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ATTD-2023
Feb 2023 | Poster | By Dr Arbinder Singal, Dr Ritika Verma, Sanjay Kalra, Sneha Nikte, Sudhindra Kulkarni, Snehal Tanna, Ashish Sarawte

Predictive model for estimating fasting and postprandial CGM readings by using baseline HbA1c values: Analysis of real-world data in Fitterfly Diabetes digital therapeutics program

This study used a multiple linear regression model to examine the influences of baseline HbA1c level on CGM based FBS reading and PPBS reading among 237 enrolled in Fitterfly Diabetes program. CGM-FBS reading was found to be significantly associated with HbA1c. CGM-PPBG reading was found to be significantly associated with HbA1c. HbA1c values along with baseline parameters can help in prediction of FBS and PPBS readings on a CGM device, thus helping in more informed use of CGM sensors.

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