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Predictive A1c Based on CGM Data Using CGM Data (A1c)

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ClinicalTrials.gov Identifier: NCT03898076
Recruitment Status : Not yet recruiting
First Posted : April 1, 2019
Last Update Posted : April 2, 2019
Sponsor:
Information provided by (Responsible Party):
Goran Petrovski, Sidra Medical and Research Center

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.


Condition or disease Intervention/treatment
Diabetes Mellitus, Type 1 Device: Flash Glucose Monitoring Other: A1c Other: Predictive A1c

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

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Study Type : Observational
Estimated Enrollment : 120 participants
Observational Model: Cohort
Time Perspective: Retrospective
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

Resource links provided by the National Library of Medicine



Intervention Details:
  • 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.


Primary Outcome Measures :
  1. 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



Information from the National Library of Medicine

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Ages Eligible for Study:   2 Years to 18 Years   (Child, Adult)
Sexes Eligible for Study:   All
Accepts Healthy Volunteers:   No
Sampling Method:   Non-Probability Sample
Study Population
Patients with Type 1 Diabetes and Flash glucose monitoring
Criteria

Inclusion Criteria:

  • Type 1 Diabetes
  • Flash glucose Monitoring system

Exclusion Criteria:

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

Information from the National Library of Medicine

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


Contacts
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Contact: Goran Petrovski, MD, MSc, PhD +97470745178 gpetrovski@sidra.org
Contact: Islam Shafiqul, MD

Locations
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Qatar
Sidra Medicine
Doha, Qa, Qatar, 26999
Sponsors and Collaborators
Sidra Medical and Research Center
Investigators
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Principal Investigator: Marwa Qaraqe, PhD Hamad Bin Khalifa University, Doha
Principal Investigator: Hasan Abbas, PhD TAMUQ, Doha

Publications of Results:
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Responsible Party: Goran Petrovski, Goran Petrovski Clinical Professor, Sidra Medical and Research Center
ClinicalTrials.gov Identifier: NCT03898076     History of Changes
Other Study ID Numbers: 2019003271
First Posted: April 1, 2019    Key Record Dates
Last Update Posted: April 2, 2019
Last Verified: March 2019
Individual Participant Data (IPD) Sharing Statement:
Plan to Share IPD: Undecided

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Studies a U.S. FDA-regulated Drug Product: No
Studies a U.S. FDA-regulated Device Product: No

Additional relevant MeSH terms:
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Diabetes Mellitus
Diabetes Mellitus, Type 1
Glucose Metabolism Disorders
Metabolic Diseases
Endocrine System Diseases
Autoimmune Diseases
Immune System Diseases