A predictive model of nocturnal hypoglycemia based on data from a mobile application for glucose monitoring

Objective: To develop a prognostic algorithm for nocturnal hypoglycemia (NH) based on data from a mobile glucose monitoring application. Materials and Methods. A retrospective analysis of 524 continuous glucose monitoring (CGM) profiles of patients with type 1 diabetes mellitus was performed. CGM was performed using the Medtronic iPro2 system for 6-7 days, and the night periods of CGM were analyzed to identify regular NH. The study included 239 patients, of whom 65 (27.1%) experienced regular NH. We constructed the models of 7-point glycemic profiles, the data from which were uploaded to the DiaLog GM mobile application to calculate conventional glucose monitoring parameters. The prognostic model of NH was developed using the logistic regression method. Results. Based on the regression analysis, the most significant predictors of NH included in the prognostic model were glycated hemoglobin level (p=0.001), use of insulin pump therapy (p=0.001), time below the target time in range (TIR) for blood glucose content of level 1 (p<0.001), and the coefficient of variation for glucose content (p=0.02). The area under the ROC curve for the prediction model was 0.917; the optimal cut-point value for the predicted probability of NH was 0.317, at which the sensitivity of the model was 86%, and its specificity was 90%. Conclusion. Due to its higher predictive ability, the developed prediction model based on the data of a specialized mobile application allows improving existing approaches to assessing the risk of NH.