Development of Algorithms for a Hypoglycemic Prevention Alarm
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 入组人数
- 20
- 试验地点
- 2
- 主要终点
- Percentage of Nights With CGM (Continuous Glucose Monitor) Sensor Values < 60 mg/dL
研究概览
简要总结
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.
详细描述
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.
研究设计
- 研究类型
- Interventional
- 分配方式
- Na
- 干预模型
- Single Group
- 主要目的
- Treatment
- 盲法
- None
入排标准
- 年龄范围
- 12 Years 至 46 Years（Child, Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •Age 18 years or older,
- •Type 1 diabetes for at least 1 year
- •Current user of the MiniMed Paradigm Real-Time Revel system and Sof-sensor glucose sensor
- •Hemoglobin A1c level of < 8.0%,
- •Home computer with access to the Internet,
- •At least one CGMglucose value < 70 mg/dL during the most recent 15 nights of CGM glucose data.
- •Not pregnant or planning to become pregnant
排除标准
- •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:
- •Asthma if treated with systemic or inhaled corticosteroids in the last 6 months
- •Cystic fibrosis
- •Angina (recurrent heart pain)
- •Past heart attack or coronary artery (heart vessel) disease
- •Past stroke or impairment of blood flow to the brain
- •Other major illness that in the judgment of the investigator might interfere with the completion of the protocol Adequately treated thyroid disease and celiac disease do not exclude subjects from enrollment
- •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.
研究组 & 干预措施
Predictive Low Glucose Suspend
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.
干预措施: Predictive Low Glucose Suspend Algorithm ON (Device)
Predictive Low Glucose Suspend
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.
干预措施: Predictive Low Glucose Suspend Algorithm OFF (Device)
结局指标
主要结局
Percentage of Nights With CGM (Continuous Glucose Monitor) Sensor Values < 60 mg/dL
时间窗: 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.
次要结局
- Percentage of Nights With CGM Values >180 mg/dL(21 days)
- Mean Morning Blood Glucose (BG)(21 days)
研究者
Bruce A. Buckingham
Principle Investigator
Stanford University