Anciferov M.B., .., Galstyan G.R., Demidov N.A., Zilov A.V., Kolbin A.S., Koteshkova O.M., Kurganovich A.V., Kurylev A.A., Mayorov A.Y., Markova T.N., Pashkova E.Y. 472

Glycaemic variability assessment based on self-measured blood glucose data as an alternative to continuous glucose monitoring

RELEVANCE. Traditional criteria for assessing glycemic control (HbA1c, fasting plasma glucose, and postprandial glycemia) have limited information value for assessing the risk of adverse outcomes in diabetes mellitus (DM). It was found that increased glycemic variability (GV) has a clear relationship with the risk of developing severe hypoglycemia. GV is an independent predictor of diabetes vascular complications, general and cardiovascular mortality in type 2 diabetes. Currently, the mechanisms of severe GV negative effect on the occurrence and progression of diabetes complications have been studied. According to the international consensus on the use of continuous glucose monitoring (CGM) in 2017 and 2019, the calculation of the coefficient of variation (КВ) based CGM data is used to assess GV with the optimal recommended duration of monitoring at least 14 days (Tab.1). However, the CGM procedure has its limitations: it is expensive, usually carried out within 5-7 days maximum. PURPOSE. To develop a unified method for GV assessing acceptable for widespread use in routine practice by comparing the CV calculated using CGM and self-measured blood glucose (SMBG) with the calculation of the minimum period and minimum number of measurements without loss of correlation. MATERIALS AND METHODS. The retrospective analysis based on routine research of CGM and SMBG data (583 measurements) in 35 patients with T1D. The patients performed self-control during ~ 17 days (95 CI: 14.88-18.43). The simulation was carried out by the Monte Carlo method, for each patient m = 1500 times were taken from the CGM data during each day. The following parameters were determined by the method of simulation modeling: the time of the first measurement, the number of measurements per day, the duration of the self-control period to maximize the sensitivity and specificity of the algorithm. RESULTS. An algorithm was developed and validated for assessing CV according to SMBG data, which provides for: at least 7 measurements per day. The duration of the self-control period at least 14 days. The maximum error in assessing the variability is observed when the CV value calculated from SMBG data falls within the range of 35% - 40%, which requires CGM. If CV value below 35% and above 40% it can be concluded with an accuracy of at least 95% that the true CV is low or high. CONCLUSIONS. The resulting algorithm for assessing the CV according to SMBG data provides a sensitivity and specificity of at least 96% and 84%, respectively.
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