Time in range prediction using the experimental mobile application in type 1 diabetes

Abstract

BACKGROUND: Time in range (TIR) is a promising indicator of glycemic control used for evaluation of continuous glucose monitoring (CGM) for patients with diabetes mellitus (DM). The current problem is the assessment and prediction of TIR for patients who use self-monitoring of blood glucose (SМBG) corresponding low CGM availability for the majority of diabetic patients.AIM: To develop a predictive model of TIR for patients with T1DM based on data of the experimental mobile application.MATERIALS AND METHODS: An analysis of 1253 professional CGM profiles of patients with T1DM was performed. On the base of included records, TIR(CGM) was calculated and training models of 7-point SMBG profiles were generated. SMBG profiles’re loaded into the developed experimental mobile application that calculated standard glycemic control parameters. The dataset was divided into main and test samples (80 and 20%). For the main sample, the following methods’re used to develop predictive models: simple linear regression (SLR), multiple linear regression (MLR), artificial neural network (ANN). The effectiveness of the developed models was assessed on the test sample with the calculation of the mean absolute error (MAE), the root mean square error (RMSE).RESULTS: The 568 CGM profiles’re included in the study. TIR in the main group (n=454) — 45 [33; 65]%, in the test group (n=114) — 43 [33; 58]%. The most significant predictors of the regression models were the derived TIR (dTIR), p<0,001; derived time below range level 1 (dTBR1), p<0,001; standard deviation of blood glucose (SD), p=0,007. Determination coefficient for SLR (predictor: dTIR) — 0,844; for MLR (predictors: dTIR, dTBR1, SD) — 0,907. ANN multilayer perceptron models with two and one hidden layers’re developed, with the RMSE on the validation set 4,617 and 6,639%, respectively. The results of the forecast efficiency on the test sample were: dTIR: MAE — 6,82%, RMSE — 8,60%; SLR: MAE — 5,66%, RMSE — 7,34%; MLR: MAE — 4,18%, RMSE — 5,28%; ANN (2 layers): MAE — 4,14%, RMSE — 5,19%; ANN (1 layer): MAE — 4,44%, RMSE — 5,52%.CONCLUSION: ANN with two hidden layers and MLR demonstrated the best ability for TIR prediction. Further studies are required for clinical validation of developed prognostic models.

Keywords

• time in range
• mhealth
• machine learning
• artificial neural networks