Improved estimation of glycated hemoglobin with continuous glucose monitoring and past glycated hemoglobin data.

Background Accurate estimation of glycated hemoglobin (HbA1c) from continuous glucose monitoring (CGM) remains challenging in clinic. We propose two statistical models and validate them in real-life conditions against the current standard, Glucose Management Indicator (GMI). Material and Methods Modelling utilized routinely collected data from patients with type 1 diabetes from central Poland (eligibility criteria: age >1 year old, diabetes duration >3 months, CGM use between 01/2015-12/2019). CGM records were extracted from dedicated Medtronic/Abbott databases and cross-referenced with HbA1c values; 28-day periods preceding HbA1c measurement with >75% of the sensor-active time were analysed. We developed a mixed linear regression including glycemic variability indices and patient's ID (GV-PS) intended for closed-group use and linear regression using patient-specific error of GMI (PE-PA) for general use. Models were validated with either new HbA1cs from closed-group patients or separate patient-HbA1c pool. External validation was performed with data from clinical trials. Performance metrics included bias, its 95% confidence interval (95%CI), coefficient of determination (R2) and mean root square error (RMSE). Results We included 723 HbA1c-CGM pairs from 174 patients (mean age 9.9+/-4.4 years, diabetes duration 3.7+/-3.6 years). GMI yielded R2=0.58, with different bias between Medtronic and Abbott devices [0.120% vs -0.152%, p<0.0001], and overall 95%CI -0.9% to +1%, RMSE=0.47%. GV-PS successfully captured patient-specific variance (closed-group validation: R2=0.83, bias=0.026%, 95%CI -0.562 to 0.591%, RMSE=0.31%). PE-PA performed similarly on new patients (R2=0.76, bias=-0.069%, 95%CI=-0.790 to 0.653%, RMSE=0.37%). In external validation GMI, GV-PS and PE-PA produced 73.8%, 87.5% and 91.0% predictions within 0.5% (5.5 mmol/mol) from the true value. Conclusion Constructed models performed better than GMI. PE-PA provided an accurate estimate of HbA1c with fast and straight-forward implementation.