NCT03898076

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.

Trial Health

87
On Track

Trial Health Score

Automated assessment based on enrollment pace, timeline, and geographic reach

Enrollment
60

participants targeted

Target at P25-P50 for all trials

Timeline
Completed

Started Jun 2020

Shorter than P25 for all trials

Geographic Reach
1 country

1 active site

Status
completed

Health score is calculated from publicly available data and should be used for screening purposes only.

Trial Relationships

Click on a node to explore related trials.

Study Timeline

Key milestones and dates

First Submitted

Initial submission to the registry

March 27, 2019

Completed
5 days until next milestone

First Posted

Study publicly available on registry

April 1, 2019

Completed
1.2 years until next milestone

Study Start

First participant enrolled

June 1, 2020

Completed
3 months until next milestone

Primary Completion

Last participant's last visit for primary outcome

August 31, 2020

Completed
4 months until next milestone

Study Completion

Last participant's last visit for all outcomes

December 30, 2020

Completed
Last Updated

September 28, 2021

Status Verified

September 1, 2021

Enrollment Period

3 months

First QC Date

March 27, 2019

Last Update Submit

September 26, 2021

Conditions

Diabetes Mellitus, Type 1

Outcome Measures

Primary Outcomes (1)

  • 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

Interventions

Flash Glucose MonitoringDEVICE

Continuous Glucose Monitoring (CGM) values will be downloaded from CGM device for a period of 90 days.

A1cOTHER

A1c levels will be collected from Hospital EMR prior to CGM data downoad

Predictive A1cOTHER

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.

Eligibility Criteria

Age2 Years - 18 Years
Sexall
Healthy VolunteersNo
Age GroupsChild (0-17), Adult (18-64)
Sampling MethodNon-Probability Sample
Study Population

Patients with Type 1 Diabetes and Flash glucose monitoring

You may qualify if:

  • Type 1 Diabetes
  • Flash glucose Monitoring system

You may not qualify if:

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

Contact the study team to confirm eligibility.

Sponsors & Collaborators

Study Sites (1)

Sidra Medicine

Doha, Qa, 26999, Qatar

Location

Related Publications (4)

  • Ball MJ, Lillis J. E-health: transforming the physician/patient relationship. Int J Med Inform. 2001 Apr;61(1):1-10. doi: 10.1016/s1386-5056(00)00130-1.

    PMID: 11248599RESULT

  • 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. doi: 10.1002/(SICI)1096-9136(199807)15:73.0.CO;2-S.

    PMID: 9686693RESULT

  • 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: 10.1016/j.diabres.2017.03.024. Epub 2017 Mar 31.

    PMID: 28437734RESULT

  • 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.

    PMID: 11815495RESULT

MeSH Terms

Conditions

Diabetes Mellitus, Type 1

Condition Hierarchy (Ancestors)

Diabetes MellitusGlucose Metabolism DisordersMetabolic DiseasesNutritional and Metabolic DiseasesEndocrine System DiseasesAutoimmune DiseasesImmune System Diseases

Study Officials

  • Marwa Qaraqe, PhD

    Hamad Bin Khalifa University, Doha

    PRINCIPAL INVESTIGATOR

  • Hasan Abbas, PhD

    TAMUQ, Doha

    PRINCIPAL INVESTIGATOR

Study Design

Study Type
observational
Observational Model
COHORT
Time Perspective
RETROSPECTIVE
Sponsor Type
OTHER
Responsible Party
PRINCIPAL INVESTIGATOR
PI Title
Goran Petrovski Clinical Professor

Study Record Dates

First Submitted

March 27, 2019

First Posted

April 1, 2019

Study Start

June 1, 2020

Primary Completion

August 31, 2020

Study Completion

December 30, 2020

Last Updated

September 28, 2021

Record last verified: 2021-09

Locations

QA(1)