Systematic Evaluation of Continuous Glucose Monitoring Data — SECOND  

Diabetes Mellitus Clinical Trial

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

Status Completed

Clinical Trial 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.

Clinical Trial 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. ;

Related Conditions & MeSH terms

• Diabetes Mellitus

• NCT number: NCT03545178
• Study type: Observational
• Source: University Hospital Inselspital, Berne
• Status: Completed
• Start date: April 1, 2018
• Completion date: July 19, 2019