Type 1 Diabetes Mellitus
Conditions
Brief summary
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.
Detailed description
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.
Interventions
DEVICEPredictive Low Glucose Suspend Algorithm ON
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.
DEVICEPredictive Low Glucose Suspend Algorithm OFF
Sponsors
University of Colorado, Denver
Stanford University
Study design
Allocation
NA
Intervention model
SINGLE_GROUP
Primary purpose
TREATMENT
Masking
NONE
Eligibility
Sex/Gender
ALL
Age
12 Years to 46 Years
Healthy volunteers
No
Inclusion criteria
1. Age 18 years or older, 2. Type 1 diabetes for at least 1 year 3. Current user of the MiniMed Paradigm Real-Time Revel system and Sof-sensor glucose sensor 4. Hemoglobin A1c level of \< 8.0%, 5. Home computer with access to the Internet, 6. At least one CGMglucose value \< 70 mg/dL during the most recent 15 nights of CGM glucose data. 7. Not pregnant or planning to become pregnant
Exclusion criteria
The
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Percentage of Nights With CGM (Continuous Glucose Monitor) Sensor Values < 60 mg/dL | 21 days | 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. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Percentage of Nights With CGM Values >180 mg/dL | 21 days | 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. |
| Mean Morning Blood Glucose (BG) | 21 days | 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. |
Countries
United States
Participant flow
Participants by arm
| Arm | Count |
|---|---|
| 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. | 20 |
| Total | 20 |
Baseline characteristics
| Characteristic | Predictive Suspend |
|---|---|
| Age, Categorical <=18 years | 0 Participants |
| Age, Categorical >=65 years | 0 Participants |
| Age, Categorical Between 18 and 65 years | 20 Participants |
| Sex: Female, Male Female | 9 Participants |
| Sex: Female, Male Male | 11 Participants |
Adverse events
| Event type | EG000 affected / at risk | EG001 affected / at risk |
|---|---|---|
| deaths Total, all-cause mortality | — / — | — / — |
| other Total, other adverse events | 0 / 19 | 0 / 19 |
| serious Total, serious adverse events | 0 / 19 | 0 / 19 |
Outcome results
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.
Time frame: 21 days
Population: Participants who were treated and had data for the respective algorithm were included in the analysis.
| Arm | Measure | Value (NUMBER) |
|---|---|---|
| Algorithm 1 - Control Nights | Percentage of Nights With CGM (Continuous Glucose Monitor) Sensor Values < 60 mg/dL | 24 percentage of nights |
| Algorithm 1 - Intervention Nights | Percentage of Nights With CGM (Continuous Glucose Monitor) Sensor Values < 60 mg/dL | 12 percentage of nights |
| Algorithm 2 - Control Nights | Percentage of Nights With CGM (Continuous Glucose Monitor) Sensor Values < 60 mg/dL | 25 percentage of nights |
| Algorithm 2 - Intervention Nights | Percentage of Nights With CGM (Continuous Glucose Monitor) Sensor Values < 60 mg/dL | 11 percentage of nights |
| Algorithm 3 - Control Nights | Percentage of Nights With CGM (Continuous Glucose Monitor) Sensor Values < 60 mg/dL | 22 percentage of nights |
| Algorithm 3 - Intervention Nights | Percentage of Nights With CGM (Continuous Glucose Monitor) Sensor Values < 60 mg/dL | 8 percentage of nights |
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.
Time frame: 21 days
Population: Participants who were treated and had data for the respective algorithm were included in the analysis.
| Arm | Measure | Value (MEAN) | Dispersion |
|---|---|---|---|
| Algorithm 1 - Control Nights | Mean Morning Blood Glucose (BG) | 125 mg/dL | Standard Deviation 53 |
| Algorithm 1 - Intervention Nights | Mean Morning Blood Glucose (BG) | 158 mg/dL | Standard Deviation 52 |
| Algorithm 2 - Control Nights | Mean Morning Blood Glucose (BG) | 138 mg/dL | Standard Deviation 63 |
| Algorithm 2 - Intervention Nights | Mean Morning Blood Glucose (BG) | 151 mg/dL | Standard Deviation 57 |
| Algorithm 3 - Control Nights | Mean Morning Blood Glucose (BG) | 133 mg/dL | Standard Deviation 57 |
| Algorithm 3 - Intervention Nights | Mean Morning Blood Glucose (BG) | 144 mg/dL | Standard Deviation 48 |
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.
Time frame: 21 days
Population: Participants who were treated and had data for the respective algorithm were included in the analysis.
| Arm | Measure | Value (NUMBER) |
|---|---|---|
| Algorithm 1 - Control Nights | Percentage of Nights With CGM Values >180 mg/dL | 63 percentage of nights |
| Algorithm 1 - Intervention Nights | Percentage of Nights With CGM Values >180 mg/dL | 78 percentage of nights |
| Algorithm 2 - Control Nights | Percentage of Nights With CGM Values >180 mg/dL | 29 percentage of nights |
| Algorithm 2 - Intervention Nights | Percentage of Nights With CGM Values >180 mg/dL | 56 percentage of nights |
| Algorithm 3 - Control Nights | Percentage of Nights With CGM Values >180 mg/dL | 49 percentage of nights |
| Algorithm 3 - Intervention Nights | Percentage of Nights With CGM Values >180 mg/dL | 60 percentage of nights |