Diabetes Mellitus, Type 1A1cNCT03898076
Predictive A1c Based on CGM Data Using CGM Data
An observational study of Diabetes Mellitus, Type 1, run by Sidra Medicine at 1 site in Qatar.
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. Read more about this study
- Who is it for?
- Ages 2 to 18 years
- Conditions the study is for
- Diabetes Mellitus, Type 1
- What is being tested?
- Flash Glucose Monitoring, A1c and Predictive A1c
- Type of treatment
- Medical device
- How many people
- 60 participants
- Where
- 1 siteQatar
- Study dates
- 1 Jun 2020to 30 Dec 2020
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 2 to 18 years
- All sexes
- No healthy volunteers
- Child, Adult
Study population: Patients with Type 1 Diabetes and Flash glucose monitoring
Who may be able to join
- Type 1 Diabetes
- Flash glucose Monitoring system
Who cannot join
- Less than 70% od CGM data in the last 90 days.
Trial locations and contacts
1 site in 1 country.
About this study
Official title: The Prediction of A1c Based on CGM Data Through Applying Machine Learning Approaches
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.
Read the 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
- Conditions
- Diabetes Mellitus, Type 1
- Being tested
- Flash Glucose Monitoring, A1c, Predictive A1c
- Medical subject headings
- Diabetes Mellitus, Type 1
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
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.
What is being measured
The primary outcome is the main question the study is designed to answer.
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
Measured: 3 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted1 Apr 2019
- Study start1 Jun 2020
- Primary completion31 Aug 2020
- Study completion30 Dec 2020
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (4)Papers the sponsor has linked to this study.
- Other trials for Diabetes Mellitus, Type 1Studies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 60 participants (actual)
Sponsor and investigators
- Lead sponsor
- Sidra Medicine
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Goran Petrovski, Sidra Medicine
- Investigators
- Marwa Qaraqe, PhD, Principal investigator, Hamad Bin Khalifa University, DohaHasan Abbas, PhD, Principal investigator, TAMUQ, Doha
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT03898076
- Sponsor's protocol ID
- 2019003271
- Acronym
- A1c
- Status last verified
- Sep 2021
- First submitted
- 27 Mar 2019
- Last updated
- 28 Sep 2021
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided
Publications
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.
PubMed 11248599Alberti 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.
PubMed 9686693Ogurtsova 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.
PubMed 28437734Rohlfing 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.
PubMed 11815495
Study at a glance
From the registry record, last updated by the sponsor on 28 Sep 2021. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- The Prediction of A1c Based on CGM Data Through Applying Machine Learning Approaches
- ClinicalTrials.gov ID
- NCT03898076
- Acronym / sponsor ID
- A1c / 2019003271
- Conditions
- Diabetes Mellitus, Type 1
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 60 participants (actual)
- Lead sponsor
- Sidra MedicineUniversity, hospital or other
- Study start
- 1 Jun 2020 (actual)
- Primary completion
- 31 Aug 2020 (actual)
- Study completion
- 30 Dec 2020 (actual)
- First posted
- 1 Apr 2019 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
Who may qualify
- Ages
- 2 to 18 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult
- Study population
- Patients with Type 1 Diabetes and Flash glucose monitoring
Key inclusion criteria (2 listed)
- Type 1 Diabetes
- Flash glucose Monitoring system
Key exclusion criteria (1 listed)
- Less than 70% od CGM data in the last 90 days.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Flash Glucose MonitoringContinuous Glucose Monitoring (CGM) values will be downloaded from CGM device for a period of 90 days. | Device | – |
| A1cA1c levels will be collected from Hospital EMR prior to CGM data downoad | Other | – |
| Predictive A1cPredictive 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. | Other | – |
Primary outcome
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
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 28 Sep 2021.
- Results publication: PubMed 11248599Ball 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.
- Results publication: PubMed 9686693Alberti 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.
- Results publication: PubMed 28437734Ogurtsova 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.
- Results publication: PubMed 11815495Rohlfing 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.
- Individual participant dataSharing planned: Undecided
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Marwa Qaraqe, PhD principal investigator
Hamad Bin Khalifa University, Doha
Hasan Abbas, PhD principal investigator
TAMUQ, Doha
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Diabetes Mellitus, Type 1, run by Sidra Medicine at 1 site in Qatar.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 2 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 28 Sep 2021. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Qatar. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
The difference of Predictive A1c level from CGM data with Real A1c level from EMR (time frame: 3 months). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT03898076, which also lists 4 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 28 Sep 2021.