University of Virginia Center for Diabetes Technology
Charlottesville, Virginia, 22904, United States
NCT Number: NCT04545567
The study team will be comparing two investigational Artificial Pancreas (AP) systems that the UVA Center for Diabetes Technology has developed. An artificial pancreas system delivers insulin automatically based on a blood glucose level that is provided from a continuous glucose monitor (CGM).
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Notify Me12 year–25 year
All sexes
Interventional
Not applicable
Charlottesville, Virginia, 22904, United States
Maintaining blood glucose (BG) control among adolescents with Type 1 diabetes (T1D) is arguably the greatest challenge in the entire field of T1D. One reason for this poor control relates to missed meal boluses, which affects 65% of adolescents at least once weekly, with 38% missing at least 15% of their boluses. Adolescents who miss four boluses weekly experience an increase of 1% in their HbA1c. While the advent of the artificial pancreas (AP) offers promise of safe reductions in HbA1c, the study team previously found that the AP only partly compensates for missed prandial insulin -demonstrating that some form of meal announcement is necessary for good BG control, even with an AP. One way to automate this process is by sharing the prandial dosing responsibilities between an automated insulin priming (based on continuous glucose monitor condition predictive of a safe situation for such insulin dosing) and a closed loop controller capable of reconstructing (estimating) the prevailing glucose rate of appearance from an unannounced meal. The study team has developed such an insulin priming schema and integrated it into a new version of the robust Model Predictive Controller University of Virginia AP system (called the RocketAP).
In the current study, the investigators are testing this new AP system in two configurations: hybrid and fully automated, among up to 20 adolescents. The primary outcome will be one of efficacy in assessing how well the new system controls post-prandial BG in the absence of carbohydrate (CHO) announcement as compared to the same situation but using the Control-IQ closed loop algorithm, also designed at UVa and using the same modular architecture and safety system, but without insulin priming and with a less advanced model-based controller. Further comparisons will be made to BG control on RocketAP with CHO announcement and on Control-IQ with CHO announcement. Adolescents will be started on the respective University of Virginia AP systems (RocketAP and Control-IQ in random order, both implemented on the DiAs platform, MAF 2109) and followed over the course of two dinners on each of the two platforms: a dinner where CHO is announced as normal and the 2nd where no announcement is made.
The study team hypothesize that performances of RocketAP in fully automated mode will lie in between Hybrid and Fully Automated Control-IQ. In time, this may provide an opportunity to improve BG control among adolescents who miss meal announcement.
Healthy volunteers accepted: No
Only the study team can determine whether someone qualifies for participation.
Inclusion criteria
Exclusion criteria
Artificial pancreas system which is designed to be able to identify when you have eaten and provide insulin for this meal
Artificial pancreas system which responds to glucose fluctuations by modulating insulin delivery but does not specifically detect meal ingestion
Time frame: 6 hours
Percentage of time from dinner time until midnight with blood glucose in range 70-180 mg/dL in the unannounced meal
Time frame: 6 hours
Number of hypoglycemia events from dinner time until midnight
Time frame: 6 hours
Percentage of Time From Dinner Time Until Midnight With Blood Glucose in Range < 70 mg/dL
Time frame: 6 hours
Percentage of Time From Dinner Time Until Midnight With Blood Glucose in Range >180 mg/dL
Time frame: 6 hours
Percentage of Time From Dinner Time Until Midnight With Blood Glucose in Range >250 mg/dL
Time frame: 6 hours
Units of insulin injected from dinner time until midnight
Time frame: 6 hours
The blood glucose area under the curve (AUC) from dinner until midnight, accounting for the initial blood glucose value.
Time frame: 12 hours
Percentage of time from dinner time until dinner time + 12h with blood glucose in range 70-180 mg/dL
Time frame: 12 hours
Number of hypoglycemia events from dinner Time until dinner time + 12h
Time frame: 12 hours
Percentage of time from dinner time until dinner time + 12h with blood Glucose in range <70 mg/dL
Time frame: 12 hours
Percentage of Time From Dinner Time Until Dinner Time + 12h With Blood Glucose >180 mg/dL
Time frame: 12 hours
Percentage of Time From Dinner Time Until Dinner Time + 12h With Blood Glucose >250 mg/dL
Time frame: 12 hours
Units of insulin injected from dinner time until dinner time + 12h
Time frame: 12 hours
The Blood Glucose Area Under the Curve (AUC) From Dinner Until dinner + 12h, Accounting for the Initial Blood Glucose Value
Time frame: 36 hours
Percentage of Time outside the dinner sessions With Blood Glucose in Range 70-180 mg/dL
Time frame: 36 hours
Number of hypoglycemia events outside of the Study Dinner Sessions
Time frame: 36 hours
Percentage of Time outside of the Study Dinner Sessions with Blood Glucose <70 mg/dL
Time frame: 36 hours
Percent of Time Outside of the Study Dinner Sessions with Blood Glucose >180 mg/dL
Time frame: 36 hours
Percent Time Outside of the Study Dinner Sessions with Blood Glucose >250 mg/dL
Time frame: 36 hours
Units of insulin injected outside of the Study Dinner Sessions
University of Virginia
Other
Fully Automated Closed Loop Control in Adolescents With Type 1 Diabetes (Rocket AP)
Acronym: RocketAP
OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.
View the official ClinicalTrials.gov record (opens in a new tab)This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.
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