NCT03545178

Systematic Evaluation of Continuous Glucose Monitoring Data

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

• Status: Completed
• Phase: —
• Study type: Observational
• Enrollment: 384 (actual)
• Sponsor: Insel Gruppe AG, University Hospital Bern · Academic / Other
• Sex: All
• Age: 16 Years
• Healthy volunteers: Not accepted

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

Interventions

Type	Name	Description
BEHAVIORAL	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
DIAGNOSTIC_TEST	hypoglycemia prediction (Substudy B)	Use of CGM/FGM data to develop and evaluate a neural network based hypoglycemia prediction model

Timeline

• Start date: 2018-04-01
• Primary completion: 2019-07-19
• Completion: 2019-07-19
• First posted: 2018-06-04
• Last updated: 2019-08-13

Locations

1 site across 1 country: Switzerland