Predictive A1c Based on CGM Data Using CGM Data (A1c)
|The safety and scientific validity of this study is the responsibility of the study sponsor and investigators. Listing a study does not mean it has been evaluated by the U.S. Federal Government. Know the risks and potential benefits of clinical studies and talk to your health care provider before participating. Read our disclaimer for details.|
|ClinicalTrials.gov Identifier: NCT03898076|
Recruitment Status : Not yet recruiting
First Posted : April 1, 2019
Last Update Posted : April 2, 2019
Introduction. The hemoglobin A1C (HbA1c) reflects the average blood glucose level for last two to three months. Recent advancements in the sensor technology facilitate the daily monitoring of the blood glucose using CGM devices. The future prediction of the HbA1C based on the CGM data holds a critical significance in maintaining long term health of diabetes patients. A higher than normal value of the HbA1c greatly increases the likelihood of diabetes related cardiovascular disease.
Goal. The aim this study is to predict the HbA1c in advance by utilizing the CGM data through applying machine learning techniques. The outcomes of this research will assist in improving the health of diabetic patients.
Methods. This is a retrospective analysis. The investigators will de-identify and analyze 120 patients with T1D who using CGM sensor for last three months. Past 15 days of CGM data will be analyzed and different glucose variability features such as time in range (TIR), coefficient of variation (CV), mean amplitude of glycemic excursion (MAGE), mean of daily differences (MODD), continuous overall net glycemic action (CONGA) will be extracted. A machine learning model will calculate (predict) HbA1c in 2-3 months advance based on these 15 days of CGM data. To evaluate the performance of the proposed prediction model, predicted HbA1c will be compared with the real HbA1c.
|Condition or disease||Intervention/treatment|
|Diabetes Mellitus, Type 1||Device: Flash Glucose Monitoring Other: A1c Other: Predictive A1c|
|Study Type :||Observational|
|Estimated Enrollment :||120 participants|
|Official Title:||The Prediction of A1c Based on CGM Data Through Applying Machine Learning Approaches|
|Estimated Study Start Date :||April 1, 2019|
|Estimated Primary Completion Date :||June 30, 2019|
|Estimated Study Completion Date :||September 30, 2019|
- Device: Flash Glucose Monitoring
Continuous Glucose Monitoring (CGM) values will be downloaded from CGM device for a period of 90 days.
- Other: A1c
A1c levels will be collected from Hospital EMR prior to CGM data downoad
- Other: Predictive A1c
Predictive A1c will be calculated based on the first 15 days of CGM data using time in range (TIR), coefficient of variation (CV), mean amplitude of glycemic excursion (MAGE), mean of daily differences (MODD), continuous overall net glycemic action (CONGA). Predictive A1c will be correlated with actual A1c.
- The difference of Predictive A1c level from CGM data with Real A1c level from EMR [ Time Frame: 3 months ]Difference (%) between Predicted A1c and laboratory A1c from the Electronic Medical Record
To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.
Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT03898076
|Contact: Goran Petrovski, MD, MSc, PhDemail@example.com|
|Contact: Islam Shafiqul, MD|
|Doha, Qa, Qatar, 26999|
|Principal Investigator:||Marwa Qaraqe, PhD||Hamad Bin Khalifa University, Doha|
|Principal Investigator:||Hasan Abbas, PhD||TAMUQ, Doha|