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| Name | Class |
|---|---|
| Stanford University | OTHER |
| University of Colorado, Denver | OTHER |
| Harvard University | OTHER |
| University of California, San Diego |
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This is a randomized crossover study testing the efficacy of the Fault Detection algorithms using the Zone MPC algorithm and DiAs artificial pancreas platform in adult patients with type 1 diabetes. The trial will last for 6 weeks for each individual subject, with three weeks using the AP algorithm and three weeks using sensor augmented pump in a randomized order
Investigational Device:
Artificial Pancreas System with Fault Detection Algorithms: Roche Accu-Check Spirit Combo Insulin Pump, Dexcom G4P System with Share, Diabetes Assistant (DiAs) on Android phone, DiAs Web Monitoring (DWM)
- referred to as Remote Monitoring Server, MPC control algorithm, Health Monitoring System (HMS) algorithm. Sensor and infusion set fault detection algorithms will be applied offline with data obtained from server and notifications will be sent to the clinician.
Control Arm:
Sensor-augmented insulin pump therapy: Subject will use their personal insulin pump and Dexcom G4P System with Share.
Primary Objective:
To determine the efficacy of the fault detection algorithm. The primary outcome is based on the amount of time the sensor glucose is >250 mg/dL in the 4 hours preceding detection of the infusion set failure during sensor augmented pump therapy vs. closed-loop control with fault detection alerts.
Secondary Objectives:
To determine the effectiveness of the sensor fault detection algorithm. To determine the efficacy of the Zone MPC controller by evaluating glycemic outcomes
Number of Subjects:
There will be 20 subjects recruited: 10 at Stanford and 10 at Denver (up to 36 subjects will be enrolled to reach 20 subjects completing the study)
Diagnosis and Main Inclusion Criteria:
Adult subjects between 18 and 55 years of age inclusive, diagnosed with type 1 diabetes.
Trial Design:
This outpatient study will be conducted over 6 weeks as shown in the figure below. The 6-week period will consist of two 2-week blocks of prolonged infusion set wear with a 1-week sensor run-in period preceding each block. In each block, subjects will wear an infusion set for up to 7 days. A new infusion set will be inserted at the start of each week in the block. Following enrollment procedures, subjects will be randomized at a ratio of 1:1 to either use the AP system with fault detection algorithms (intervention) or sensor-augmented pump therapy (control) in the first block.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Sensor Augmented Pump (control) | No Intervention | Use sensor augmented pump (SAP) for 3 weeks. | |
| Artificial Pancreas (intervention) | Experimental | Artificial pancreas system (Algorithm + CGM + pump)--use the AP system for 3 weeks which consists of: (1) Fault detection and Zone MPC algorithm housed on the DiAs platform + (2) Roche insulin pump + (3) Dexcom CGM |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Artificial pancreas system (Algorithm + CGM + pump) | Device | The AP system using fault detection algorithms will determine whether insulin infusion problems are occurring and may prevent severe hyperglycemia due to its predictive nature |
| Measure | Description | Time Frame |
|---|---|---|
| Amount of time sensor glucose levels are >250 mg/dl | Amount of time sensor glucose levels are >250 mg/dl in the control arm versus the experimental arm | 4 hours after insulin infusion set failure |
| Measure | Description | Time Frame |
|---|---|---|
| Effectiveness of sensor fault detection algorithm as defined by % of sensor failures caught by the system | Effectiveness of sensor fault detection algorithm as defined by % of sensor failures caught by the system | During 2 week intervention period |
| Mean sensor glucose values |
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Inclusion Criteria:
Exclusion Criteria:
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| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| David Maahs, MD, PhD | Contact | David.Maahs@ucdenver.edu | ||
| Laurel Messer | Contact | Laurel.Messer@ucdenver.edu |
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| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 29390915 | Derived | Howsmon DP, Baysal N, Buckingham BA, Forlenza GP, Ly TT, Maahs DM, Marcal T, Towers L, Mauritzen E, Deshpande S, Huyett LM, Pinsker JE, Gondhalekar R, Doyle FJ 3rd, Dassau E, Hahn J, Bequette BW. Real-Time Detection of Infusion Site Failures in a Closed-Loop Artificial Pancreas. J Diabetes Sci Technol. 2018 May;12(3):599-607. doi: 10.1177/1932296818755173. Epub 2018 Feb 1. | |
| 28584075 |
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| ID | Term |
|---|---|
| D003922 | Diabetes Mellitus, Type 1 |
| ID | Term |
|---|---|
| D003920 | Diabetes Mellitus |
| D044882 | Glucose Metabolism Disorders |
| D008659 | Metabolic Diseases |
| D009750 | Nutritional and Metabolic Diseases |
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| ID | Term |
|---|---|
| D000095583 | Continuous Glucose Monitoring |
| ID | Term |
|---|---|
| D001774 | Blood Chemical Analysis |
| D019963 | Clinical Chemistry Tests |
| D019411 | Clinical Laboratory Techniques |
| D019937 | Diagnostic Techniques and Procedures |
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| OTHER |
| University of California, Santa Barbara | OTHER |
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mean sensor glucose values |
| 2 week intervention period versus 2 week control period |
| Percent of time in range between 70-180 mg/dl | Percent of time in range between 70-180 mg/dl | 2 week intervention period versus 2 week control period |
| Derived |
| Forlenza GP, Deshpande S, Ly TT, Howsmon DP, Cameron F, Baysal N, Mauritzen E, Marcal T, Towers L, Bequette BW, Huyett LM, Pinsker JE, Gondhalekar R, Doyle FJ 3rd, Maahs DM, Buckingham BA, Dassau E. Application of Zone Model Predictive Control Artificial Pancreas During Extended Use of Infusion Set and Sensor: A Randomized Crossover-Controlled Home-Use Trial. Diabetes Care. 2017 Aug;40(8):1096-1102. doi: 10.2337/dc17-0500. Epub 2017 Jun 5. |
| D004700 | Endocrine System Diseases |
| D001327 | Autoimmune Diseases |
| D007154 | Immune System Diseases |
| D003933 | Diagnosis |
| D003940 | Diagnostic Techniques, Endocrine |
| D008991 | Monitoring, Physiologic |
| D008919 | Investigative Techniques |