Predictive A1c Based on CGM Data Using CGM Data
Condition: Diabetes Mellitus, Type 1
MeSH terms: Diabetes Mellitus, Type 1
Study type: Observational

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.
    

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
    

Intervention name(s): Flash Glucose Monitoring; A1c; Predictive A1c
Intervention type(s): Device; Other; Other
Intervention description(s): Continuous Glucose Monitoring (CGM) values will be downloaded from CGM device for a period of 90 days.; A1c levels will be collected from Hospital EMR prior to CGM data downoad; 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.
Observational model: Cohort
Time perspective: Retrospective
Sampling method: Non-Probability Sample
Study population: 
        Patients with Type 1 Diabetes and Flash glucose monitoring