Analysis of "Simple Post-Processing of Continuous Glucose Monitoring Measurements Improves Endpoints in Clinical Trials".
Günther Schmelzeisen-RedekerPublished in: Journal of diabetes science and technology (2019)
Jensen et al used continuous glucose monitoring (CGM) data of the Dexcom G4 Platinum (DG4P) sensor obtained in a clinical efficacy and safety study of Novo Nordisk's new insulin Fiasp® to calculate the CGM time delay versus plasma glucose (PG) and self-measured blood glucose (SMBG) measurements (9-10 min). Shifting the CGM signal by 9 min backward in time versus PG and SMBG data improved the analytical accuracy of the DG4P sensor and the reliability of clinical research endpoint (hypoglycemia, postprandial glucose increments) detection. Since this method takes advantage of post-processing of CGM data, it is particularly suited for the optimization of data processing in clinical studies. In contrast, real-time corrections of time delays need predictive algorithms.
Keyphrases
- blood glucose
- electronic health record
- glycemic control
- clinical trial
- type diabetes
- big data
- machine learning
- magnetic resonance
- randomized controlled trial
- deep learning
- data analysis
- insulin resistance
- computed tomography
- skeletal muscle
- open label
- weight loss
- study protocol
- sensitive detection
- label free
- phase iii