, the computer used CGM values to make recommendations of insulin treatment based on the algorithms.

Outcomes Module

Primary Outcomes

Measure	Description	Time Frame
Hypoglycemic Events	Number of hypoglycemic events below 70 mg/dL per patient per day	Throughout each 22-hour closed-loop and open-loop admission for sCTR and eCTR

Secondary Outcomes

Measure	Description	Time Frame
Percent Time Spent in Near Normoglycemia	Comparison of time spent in near normoglycemia (3.9 to 10 mmol/mL) in open-loop vs closed-loop sCTR and eCTR.	Throughout each 22-hour closed-loop and open-loop admission for sCTR and eCTR
Mean Glucose

Eligibility Module

Eligibility Criteria

Inclusion Criteria:

21 years of age and up to and including 65 years of age.
Have Type 1 Diabetes Mellitus as defined by American Diabetes Association criteria or judgment of physician for at least 2 years (including those who may also be treated with metformin, thiazolidinedione, exenatide, or pramlintide).
Use of an insulin pump
If on antihypertensive, thyroid, anti-depressant or lipid lowering medication, have stability on the medication for at least 2 months prior to enrollment in the study
Willingness to use lispro (Humalog) insulin for the duration of the inpatient study
Demonstration of proper mental status and cognition for the study
Willingness to stop using any dietary supplements for two weeks prior to admission and for the duration of their participation.

Exclusion Criteria:

Age <21or >65
Pregnancy
Hematocrit <36% (females); <38% (males)
HemoglobinA1c > 10.0
Symptomatic coronary artery disease (e.g. history of myocardial infarction, history of acute coronary syndrome, history of therapeutic coronary intervention, history of coronary bypass or stenting procedure, stable or unstable angina, episode of chest pain of cardiac etiology with documented EKG changes, positive stress test or catheterization with coronary blockages >50%).
Use of a medication that significantly lowers heart rate (beta blockers, reserpine, guanethidine, methyldopa, clonidine, cimetidine, digitalis, calcium channel blockers, amiodarone, antiarrythmic drugs, or lithium)
Congestive heart failure
History of a cerebrovascular event
Use of a medication that significantly impacts glucose metabolism (oral steroids)
Atrial fibrillation
Uncontrolled hypertension (resting blood pressure >140/90)
History of a systemic or deep tissue infection with methicillin- resistant staph aureus or Candida albicans
Use of a device that may pose electromagnetic compatibility issues and/or radiofrequency interference with the FreeStyle NavigatorTM CGM (implantable cardioverter-defibrillator, electronic pacemaker, neurostimulator,intrathecal pump, and cochlear implants)
Active enrollment in another clinical trial
Allergy or adverse reaction to lispro insulin
Known adrenal gland problem, pancreatic tumor, or insulinoma
Current alcohol abuse by patient history, substance abuse by patient history, or severe mental illness
Retinopathy and renal failure
Uncontrolled anxiety or panic disorder
Known bleeding diathesis or dyscrasia
Renal insufficiency (creatinine >1.5)
Any comorbid condition affecting glucose metabolism

Name	Affiliation	Role
Marc D Breton, Ph.D.	University of Virgnia - School of Medicine, Psychiatry and Neurobehavioral Sciences	Principal Investigator

Facility	Status	City	State	ZIP	Country	Contacts
University of Virginia Health System		Charlottesville	Virginia	22904	United States

References Module

Citations

PubMed Identifier	Type	Citation	Retractions
22688340	Result	Breton M, Farret A, Bruttomesso D, Anderson S, Magni L, Patek S, Dalla Man C, Place J, Demartini S, Del Favero S, Toffanin C, Hughes-Karvetski C, Dassau E, Zisser H, Doyle FJ 3rd, De Nicolao G, Avogaro A, Cobelli C, Renard E, Kovatchev B; International Artificial Pancreas Study Group. Fully integrated artificial pancreas in type 1 diabetes: modular closed-loop glucose control maintains near normoglycemia. Diabetes. 2012 Sep;61(9):2230-7. doi: 10.2337/db11-1445. Epub 2012 Jun 11.

Arm/Group Information

ID	Title	Description
FG000	Open-Loop First, Then sCTR Closed-Loop	Completed open-loop, then sCTR (Standard Control to Range ) Closed-Loop admission. The two modules of sCTR are the SSM (safety supervision module) and a standard range control module that avoids prolonged hyperglycemic excursions. Both modules use a real-time estimate of the patient 's metabolic state based on CGM (continuous glucose monitor) and insulin infusion data. This estimate is used for prediction of the risks of hypo-and hyperglycemia 30-45 min ahead of the event. If a risk for hypoglycemia is predicted, the SSM attenuates automatically any insulin requests proportionally to the predicted risk level. How aggressively the system attenuates insulin is determined with patient characteristics (e.g. body weight, insulin-to-carbohydrate ratio, and basal insulin delivery). If a risk for hyperglycemia is predicted, the range controller gives a correction bolus using the predicted plasma glucose and the patient's CSII (continuous subcutaneous insulin infusion) parameters.
FG001	sCTR Closed-Loop First, Then Open-Loop	Participants completed sCTR Closed-Loop admission, then completed open-loop admission. The two modules of sCTR are the SSM and a standard range control module that avoids prolonged hyperglycemic excursions. Both modules use a real-time estimate of the patient 's metabolic state based on CGM and insulin infusion data. This estimate is used for prediction of the risks of hypo-and hyperglycemia 30-45 min ahead of the event. If a risk for hypoglycemia is predicted, the SSM attenuates automatically any insulin requests proportionally to the predicted risk level. How aggressively the system attenuates insulin is determined with patient characteristics (e.g., body weight, insulin-to-carbohydrate ratio, and basal insulin delivery). If a risk for hyperglycemia is predicted, the range controller gives a correction bolus using the predicted plasma glucose and the patient's CSII parameters; the system injects only half of the computed bolus and can do so once every hour.
FG002	Open-Loop First, Then eCTR Closed-Loop	Participants completed open-loop admission, then completed eCTR (Enhanced Control to Range) closed-loop admission. The two modules of the eCTR are the SSM and an enhanced range control module based on an MPC (model predictive control) algorithm that aims to maintain glycemia in a target range. eCTR also uses insulin-on-board constraints (29) intended to prevent insulin overdose during intensified therapy. The rationale behind MPC was presented in detail in a recent review (7). Controller aggressiveness was individualized for each subject based on readily available patient characteristics (e.g., body weight, insulin-to-carbohydrate ratio, and basal insulin delivery) (30). In this application, the MPC worked using information from the individual's conventional therapy. Premeal boluses were triggered by the patient, with the carbohydrate amount measured in the clinical research center (CRC) kitchen but automatically calculated by eCTR.
FG003	eCTR Closed-Loop First, Then Open-Loop	Participants completed open-loop admission, then completed eCTR closed-loop admission. The two modules of the eCTR are the SSM and an enhanced range control module based on an MPC algorithm that aims to maintain glycemia in a target range. eCTR also uses insulin-on-board constraints (29) intended to prevent insulin overdose during intensified therapy. The rationale behind MPC was presented in detail in a recent review (7). Controller aggressiveness was individualized for each subject based on readily available patient characteristics (e.g., body weight, insulin-to-carbohydrate ratio, and basal insulin delivery) (30). In this application, the MPC worked using information from the individual's conventional therapy. Premeal boluses were triggered by the patient, with the carbohydrate amount measured in the CRC kitchen but automatically calculated by eCTR.

Baseline Characteristics Module

Arm/Group Information

ID	Title	Description
BG000	Standard Control to Range (sCTR)	The two modules of sCTR are the SSM and a standard range control module that avoids prolonged hyperglycemic excursions. Both modules use a real-time estimate of the patient 's metabolic state based on CGM and insulin infusion data. This estimate is used for prediction of the risks of hypo-and hyperglycemia 30-45 min ahead of the event. If a risk for hypoglycemia is predicted, the SSM attenuates automatically any insulin requests proportionally to the predicted risk level. How aggressively the system attenuates insulin is determined with patient characteristics (e.g., body weight, insulin-to-carbohydrate ratio, and basal insulin delivery). If a risk for hyperglycemia is predicted, the range controller gives a correction bolus using the predicted plasma glucose and the patient's CSII parameters; the system injects only half of the computed bolus and can do so once every hour.

Units	Counts
Participants	BG000

Baseline Measures

Title	Description	Population Description	Parameter Type	Dispersion Type	Unit of Measure	Calculate Percentage	Denominator Units Selected	Denominators	Classes
Age, Categorical			Count of Participants

Outcome Measures Module

Outcome Measures

Type

Title

Description

Population Description

Reporting Status

Anticipated Posting Date

Parameter Type

Dispersion Type

Unit of Measure

Calculate Percentage

Time Frame

Units Analyzed

Denominator Units Selected

Arm/Group Information

Denominators

Classes

Analyses

Primary

Hypoglycemic Events

Number of hypoglycemic events below 70 mg/dL per patient per day

Posted

Mean

Standard Deviation

events/admission per patient

Throughout each 22-hour closed-loop and open-loop admission for sCTR and eCTR

ID	Title	Description
OG000	Open Loop	The subject were in charge of their insulin treatment. Open Loop: This admission was to assess the subjects' level of glucose control and created a base to compare the performance of the closed-loop system. Subjects monitored their own blood glucose values and administer their basal/bolus as they would at home. Subjects use their own pump. Otherwise, the admission remained the same as in the closed-loop admission (i.e. meals, exercise, etc...).
OG001	Closed Loop

Adverse Events Module

Arm/Groups

ID	Title	Description	Deaths (Affected)	Deaths (At Risk)	Serious Events (Affected)	Serious Events (At Risk)	Other Events (Affected)	Other Events (At Risk)
EG000	Open-Loop	This admission was to assess the subjects' level of glucose control and created a base to compare the performance of the closed-loop system. Subjects monitored their own blood glucose values and administer their basal/bolus as they would at home. Subjects use their own pump. Otherwise, the admission remained the same as in the closed-loop admission (i.e. meals, exercise, etc...).

Title	Organization	Phone	Extension	Email
Marc D Breton, Ph.D.	University of Virginia	434-982-6484		mb6nt@virginia.edu

ID	Term
D003922	Diabetes Mellitus, Type 1

ID	Term
D003920	Diabetes Mellitus
D044882	Glucose Metabolism Disorders
D008659	Metabolic Diseases
D009750	Nutritional and Metabolic Diseases

The CLC used a computer to make recommendations for their insulin treatment. This study arm was designed to demonstrate management of glucose using a modular insulin management system based on continuous glucose monitoring and targeted towards the avoidance of hypoglycemic and prolonged hyperglycemic episodes (i.e. control to range). This system was designed to both:

monitor the meal boluses of the patient and correct it in case of observed/predicted under insulinization (avoidance of prolonged hyperglycemia), based on a coarse and subjective knowledge of the meal amount, a precise understanding of the subject's day to day insulin treatment, continuous glucose monitoring, and past insulin injections;
predict and avoid hypoglycemic events, based on continuous glucose reading and past insulin injection.

Closed Loop Control (CLC): During the closed-loop admission, the computer used CGM values to make recommendations of insulin treatment based on the algorithms.