Predictive A1c Based on CGM Data Using CGM Data

Diabetes Mellitus, Type 1 Clinical Trial

— A1c  

Official title:

The Prediction of A1c Based on CGM Data Through Applying Machine Learning Approaches

Clinical Trial Details — Status: Completed

Administrative data

• NCT number: NCT03898076
• Other study ID #: 2019003271
• Status: Completed
• Start date: June 1, 2020
• Est. completion date: December 30, 2020

Study information

• Verified date: September 2021
• Source: Sidra Medical and Research Center
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational

Clinical Trial 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.

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

Recruitment information / eligibility

• Status: Completed
• Enrollment: 60
• Est. completion date: December 30, 2020
• Est. primary completion date: August 31, 2020
• Accepts healthy volunteers: No
• Gender: All
• Age group: 2 Years to 18 Years

Eligibility

Inclusion Criteria:

• Type 1 Diabetes

• Flash glucose Monitoring system

Exclusion Criteria:

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

Related Conditions & MeSH terms

• Diabetes Mellitus, Type 1

Intervention

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
• 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.

Locations

Country	Name	City	State
Qatar	Sidra Medicine	Doha	Qa

Sponsors (1)

Lead Sponsor	Collaborator
Sidra Medical and Research Center

Country where clinical trial is conducted

Qatar, 

References & Publications (4)

Alberti KG, Zimmet PZ. Definition, diagnosis and classification of diabetes mellitus and its complications. Part 1: diagnosis and classification of diabetes mellitus provisional report of a WHO consultation. Diabet Med. 1998 Jul;15(7):539-53. — View Citation

Ball MJ, Lillis J. E-health: transforming the physician/patient relationship. Int J Med Inform. 2001 Apr;61(1):1-10. Review. — View Citation

Ogurtsova K, da Rocha Fernandes JD, Huang Y, Linnenkamp U, Guariguata L, Cho NH, Cavan D, Shaw JE, Makaroff LE. IDF Diabetes Atlas: Global estimates for the prevalence of diabetes for 2015 and 2040. Diabetes Res Clin Pract. 2017 Jun;128:40-50. doi: 10.101 — View 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 — View Citation

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	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