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Predictive A1c Based on CGM Data Using CGM Data

NCT03898076 · Status: COMPLETED · Type: OBSERVATIONAL · Enrollment: 60

Last updated 2021-09-28

No results posted yet for this study

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.

Conditions

  • Diabetes Mellitus, Type 1

Interventions

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.

Sponsors & Collaborators

  • Sidra Medicine

    lead OTHER

Principal Investigators

  • Marwa Qaraqe, PhD · Hamad Bin Khalifa University, Doha

  • Hasan Abbas, PhD · TAMUQ, Doha

Eligibility

Min Age
2 Years
Max Age
18 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2020-06-01
Primary Completion
2020-08-31
Completion
2020-12-30

Countries

  • Qatar

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT03898076 on ClinicalTrials.gov