Outpatient Control-to-Range: System and Monitoring Testing
|Study Design:||Endpoint Classification: Safety/Efficacy Study
Intervention Model: Single Group Assignment
Masking: Open Label
Primary Purpose: Treatment
|Official Title:||Outpatient Control-to-Range: System and Monitoring Testing|
- Percent Time of Active CTR [ Time Frame: 42 hours ] [ Designated as safety issue: Yes ]The main endpoint will be the percent time with all expected data from CGM, pump and patient manual inputs that should be available on Artificial Pancreas platform and monitoring stations. To be considered as successful, this percent time will have to reach more than 80% of total time of investigation for the entire arm.
- Frequency of Unplanned System Resets or Restarts [ Time Frame: 42 hours ] [ Designated as safety issue: No ]
Frequency of unplanned system resets or restarts
Secondary endpoints include the estimation of the failure rates of system components, frequency analysis of lost or inaccurate CGM records, and percent time of active CTR. The failure/missing data records will be compared to failure/missing data records from our past in-clinic studies.
|Study Start Date:||April 2012|
|Study Completion Date:||June 2012|
|Primary Completion Date:||June 2012 (Final data collection date for primary outcome measure)|
Experimental: Outpatient Control-to-Range
Outpatient Control-to-Range: Testing system connectivity
Device: Outpatient Control-to-Range
Subjects will spend two nights (~42 hours) in a local hotel during which the AP Platform will be remotely monitored in an adjacent hotel room for validation that remote system monitoring can successfully occur.
Other Name: Ambulatory Artificial Pancreas
Automated closed-loop control (CLC) of blood glucose, known as "artificial pancreas" (AP) can have tremendous impact on the health and lives of people with type 1 diabetes (T1DM). This inter-institutional and international research team has been on the forefront of CLC developments since the beginning of the Juvenile Diabetes Research Foundation (JDRF) Artificial Pancreas initiative in 2006. Thus far, the investigators have conducted three closed-loop control clinical trials (totaling 60 subjects with T1DM), which demonstrated significantly more time in an acceptable "target" blood glucose range during CLC, and significantly fewer hypoglycemic events during CLC compared to open loop. The overall objective is to sequentially test, validate, obtain regulatory approval for, and deploy at home, a closed-loop Control-to-Range (CTR) system comprised of two algorithmic components: a Safety Supervision Module (SSM) and a Hypoglycemia Mitigation Module (HMM). The SSM will monitor the safety of the subject's continuous subcutaneous insulin infusion pump (CSII) to prevent hypoglycemia and will also monitor the integrity of continuous glucose monitor (CGM) data for signal sensor deviations or loss of sensitivity. The HMM will be responsible for the optimal regulation of postprandial hyperglycemic excursions through correction boluses.
This study will test the ability of AP Platform to (1) run CTR in an outpatient setting, and (2) be remotely monitored. Specifically, this study involves studying adults with T1DM who are experienced insulin pump users. Subjects will spend two nights (~42 hours) in a local hotel, during which the AP Platform will be remotely monitored in an adjacent hotel room for validation that remote system monitoring can successfully occur. During the study, study subjects will be responsible for operating the CTR system with nursing and technicians available for additional support. A study physician and senior engineer will be on call.
Five subjects each will be enrolled at University of Virginia and the University of California, Santa Barbara.
Please refer to this study by its ClinicalTrials.gov identifier: NCT01578980
|United States, California|
|Sansum Diabetes Research Institute|
|Santa Barbara, California, United States, 93105|
|United States, Virginia|
|University of Virginia|
|Charlottesville, Virginia, United States, 22904|
|Study Chair:||Boris P. Kovatchev, Ph.D.||University of Virginia|