The review summarizes the current state of the artificial pancreas (AP) systems and introduces various new modules that should be included in future AP systems.
A fully automated AP must be able to detect and mitigate the effects of meals, exercise, stress and sleep on blood glucose concentrations. This can only be achieved by using a multivariable approach that leverages information from wearable devices that provide real-time streaming data about various physiological variables that indicate imminent changes in blood glucose concentrations caused by meals, exercise, stress and sleep.
The development of a fully automated AP will necessitate the design of multivariable and adaptive systems that use information from wearable devices in addition to glucose sensors and modify the models used in their model-predictive alarm and control systems to adapt to the changes in the metabolic state of the user. These AP systems will also integrate modules for controller performance assessment, fault detection and diagnosis, machine learning and classification to interpret various signals and achieve fault-tolerant control. Advances in wearable devices, computational power, and safe and secure communications are enabling the development of fully automated multivariable AP systems.
CME-accredited GLP-1RAs webcast on real-world data
Led by Melanie Davies, Stewart Harris & Takashi Kadowaki, this on-demand webcast focuses on treatment with GLP-1RAs, based on real-world data, and the emerging landscape of GLP-1RAs in the context of approved guidelines.