A real-time data-driven algorithm using a Kalman filter and autoregressive model yielded ~10-min-ahead glucose predictions with clinically acceptable accuracy in 34 diabetic patients.
Does a real-time data-driven algorithm accurately predict subcutaneous glucose concentration ~10 minutes ahead in diabetic patients?
A real-time data-driven algorithm can predict subcutaneous glucose concentrations ~10 minutes ahead with clinically acceptable accuracy, supporting the feasibility of universal glucose prediction models.
Continuous glucose monitoring (CGM) devices measure and record a patient's subcutaneous glucose concentration as frequently as every minute for up to several days. When coupled with data-driven mathematical models, CGM data can be used for short-term prediction of glucose concentrations in diabetic patients. In this study, we present a real-time implementation of a previously developed offline data-driven algorithm. The implementation consists of a Kalman filter for real-time filtering of CGM data and a data-driven autoregressive model for prediction. Results based on CGM data from 3 different studies involving 34 type 1 and 2 diabetic patients suggest that the proposed real-time approach can yield ~10-min-ahead predictions with clinically acceptable accuracy and, hence, could be useful as a tool for warning against impending glucose deregulation episodes. The results further support the feasibility of "universal" glucose prediction models, where an offline-developed model based on one individual's data can be used to predict the glucose levels of any other individual in real time.
Lu et al. (Mon,) conducted a other in Type 1 and 2 diabetes (n=34). Real-time data-driven algorithm (Kalman filter and autoregressive model) was evaluated on ~10-min-ahead glucose predictions. A real-time data-driven algorithm using a Kalman filter and autoregressive model yielded ~10-min-ahead glucose predictions with clinically acceptable accuracy in 34 diabetic patients.