Systematic Evaluation of Continuous Glucose Monitoring Data

NCT ID: NCT03545178

Last Updated: 2019-08-13

Basic Information

• Recruitment Status: COMPLETED
• Total Enrollment: 384 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2018-04-01
• Study Completion Date: 2019-07-19

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.

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 three days prior to the consultation with the CGM-/FGM data of the days 4-28 prior to the consultation, as well as the last seven days prior to the consultation with 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.

Conditions

Diabetes Mellitus

Study Design

• Observational Model Type: COHORT
• Study Time Perspective: RETROSPECTIVE

Study Groups

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.

glucose control (Substudy A)

Intervention Type: BEHAVIORAL

Comparison of glucose values during days 0 - 3 with days 4 - 28 and 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

hypoglycemia prediction (Substudy B)

Intervention Type: DIAGNOSTIC_TEST

Use of CGM/FGM data to develop and evaluate a neural network based hypoglycemia prediction model

Interventions

glucose control (Substudy A)

Comparison of glucose values during days 0 - 3 with days 4 - 28 and 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

Intervention Type: BEHAVIORAL

hypoglycemia prediction (Substudy B)

Use of CGM/FGM data to develop and evaluate a neural network based hypoglycemia prediction model

Intervention Type: DIAGNOSTIC_TEST

Eligibility 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

• Minimum Eligible Age: 16 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

Insel Gruppe AG, University Hospital Bern

OTHER

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Principal Investigators

Thomas Zueger, MD

Role: PRINCIPAL_INVESTIGATOR

Department of Diabetes, Endocrinology, Clinical Nutrition and Metabolism, Inselspital, Bern University Hospital, University of Bern, 3010 Bern, Switzerland

Christoph Stettler, Prof.

Role: STUDY_DIRECTOR

Department of Diabetes, Endocrinology, Clinical Nutrition and Metabolism, Inselspital, Bern University Hospital, University of Bern, 3010 Bern, Switzerland

Locations

Inselspital, Bern University Hospital, University of Bern

Bern, Canton of Bern, Switzerland

Countries

Switzerland

Other Identifiers

2018-00207

Identifier Type: -

Identifier Source: org_study_id