Diabetes Mellitus, Type 1
Conditions
Keywords
Diabetes Mellitus, Type 1, Artificial Pancreas, Closed-Loop Delivery, Insulin, Adolescent, Missed Bolus
Brief summary
Despite current treatments for type 1 diabetes, maintaining blood glucose levels within a good range is a difficult task. A primary source for poor glucose control in adolescents is skipping insulin boluses at mealtimes. Advances in glucose sensors have motivated the research towards closed-loop delivery systems to automatically regulate glucose levels. Closed-loop delivery (artificial pancreas) is composed of an insulin pump, a continuous glucose sensor and a dosing algorithm that calculates the insulin dose to infuse based on sensor readings. The performance of a closed-loop delivery after a missed bolus may be improved if the computer program that calculates the insulin is enhanced with a meal detection module. The meal detection module will automatically detect the meal (which had no bolus delivered), and signal the delivery of more insulin. The aim of this study is to assess the safety and efficacy of a closed-loop delivery with and without meal detection module compared to conventional pump therapy in regulating post-prandial glycemic levels after omission of a meal bolus. The primary hypothesis is that closed-loop delivery with no meal detection module will reduce the mean increase in postprandial glucose levels after a missed bolus compared to conventional pump therapy.
Detailed description
Despite current treatments for type 1 diabetes, maintaining blood glucose levels within a good range is a difficult task. A primary source for poor glucose control in adolescents is skipping insulin boluses at mealtimes. Advances in glucose sensors have motivated the research towards closed-loop delivery systems to automatically regulate glucose levels. Closed-loop delivery (artificial pancreas) is composed of an insulin pump, a continuous glucose sensor and a dosing algorithm that calculates the insulin dose to infuse based on sensor readings. The performance of a closed-loop delivery after a missed bolus may be improved if the computer program that calculates the insulin is enhanced with a meal detection module. The meal detection module will automatically detect the meal (which had no bolus delivered), and signal the delivery of more insulin. The aim of this study is to conduct a randomized, three-way, cross-over trial to compare the efficacy of closed-loop delivery, closed-loop delivery with a meal detection module, and conventional pump therapy. The study aims to compare these three interventions for 9 hours in adolescents with poorly controlled type 1 diabetes. Each 9 hours will include two meals of different carbohydrate content, of which one will not have a carbohydrate-matched prandial bolus. This study will allow for the assessment of the safety and efficacy of closed-loop delivery with and without a meal detection module compared to conventional pump therapy in regulating post-prandial glycaemia. The aim of this study is to assess the safety and efficacy of a closed-loop delivery with and without meal detection module compared to conventional pump therapy in regulating post-prandial glycemic levels after omission of a meal bolus. The primary hypothesis is that closed-loop delivery with no meal detection module will reduce the mean increase in postprandial glucose levels after a missed bolus compared to conventional pump therapy. The secondary hypotheses are: 1. Closed-loop delivery with meal detection module will reduce the mean increase in postprandial glucose levels after a missed bolus compared to conventional pump therapy. 2. Closed-loop delivery with meal detection module will reduce the mean increase in postprandial glucose levels after a missed bolus compared to closed-loop delivery with no meal detection module.
Interventions
DEVICEClosed Loop Delivery
Insulin will be delivered by subcutaneous insulin infusion pump. Infusion rates will be changed manually every 10 minutes based on the computer generated recommendation infusion rates, calculated from the glucose levels measured by a real time sensor. The computer generated recommendations are based on a predictive algorithm.
DEVICEClosed Loop Delivery with Meal Detection Module
Closed Loop Delivery with Meal Detection Module Insulin will be delivered by subcutaneous insulin infusion pump. Infusion rates will be changed manually every 10 minutes based on the computer generated recommendation infusion rates, calculated from the glucose levels measured by a real time sensor. The computer generated recommendations are based on a predictive algorithm with an overlying meal detection module which detects missed meals and will increase insulin infusion rates based on a predictive meal detection algorithm
DEVICEConventional Pump Therapy
Insulin will be delivered by subcutaneous insulin infusion pump with participants usual infusion rate
Sponsors
McGill University
Study design
Allocation
RANDOMIZED
Intervention model
CROSSOVER
Primary purpose
TREATMENT
Masking
NONE
Eligibility
Sex/Gender
ALL
Age
12 Years to 18 Years
Healthy volunteers
No
Inclusion criteria
1. Clinical diagnosis of type 1 diabetes for at least 12 months. (The diagnosis of type 1 diabetes is based on the investigator's judgment; C peptide level and antibody determinations are not needed.) 2. The participant will have been on insulin pump therapy for at least 3 months. 3. HbA1c 7.5% to 12%. 4. Self-reported or documented history of missed-bolus for meals during the previous 6 months.
Exclusion criteria
1. Clinically significant nephropathy, neuropathy or retinopathy as judged by the investigator. 2. Severe hypoglycemic episode within one month of screening. 3. Pregnancy. 4. Current use of oral glucocorticoid medication (except low stable dose). Stable doses of inhaled steroids are acceptable. 5. Known or suspected allergy to the trial products, including the meal content. 6. Other serious medical illness likely to interfere with study participation or with the ability to complete the trial by the judgment of the investigator. 7. Failure to comply with team's recommendations (e.g. not willing to eat meals/snacks, not willing to change pump parameters, etc.).
Design outcomes
Primary
| Measure | Time frame |
|---|---|
| AUCinc: The incremental area under the curve (as compared to pre-meal glucose value) of the postprandial glucose excursions for the lunch meal. | 0 - 240 min postprandial lunch meal |
Secondary
| Measure | Time frame |
|---|---|
| Percentage of postprandial time of sensor glucose measurements spent: a. <3.9 mmol/L; b. between 3.9 and 7.8 mmol/L; c. between 3.9 and 10.0 mmol/L; d. >10.0 mmol/L; e. >13.9 mmol/L; f. >16.7 mmol/L. | 0 - 240 min postprandial lunch meal |
| Mean sensor glucose concentration. | 9 hours |
| Total insulin delivery | 9 hours |
| Glucose concentration as measured by CGM at 2 hours (120 min) post-meal. | 120 min postprandial lunch meal |
| AUCinc: The incremental area under the curve (as compared to pre-meal glucose value) of the postprandial glucose excursions: a. >10.0 mmol/L; b. >13.9 mmol/L; c. >16.7 mmol/L | 0 - 240 min postprandial lunch meal |
| Incremental postprandial peak of glucose concentration as measured by CGM. | 0 - 240 min postprandial lunch meal |
| Number of hyperglycemic events > 18.0mmol/L. | 0 - 240 min postprandial lunch meal |
| 1. Glucose concentration as measured by CGM at 5 hours (300 min) post-meal. Glucose concentration as measured by CGM at 5 hours (300 min) post-meal | 300 min postprandial lunch meal |
| Incremental glucose concentration as measured by CGM at 2 hours (120 min) post-meal. | 120 min postprandial lunch meal |
Countries
Canada
Outcome results
None listed