Trial Outcomes & Findings for Feasibility Study of a Modular Control to Range System in Type 1 Diabetes Mellitus (NCT NCT01418703)
NCT ID: NCT01418703
Last Updated: 2014-11-13
Results Overview
Number of hypoglycemic events below 70 mg/dL per patient per day
Recruitment status
COMPLETED
Study phase
NA
Target enrollment
38 participants
Primary outcome timeframe
Throughout each 22-hour closed-loop and open-loop admission for sCTR and eCTR
Results posted on
2014-11-13
Participant Flow
Participant milestones
| Measure |
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.
|
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.
|
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.
|
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.
|
|---|---|---|---|---|
|
Overall Study
STARTED
|
13
|
13
|
6
|
6
|
|
Overall Study
COMPLETED
|
13
|
13
|
6
|
6
|
|
Overall Study
NOT COMPLETED
|
0
|
0
|
0
|
0
|
Reasons for withdrawal
Withdrawal data not reported
Baseline Characteristics
Feasibility Study of a Modular Control to Range System in Type 1 Diabetes Mellitus
Baseline characteristics by cohort
| Measure |
Standard Control to Range (sCTR)
n=26 Participants
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.
|
Enhanced Control to Range (eCTR)
n=12 Participants
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.
|
Total
n=38 Participants
Total of all reporting groups
|
|---|---|---|---|
|
Age, Categorical
<=18 years
|
11 Participants
n=5 Participants
|
0 Participants
n=7 Participants
|
11 Participants
n=5 Participants
|
|
Age, Categorical
Between 18 and 65 years
|
15 Participants
n=5 Participants
|
12 Participants
n=7 Participants
|
27 Participants
n=5 Participants
|
|
Age, Categorical
>=65 years
|
0 Participants
n=5 Participants
|
0 Participants
n=7 Participants
|
0 Participants
n=5 Participants
|
|
Sex: Female, Male
Female
|
11 Participants
n=5 Participants
|
4 Participants
n=7 Participants
|
15 Participants
n=5 Participants
|
|
Sex: Female, Male
Male
|
15 Participants
n=5 Participants
|
8 Participants
n=7 Participants
|
23 Participants
n=5 Participants
|
PRIMARY outcome
Timeframe: Throughout each 22-hour closed-loop and open-loop admission for sCTR and eCTRNumber of hypoglycemic events below 70 mg/dL per patient per day
Outcome measures
| Measure |
Open Loop
n=38 Participants
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...).
|
Closed Loop
n=38 Participants
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.
|
|---|---|---|
|
Hypoglycemic Events
sCTR (N=26)
|
1.08 events/admission per patient
Standard Deviation 0.27
|
0.4 events/admission per patient
Standard Deviation 0.13
|
|
Hypoglycemic Events
eCTR (N=12)
|
1.4 events/admission per patient
Standard Deviation 0.56
|
1.6 events/admission per patient
Standard Deviation 0.68
|
SECONDARY outcome
Timeframe: Throughout each 22-hour closed-loop and open-loop admission for sCTR and eCTRComparison of time spent in near normoglycemia (3.9 to 10 mmol/mL) in open-loop vs closed-loop sCTR and eCTR.
Outcome measures
| Measure |
Open Loop
n=38 Participants
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...).
|
Closed Loop
n=38 Participants
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.
|
|---|---|---|
|
Percent Time Spent in Near Normoglycemia
sCTR (N=26)
|
61.5 percentage of time
Standard Deviation 5.2
|
74.4 percentage of time
Standard Deviation 3.9
|
|
Percent Time Spent in Near Normoglycemia
eCTR (N=12)
|
76.8 percentage of time
Standard Deviation 5.0
|
90.1 percentage of time
Standard Deviation 3.4
|
SECONDARY outcome
Timeframe: Throughout each 22-hour closed-loop and open-loop admission for sCTR and eCTRAverage plasma glucose concentration in mg/dl
Outcome measures
| Measure |
Open Loop
n=38 Participants
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...).
|
Closed Loop
n=38 Participants
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.
|
|---|---|---|
|
Mean Glucose
sCTR (N=26)
|
8.82 mg/dL
Standard Deviation 0.54
|
8.34 mg/dL
Standard Deviation 0.28
|
|
Mean Glucose
eCTR (N=12)
|
7.74 mg/dL
Standard Deviation 0.44
|
6.68 mg/dL
Standard Deviation 0.28
|
Adverse Events
Open-Loop
Serious events: 0 serious events
Other events: 2 other events
Deaths: 0 deaths
sCTR Closed-Loop Control
Serious events: 0 serious events
Other events: 2 other events
Deaths: 0 deaths
eCTR Closed-Loop Control
Serious events: 0 serious events
Other events: 0 other events
Deaths: 0 deaths
Serious adverse events
Adverse event data not reported
Other adverse events
| Measure |
Open-Loop
n=38 participants at risk
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...).
|
sCTR Closed-Loop Control
n=26 participants at risk
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.
|
eCTR Closed-Loop Control
n=12 participants at risk
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.
|
|---|---|---|---|
|
Endocrine disorders
Hyperglycemia
|
0.00%
0/38
|
7.7%
2/26 • Number of events 2
|
0.00%
0/12
|
|
Skin and subcutaneous tissue disorders
Ecchymosis
|
2.6%
1/38 • Number of events 1
|
0.00%
0/26
|
0.00%
0/12
|
|
Nervous system disorders
Vasovagal Episode
|
2.6%
1/38 • Number of events 1
|
0.00%
0/26
|
0.00%
0/12
|
Additional Information
Results disclosure agreements
- Principal investigator is a sponsor employee
- Publication restrictions are in place