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Clinical Trial NCT03898076 (A1c) for Diabetes Mellitus, Type 1 is completed. See the Trial Radar Card View and AI discovery tools for all the details. Or ask anything here. | ||
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Predictive A1c Based on CGM Data Using CGM Data 60 Machine Learning
Clinical Trial NCT03898076 (A1c) was an observational study for Diabetes Mellitus, Type 1 that is now completed. The study started on June 1, 2020, with plans to enroll 60 participants. Led by Sidra Medicine, the expected completion date was December 30, 2020. The latest data from ClinicalTrials.gov was last updated on September 28, 2021.
Brief Summary
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 in...Show More
Detailed Description
This is a retrospective analysis. The investigators will de-identify and analyze 120 patients with T1D using Continuous Glucose Monitoring (CGM) system 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 ne...Show More
Official Title
The Prediction of A1c Based on CGM Data Through Applying Machine Learning Approaches
Conditions
Diabetes Mellitus, Type 1Publications
Scientific articles and research papers published about this clinical trial:Other Study IDs
- A1c
- 2019003271
NCT ID Number
Start Date (Actual)
2020-06-01
Last Update Posted
2021-09-28
Completion Date (Estimated)
2020-12-30
Enrollment (Estimated)
60
Study Type
Observational
Status
Completed
Arms / Interventions
| Participant Group/Arm | Intervention/Treatment |
|---|---|
N/A | Flash Glucose Monitoring Continuous Glucose Monitoring (CGM) values will be downloaded from CGM device for a period of 90 days. A1c A1c levels will be collected from Hospital EMR prior to CGM data downoad 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. |
Primary Outcome Measures
| Outcome Measure | Measure Description | Time Frame |
|---|---|---|
The difference of Predictive A1c level from CGM data with Real A1c level from EMR | Difference (%) between Predicted A1c and laboratory A1c from the Electronic Medical Record | 3 months |
Eligibility Criteria
Eligible Ages
Child, Adult
Minimum Age
2 Years
Eligible Sexes
All
- Type 1 Diabetes
- Flash glucose Monitoring system
- Less than 70% od CGM data in the last 90 days.
Sidra MedicineStudy Responsible Party
Goran Petrovski, Principal Investigator, Goran Petrovski Clinical Professor, Sidra Medicine
No contact data.
1 Study Locations in 1 Countries
Qa
Sidra Medicine, Doha, Qa, 26999, Qatar