Systematic Evaluation of Continuous Glucose Monitoring Data

Study Overview
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Brief Summary
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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
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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.

Official Title
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Systematic Evaluation of Continuous Glucose Monitoring Data to for the Development of Clinical Solutions

Conditions
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Diabetes Mellitus

Intervention / Treatment
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* Behavioral: glucose control (Substudy A)
* Diagnostic Test: hypoglycemia prediction (Substudy B)


Participation Criteria
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Eligibility Criteria
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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

Ages Eligible for Study
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Minimum Age: 16 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Diabetic patients using CGM/FGM<br>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)<br>* 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<br>Diagnostic Test: hypoglycemia prediction (Substudy B)<br>* Use of CGM/FGM data to develop and evaluate a neural network based hypoglycemia prediction model<br>|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Change of time in target glucose range day 0-3 compared to day 4-28 and day 0-7 compared to day 8-28 prior to consultation (for Substudy A) | The time spent in the target glucose range from 3.9 to 10.0 mmol/l assessed by CGM/FGM. | 01.01.2013 - 31.07.2018; outcome assessed at study end | 
| Hypoglycemia prediction (for Substudy B) | 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? | 01.01.2013 - 31.07.2018; outcome assessed at study end | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Change of time above and below glucose target range day 0-3 compared to day 4-28 and day 0-7 compared to day 8-28 prior to consultation (for Substudy A) | The time spent above and below the target glucose (3.9 to 10.0 mmol/l) assessed by CGM/FGM. | 01.01.2013 - 31.07.2018; outcome assessed at study end | 
| Change of average and standard deviation glucose day 0-3 compared to day 4-28 and day 0-7 compared to day 8-28 prior to consultation (for Substudy A) | Average and standard deviation glucose levels based on CGM/FGM data | 01.01.2013 - 31.07.2018; outcome assessed at study end | 
| Sensor wearing time day 0-3 compared to day 4-28 and day 0-7 compared to day 8-28 prior to consultation (for Substudy A) | Time CGM-/FGM sensor has been worn (%) | 01.01.2013 - 31.07.2018; outcome assessed at study end | 
| Change of coefficient of variation (CV) day 0-3 compared to day 4-28 and day 0-7 compared to day 8-28 prior to consultation (for Substudy A) | Coefficient of variation (CV) based on CGM/FGM data | 01.01.2013 - 31.07.2018; outcome assessed at study end | 
| Change of time in hypoglycemia day 0-3 compared to day 4-28 and day 0-7 compared to day 8-28 prior to consultation (for Substudy A) | The time with glucose levels < 3.0 based on CGM/FGM data | 01.01.2013 - 31.07.2018; outcome assessed at study end | 
| Change of time in hyperglycemia day 0-3 compared to day 4-28 and day 0-7 compared to day 8-28 prior to consultation (for Substudy A) | The time with glucose levels in the significant hyperglycaemia, as based on CGM/FGM (glucose levels > 13.9 mmol/l) | 01.01.2013 - 31.07.2018; outcome assessed at study end | 
| Change of mean amplitude of glucose excursion (MAGE) day 0-3 compared to day 4-28 and day 0-7 compared to day 8-28 prior to consultation (for Substudy A) | The mean amplitude of glucose excursion assessed by CGM/FGM | 01.01.2013 - 31.07.2018; outcome assessed at study end | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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white coat adherence, continuous glucose monitoring, flash glucose monitoring, hypoglycemia prediction, deep learning algorithm