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NCT Number: NCT00884611

Development of Algorithms for a Hypoglycemic Prevention Alarm: Closed Loop Study

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.

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Key information

Conditions

Type 1 Diabetes Mellitus Autoimmune Diseases Diabetes Mellitus Diabetes Mellitus, Type 1 Endocrine System Diseases Glucose Metabolism Disorders Immune System Diseases Metabolic Diseases Nutritional and Metabolic Diseases

Age range

12 year–46 year

Sex eligibility

All sexes

Study type

Interventional

Phase

Not applicable

Primary location

Stanford University School of Medicine, Stanford, California, United States

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About this study

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.

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

  • 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

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:
  • 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.

Treatment and study plan

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.

Other names: Intervention Night

Predictive Low Glucose Suspend Algorithm OFF

Device

Other names: Control Night

Primary outcomes

  1. Percentage of Nights With CGM (Continuous Glucose Monitor) Sensor Values < 60 mg/dL

    Time frame: 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 outcomes

  1. Percentage of Nights With CGM Values >180 mg/dL

    Time frame: 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.

  2. Mean Morning Blood Glucose (BG)

    Time frame: 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.

Sponsors and collaborators

Lead sponsor

Stanford University

Other

Collaborators

  • University of Colorado, Denver

Registry information

Official study title

Development of Algorithms for a Hypoglycemic Prevention Alarm

Important dates

Study start
2007
Primary completion
2011
Study completion
2011
First posted
Apr 21, 2009
Registry last updated
Feb 28, 2018

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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