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| ID | Type | Description | Link |
|---|---|---|---|
| Stanford eprotocol # 6789 | Other Identifier | Stanford University |
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| Name | Class |
|---|---|
| University of Colorado, Denver | OTHER |
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This research study, Development of Algorithms for a Hypoglycemic Prevention Alarm, is being conducted at Stanford University Medical Center and the University of Colorado Barbara Davis Center. It is paid for by the Juvenile Diabetes Research Foundation.
The purpose of doing this research study is to understand the best way to stop an insulin infusion pump from delivering insulin to prevent a subject from having hypoglycemia. Nocturnal hypoglycemia is a common problem with type 1 diabetes. This is a pilot study to evaluate the safety of a system consisting of an insulin pump and continuous glucose monitor communicating wirelessly with a bedside computer running an algorithm that temporarily suspends insulin delivery when hypoglycemia is predicted in a home setting.
After the run-in phase, there is a 21-night trial in which each night is randomly assigned 2:1 to have either the predictive low-glucose suspend (PLGS) system active (intervention night) or inactive (control night).
Three predictive algorithm versions were studied sequentially during the study.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Predictive Low Glucose Suspend | Experimental | The pump suspension system consists of the Revel CGM device communicating with a laptop computer that contains the hypoglycemia prediction algorithm. During the 21 night study period, the laptop is placed at the bedside and turned on by the participant at bedtime and off on arising in the morning.The laptop contains a randomization schedule (2:1) that indicats whether the hypoglycemia prediction algorithm will be in operation that night (Predictive Low Glucose Suspend Algorithm ON) or will not be activated (Predictive Low Glucose Suspend Algorithm OFF), to which the participant is blinded. |
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| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Predictive Low Glucose Suspend Algorithm ON | Device | The algorithm uses a Kalman filter-based model to predict whether the sensor glucose level will fall below 80 mg/dL within a given time period and suspends the insulin pump if this event is predicted. |
| Measure | Description | Time Frame |
|---|---|---|
| Percentage of Nights With CGM (Continuous Glucose Monitor) Sensor Values < 60 mg/dL | Nights with CGM sensor values < 60 mg/dL were considered to be undesirable. A Kalman filter-based model algorithm predicted whether the sensor glucose level would fall below 80 mg/dL and would suspend insulin delivery as needed. Participants may have received treatment using one or more of the following algorithms: Algorithm 1 had a hypoglycaemic prediction horizon of 70 minutes; algorithm 2: 50 minutes; algorithm 3: 30 minutes. | 21 days |
| Measure | Description | Time Frame |
|---|---|---|
| Percentage of Nights With CGM Values >180 mg/dL | Nights with CGM sensor values >180 mg/dL were considered to be undesirable. Participants may have received treatment using one or more of the following algorithms: Algorithm 1 had a hypoglycaemic prediction horizon of 70 minutes; algorithm 2: 50 minutes; algorithm 3: 30 minutes. | 21 days |
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Inclusion Criteria:
Exclusion Criteria:
The exclusion criteria for this study is the following:
The presence of a significant medical disorder that in the judgment of the investigator will affect the wearing of the sensors or the completion of any aspect of the protocol
The presence of any of the following diseases:
Inpatient psychiatric treatment in the past 6 months for either the subject or the subject's primary care giver (i.e., parent or guardian)
Current use of oral/inhaled glucocorticoids or other medications, which in the judgment of the investigator would be a contraindication to participation in the study
Severe hypoglycemic event, as described as a seizure, loss of consciousness, severe neurological impairment, or neurological impairment suggestive of hypoglycemia and requiring an emergency department visit or hospitalization within 18 months of enrollment.
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| Name | Affiliation | Role |
|---|---|---|
| Bruce A. Buckingham | Stanford University | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Stanford University School of Medicine | Stanford | California | 94305 | United States | ||
| Barbara Davis Center for Childhood Diabetes, University of Colorado |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 23883408 | Result | Buckingham BA, Cameron F, Calhoun P, Maahs DM, Wilson DM, Chase HP, Bequette BW, Lum J, Sibayan J, Beck RW, Kollman C. Outpatient safety assessment of an in-home predictive low-glucose suspend system with type 1 diabetes subjects at elevated risk of nocturnal hypoglycemia. Diabetes Technol Ther. 2013 Aug;15(8):622-7. doi: 10.1089/dia.2013.0040. Epub 2013 Jul 24. |
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| ID | Title | Description |
|---|---|---|
| FG000 | Predictive Suspend | Participants had continuous glucose monitoring (CGM) using a glucose sensor and received insulin from an insulin pump during sleep. On intervention nights, participants received insulin uising an algorithm that allowed a computer to assess the data received from the CGM sensor and suspend insulin delivery to avoid potential hypoglycaemia. On control night, participants received insulin delivery as normally received by the insulin pump. Participants had 2 intervention nights to each control night. |
| Title | Milestones | Reasons Not Completed | |||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Overall Study |
|
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| ID | Title | Description |
|---|---|---|
| BG000 | Predictive Suspend | Participants had continuous glucose monitoring (CGM) using a glucose sensor and received insulin from an insulin pump during sleep. On intervention nights, participants received insulin uising an algorithm that allowed a computer to assess the data received from the CGM sensor and suspend insulin delivery to avoid potential hypoglycaemia. On control night, participants received insulin delivery as normally received by the insulin pump. Participants had 2 intervention nights to each control night. |
| Units | Counts |
|---|---|
| Participants |
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| Title | Description | Population Description | Parameter Type | Dispersion Type | Unit of Measure | Calculate Percentage | Denominator Units Selected | Denominators | Classes |
|---|---|---|---|---|---|---|---|---|---|
| Age, Categorical | Count of Participants |
| Type | Title | Description | Population Description | Reporting Status | Anticipated Posting Date | Parameter Type | Dispersion Type | Unit of Measure | Calculate Percentage | Time Frame | Units Analyzed | Denominator Units Selected | Arm/Group Information | Denominators | Classes | Analyses | |||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Primary | Percentage of Nights With CGM (Continuous Glucose Monitor) Sensor Values < 60 mg/dL | Nights with CGM sensor values < 60 mg/dL were considered to be undesirable. A Kalman filter-based model algorithm predicted whether the sensor glucose level would fall below 80 mg/dL and would suspend insulin delivery as needed. Participants may have received treatment using one or more of the following algorithms: Algorithm 1 had a hypoglycaemic prediction horizon of 70 minutes; algorithm 2: 50 minutes; algorithm 3: 30 minutes. | Participants who were treated and had data for the respective algorithm were included in the analysis. | Posted | Number | percentage of nights | 21 days | Nights | Nights |
|
21 days
Only treated participants were evaluable for adverse events.
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| ID | Title | Description | Deaths (Affected) | Deaths (At Risk) | Serious Events (Affected) | Serious Events (At Risk) | Other Events (Affected) | Other Events (At Risk) |
|---|---|---|---|---|---|---|---|---|
| EG000 | Control Night (PLGS Algorithm OFF) | Participants had continuous glucose monitoring (CGM) using a glucose sensor and received insulin from an insulin pump during sleep. On intervention nights, participants received insulin uising an algorithm that allowed a computer to assess the data received from the CGM sensor and suspend insulin delivery to avoid potential hypoglycaemia. On control night, participants received insulin delivery as normally received by the insulin pump. Participants had 2 intervention nights to each control night. Data for control night are reported in this group. |
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| Title | Organization | Phone | Extension | |
|---|---|---|---|---|
| Dr. Bruce Buckingham, MD | Stanford University | buckingham@stanford.edu |
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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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|
| Predictive Low Glucose Suspend Algorithm OFF | Device |
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| Mean Morning Blood Glucose (BG) |
Desirable glucose level was 70-180 mg/mL. Average of all morning BG data is presented. Participants may have received treatment using one or more of the following algorithms: Algorithm 1 had a hypoglycaemic prediction horizon of 70 minutes; algorithm 2: 50 minutes; algorithm 3: 30 minutes. |
| 21 days |
| Aurora |
| Colorado |
| 80045 |
| United States |
| Participants |
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| Sex: Female, Male | Count of Participants | Participants |
|
| OG001 | Algorithm 1 - Intervention Nights | Algorithm 1 had a hypoglycaemic prediction horizon of 70 minutes. Participants had continuous glucose monitoring (CGM) using a glucose sensor and received insulin from an insulin pump during sleep. On intervention nights, participants received insulin uising an algorithm that allowed a computer to assess the data received from the CGM sensor and suspend insulin delivery to avoid potential hypoglycaemia. On control night, participants received insulin delivery as normally received by the insulin pump. Participants had 2 intervention nights to each control night. |
| OG002 | Algorithm 2 - Control Nights | Algorithm 2 had a hypoglycaemic prediction horizon of 50 minutes. Participants had continuous glucose monitoring (CGM) using a glucose sensor and received insulin from an insulin pump during sleep. On intervention nights, participants received insulin uising an algorithm that allowed a computer to assess the data received from the CGM sensor and suspend insulin delivery to avoid potential hypoglycaemia. On control night, participants received insulin delivery as normally received by the insulin pump. Participants had 2 intervention nights to each control night. |
| OG003 | Algorithm 2 - Intervention Nights | Algorithm 2 had a hypoglycaemic prediction horizon of 50 minutes. Participants had continuous glucose monitoring (CGM) using a glucose sensor and received insulin from an insulin pump during sleep. On intervention nights, participants received insulin uising an algorithm that allowed a computer to assess the data received from the CGM sensor and suspend insulin delivery to avoid potential hypoglycaemia. On control night, participants received insulin delivery as normally received by the insulin pump. Participants had 2 intervention nights to each control night. |
| OG004 | Algorithm 3 - Control Nights | Algorithm 3 had a hypoglycaemic prediction horizon of 30 minutes. Participants had continuous glucose monitoring (CGM) using a glucose sensor and received insulin from an insulin pump during sleep. On intervention nights, participants received insulin uising an algorithm that allowed a computer to assess the data received from the CGM sensor and suspend insulin delivery to avoid potential hypoglycaemia. On control night, participants received insulin delivery as normally received by the insulin pump. Participants had 2 intervention nights to each control night. |
| OG005 | Algorithm 3 - Intervention Nights | Algorithm 3 had a hypoglycaemic prediction horizon of 30 minutes. Participants had continuous glucose monitoring (CGM) using a glucose sensor and received insulin from an insulin pump during sleep. On intervention nights, participants received insulin uising an algorithm that allowed a computer to assess the data received from the CGM sensor and suspend insulin delivery to avoid potential hypoglycaemia. On control night, participants received insulin delivery as normally received by the insulin pump. Participants had 2 intervention nights to each control night. |
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| Secondary | Percentage of Nights With CGM Values >180 mg/dL | Nights with CGM sensor values >180 mg/dL were considered to be undesirable. Participants may have received treatment using one or more of the following algorithms: Algorithm 1 had a hypoglycaemic prediction horizon of 70 minutes; algorithm 2: 50 minutes; algorithm 3: 30 minutes. | Participants who were treated and had data for the respective algorithm were included in the analysis. | Posted | Number | percentage of nights | 21 days | Nights | Nights |
|
|
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| Secondary | Mean Morning Blood Glucose (BG) | Desirable glucose level was 70-180 mg/mL. Average of all morning BG data is presented. Participants may have received treatment using one or more of the following algorithms: Algorithm 1 had a hypoglycaemic prediction horizon of 70 minutes; algorithm 2: 50 minutes; algorithm 3: 30 minutes. | Participants who were treated and had data for the respective algorithm were included in the analysis. | Posted | Mean | Standard Deviation | mg/dL | 21 days |
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| 0 |
| 19 |
| 0 |
| 19 |
| EG001 | Intervention Night (PLGS Algorithm ON) | Participants had continuous glucose monitoring (CGM) using a glucose sensor and received insulin from an insulin pump during sleep. On intervention nights, participants received insulin uising an algorithm that allowed a computer to assess the data received from the CGM sensor and suspend insulin delivery to avoid potential hypoglycaemia. On control night, participants received insulin delivery as normally received by the insulin pump. Participants had 2 intervention nights to each control night. Data for intervention nights are reported in this group. | 0 | 19 | 0 | 19 |
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| D004700 | Endocrine System Diseases |
| D001327 | Autoimmune Diseases |
| D007154 | Immune System Diseases |
| Nights |
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