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Systematic Evaluation of Continuous Glucose Monitoring Data (SECOND)

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ClinicalTrials.gov Identifier: NCT03545178
Recruitment Status : Recruiting
First Posted : June 4, 2018
Last Update Posted : June 4, 2018
Sponsor:
Information provided by (Responsible Party):
University Hospital Inselspital, Berne

Brief Summary:
This study retrospectively evaluates continuous glucose monitoring (CGM) and flash glucose monitoring (FGM) data and pursues two main objectives: First, the investigators analyze if glucose values are better controlled in the days directly before a consultation at our tertiary referral centre (so called "white coat adherence"). Second, the investigators use the collected CGM and FGM data to develop a hypoglycemia prediction model.

Condition or disease Intervention/treatment
Diabetes Mellitus Behavioral: glucose control (Substudy A) Diagnostic Test: hypoglycemia prediction (Substudy B)

Detailed Description:

Substudy A.) Presence of white coat adherence in diabetic patients:

The investigators aim at evaluating the existence of a so called "white coat adherence" with regard to diabetes control, which means that blood-glucose is better controlled in the days immediately prior to a consultation at the diabetes clinic compared to the time-period further back. To analyse this phenomenon, the investigators use continuous glucose monitoring (CGM) and flash glucose monitoring (FGM) of diabetic patients and compare CGM-/FGM data of the last seven days prior to the consultation with the CGM-/FGM data of days 8-28 prior to the consultation.

Substudy B.) Retrospective data collection for the development and evaluation of a hypoglycemia prediction model:

Scope of the study is to use retrospective data for training and evaluation of a deep recurrent neural network based system for predicting the onset of hypoglycemic event at least 20 min ahead in time. The study aims to: I, assess the ability of deep learning algorithm to predict hypoglycemic events using the data collected during substudy 1. II, assess the ability of global model to be personalized using the data collected during sub-study 1. III, investigate the amount of "history" to be involved to achieve maximum performance in terms of prediction ability. IV, develop a global model, which can be easily further personalized to achieve optimum prediction performance per patient.


Study Type : Observational
Estimated Enrollment : 130 participants
Observational Model: Cohort
Time Perspective: Retrospective
Official Title: Systematic Evaluation of Continuous Glucose Monitoring Data to for the Development of Clinical Solutions
Actual Study Start Date : April 1, 2018
Estimated Primary Completion Date : October 1, 2018
Estimated Study Completion Date : December 1, 2018

Resource links provided by the National Library of Medicine

MedlinePlus related topics: Hypoglycemia
Drug Information available for: Dextrose

Group/Cohort Intervention/treatment
Diabetic patients using CGM/FGM
Evaluation of glucose control and application of hypoglycemia prediction models in diabetic patients wearing CGM and/or FGM devices for at least 50% of the time during the last 4 weeks prior to the medical consultation.
Behavioral: glucose control (Substudy A)
Comparison of glucose values during days 0 - 7 with days 8 - 28 before a medical consultation at the diabetes clinic in patients suffering from diabetes and wearing a continuous glucose monitoring and/or flash glucose monitoring device

Diagnostic Test: hypoglycemia prediction (Substudy B)
Use of CGM/FGM data to develop and evaluate a neural network based hypoglycemia prediction model




Primary Outcome Measures :
  1. Change of time in target glucose range day 0-7 compared to day 8-28 prior to consultation (for Substudy A) [ Time Frame: 01.01.2013 - 31.07.2018; outcome assessed at study end ]
    The time spent in the target glucose range from 3.9 to 10.0 mmol/l assessed by CGM/FGM.

  2. Hypoglycemia prediction (for Substudy B) [ Time Frame: 01.01.2013 - 31.07.2018; outcome assessed at study end ]
    Proportion of times a deep learning based algorithm can predict a hypoglycemic event (BG <4.0 mmol/l) at least 20 min ahead in time?


Secondary Outcome Measures :
  1. Change of time above and below glucose target range day 0-7 compared to day 8-28 prior to consultation (for Substudy A) [ Time Frame: 01.01.2013 - 31.07.2018; outcome assessed at study end ]
    The time spent above and below the target glucose (3.9 to 10.0 mmol/l) assessed by CGM/FGM.

  2. Change of average and standard deviation glucose day 0-7 compared to day 8-28 prior to consultation (for Substudy A) [ Time Frame: 01.01.2013 - 31.07.2018; outcome assessed at study end ]
    Average and standard deviation glucose levels based on CGM/FGM data

  3. Change of time in hypoglycemia day 0-7 compared to day 8-28 prior to consultation (for Substudy A) [ Time Frame: 01.01.2013 - 31.07.2018; outcome assessed at study end ]
    The time with glucose levels < 3.5 mmol/l and <2.8 mmol/l based on CGM/FGM data

  4. Change of time in significant hyperglycemia day 0-7 compared to day 8-28 prior to consultation (for Substudy A) [ Time Frame: 01.01.2013 - 31.07.2018; outcome assessed at study end ]
    The time with glucose levels in the significant hyperglycaemia, as based on CGM/FGM (glucose levels > 16.7 mmol/l)

  5. Change of mean amplitude of glucose excursion (MAGE) day 0-7 compared to day 8-28 prior to consultation (for Substudy A) [ Time Frame: 01.01.2013 - 31.07.2018; outcome assessed at study end ]
    The mean amplitude of glucose excursion assessed by CGM/FGM


Other Outcome Measures:
  1. Change of total, basal and bolus insulin dose day 0-7 compared to day 8-28 prior to consultation (for Substudy A) [ Time Frame: 01.01.2013 - 31.07.2018; outcome assessed at study end ]
    Total, basal and bolus insulin dose based on data of continuous subcutaneous insulin infusion data in patients treated with insulin pumps

  2. Change of periods with glucose below 3.5mmol/l for at least 20 minutes day 0-7 compared to day 8-28 prior to consultation (for Substudy A) [ Time Frame: 01.01.2013 - 31.07.2018; outcome assessed at study end ]
    Duration of periods when sensor glucose values was below 3.5mmol/l for at least 20 minutes

  3. Change of periods with glucose above 16.7mmol/l for at least 20 minutes day 0-7 compared to day 8-28 prior to consultation (for Substudy A) [ Time Frame: 01.01.2013 - 31.07.2018; outcome assessed at study end ]
    Duration of periods when sensor glucose values was above 16.7mmol/l for at least 20 minutes

  4. Change of Low Blood Glucose Index (LBGI) day 0-7 compared to day 8-28 prior to consultation (for Substudy A) [ Time Frame: 01.01.2013 - 31.07.2018; outcome assessed at study end ]
    Low Blood Glucose Index (LBGI) based on CGM/FGM data



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Ages Eligible for Study:   16 Years and older   (Child, Adult, Older Adult)
Sexes Eligible for Study:   All
Accepts Healthy Volunteers:   No
Sampling Method:   Non-Probability Sample
Study Population
All patients suffering from diabetes mellitus controlled at our tertiary referral centre using a CGM/FMG device for at least 50% of the time
Criteria

Inclusion Criteria:

  • Diabetes mellitus
  • CGM and/or FGM available for at least 50% of the time in last 4 weeks before consultation
  • Written informed general consent for the retrospective analysis of data

Exclusion Criteria:

  • Pregnancy

Information from the National Library of Medicine

To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.

Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT03545178


Contacts
Contact: Thomas Zueger, MD +41316324070 thomas.zueger@insel.ch

Locations
Switzerland
Inselspital, Bern University Hospital, University of Bern Recruiting
Bern, BE, Switzerland, 3010
Contact: Thomas Züger, MD    +41316324070    thomas.zueger@insel.ch   
Sponsors and Collaborators
University Hospital Inselspital, Berne
Investigators
Principal Investigator: Thomas Zueger, MD Department of Diabetes, Endocrinology, Clinical Nutrition and Metabolism, Inselspital, Bern University Hospital, University of Bern, 3010 Bern, Switzerland
Study Director: Christoph Stettler, Prof. Department of Diabetes, Endocrinology, Clinical Nutrition and Metabolism, Inselspital, Bern University Hospital, University of Bern, 3010 Bern, Switzerland

Responsible Party: University Hospital Inselspital, Berne
ClinicalTrials.gov Identifier: NCT03545178     History of Changes
Other Study ID Numbers: 2018-00207
First Posted: June 4, 2018    Key Record Dates
Last Update Posted: June 4, 2018
Last Verified: May 2018
Individual Participant Data (IPD) Sharing Statement:
Plan to Share IPD: Undecided

Studies a U.S. FDA-regulated Drug Product: No
Studies a U.S. FDA-regulated Device Product: No

Keywords provided by University Hospital Inselspital, Berne:
white coat adherence
continuous glucose monitoring
flash glucose monitoring
hypoglycemia prediction
deep learning algorithm

Additional relevant MeSH terms:
Diabetes Mellitus
Glucose Metabolism Disorders
Metabolic Diseases
Endocrine System Diseases