Time in Range Estimation in Patients with Type  2 Diabetes is Improved by Incorporating Fasting and Postprandial Glucose Levels

ConclusionsThe results offered a comprehensive understanding of glucose fluctuations through FPG and PPG compared to HbA1c alone. Our novel TIR prediction model based on random forest regression with FPG, PPG, and HbA1c provides a better prediction performance than the univariate model with solely HbA1c. The results indicate a nonlinear relationship between TIR and glycaemic parameters. Our results suggest that machine learning may have the potential to be used in developing better models for understanding patients ’ disease status and providing necessary interventions for glycaemic control.
Source: Diabetes Therapy - Category: Endocrinology Source Type: research