Status
Conditions
Treatments
About
Our lab has developed an artificial pancreas system called the McGill Artificial Pancreas (MAP) for automating insulin delivery. Using patient's basal-bolus parameters (basal rates and ICRs), the artificial pancreas involves a control algorithm that modulates insulin infusion based on the sensor readings and meal information. However, because basal-bolus parameters are difficult to optimize, proper glycemic control is not always achieved. Therefore, we have developed a learning algorithm that estimates optimal basal-bolus parameters using data over several days. The algorithm examines daily glucose, insulin, and meal data to make changes in patients' basal rates and ICRs.
The objective of this project is to test our artificial pancreas system with and without the learning algorithm using a randomized crossover design in between 31 and 67 children and adolescents at camp Carowanis. We hypothesize that adding a learning algorithm to the artificial pancreas will improve the performance of our artificial pancreas system by increasing the time spent in target glucose range (4mmol/L - 10mmol/L) compared with the artificial pancreas system alone.
Full description
This is an open-label, randomized, two-way, cross-over study to compare the glucose control between closed-loop strategy with and without a learning module. Children and adolescent type 1 diabetes patients at Camp Carowanis will be enrolled in the study, where they will undergo two randomly ordered interventions:
Enrollment
Sex
Ages
Volunteers
Inclusion criteria
Exclusion criteria
Primary purpose
Allocation
Interventional model
Masking
45 participants in 2 patient groups
Loading...
Data sourced from clinicaltrials.gov
Clinical trials
Research sites
Resources
Legal