Use of continuous glucose monitoring systems in pediatric patients in the perioperative environment: Challenges and machine learning opportunities

Pediatric type 1 diabetes (T1D) is a lifelong condition requiring meticulous glucose management to prevent acute and chronic complications. Conventional management of diabetic patients does not allow for continuous monitoring of glucose trends, and can place patients at risk for hypo- and hyperglycemia. Continuous glucose monitors (CGMs) have emerged as a mainstay for pediatric diabetic care and are continuing to advance treatment by providing real-time blood glucose (BG) data, with trend analysis aided by machine learning (ML) algorithms. These predictive analytics serve to prevent against dangerous BG variations in the perioperative environment for fasted children undergoing surgical stress. Integration of CGM data into electronic health records (EHR) is essential, as it establishes a foundation for future technologic interfaces with artificial intelligence (AI). Challenges in perioperative CGM implementation include equitable device access, protection of patient privacy and data accuracy, ensuring institution of standardized protocols, and financing the cumbersome healthcare costs associated with staff training and technology platforms. This paper advocates for implementation of CGM data into the EHR utilizing multiple facets of AI/ML algorithms.