Clinical trial NCT03898076: Predictive A1c Based on CGM Data Using CGM Data
Official title: The Prediction of A1c Based on CGM Data Through Applying Machine Learning Approaches
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: Diabetes Mellitus, Type 1
Lead sponsor: Sidra Medicine
Sponsor class: OTHER
First posted: 2019-04-01
Enrollment: 60
Sex: ALL
Minimum age (years): 2
Maximum age (years): 18
Standard ages: CHILD
ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: RETROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Patients with Type 1 Diabetes and Flash glucose monitoring

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 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.

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 net glycemic action (CONGA) will be extracted. A machine learning model will be developed to predict HbA1c in 2-3 months advance based on these 15 days of CGM data. The model is using linear regression, penalized regression (Ridge regression, Lasso regression and Elastic net regression) in combination gradient boosting to calculate predictive A1c

Eligibility criteria: Inclusion Criteria:

* Type 1 Diabetes
* Flash glucose Monitoring system

Exclusion Criteria:

* Less than 70% od CGM data in the last 90 days.

Primary outcomes:
measure: The difference of Predictive A1c level from CGM data with Real A1c level from EMR
timeFrame: 3 months
Secondary outcomes:
Other outcomes:
Locations:
city: Doha
country: Qatar
facility: Sidra Medicine
state: Qa
Study officials:
affiliation: Hamad Bin Khalifa University, Doha
name: Marwa Qaraqe, PhD
role: PRINCIPAL_INVESTIGATOR
affiliation: TAMUQ, Doha
name: Hasan Abbas, PhD
role: PRINCIPAL_INVESTIGATOR
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citation: Rohlfing CL, Wiedmeyer HM, Little RR, England JD, Tennill A, Goldstein DE. Defining the relationship between plasma glucose and HbA(1c): analysis of glucose profiles and HbA(1c) in the Diabetes Control and Complications Trial. Diabetes Care. 2002 Feb;25(2):275-8. doi: 10.2337/diacare.25.2.275.