©The Author(s) 2025. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Clin Pediatr. Dec 9, 2025; 14(4): 107127
Published online Dec 9, 2025. doi: 10.5409/wjcp.v14.i4.107127
Published online Dec 9, 2025. doi: 10.5409/wjcp.v14.i4.107127
Use of continuous glucose monitoring systems in pediatric patients in the perioperative environment: Challenges and machine learning opportunities
Tara Doherty, Ashley Kelley, Elizabeth Kim, Irim Salik, Department of Anesthesiology, Westchester Medical Center, Maria Fareri Children's Hospital, Valhalla, NY 10595, United States
Author contributions: Doherty T, Kim E conceptualized and wrote the manuscript, conceptualized figures and tables, edited and formatted the manuscript and references; Salik I wrote and conceptualized portions of the manuscript, performed literature review and participated in the editing process; Kelley A helped with editing of the manuscript.
Conflict-of-interest statement: The authors have no conflicts of interest to declare.
Corresponding author: Irim Salik, MD, Associate Professor, Department of Anesthesiology, Westchester Medical Center, Maria Fareri Children's Hospital, 100 Woods Road, Valhalla, NY 10595, United States. irim.salik@wmchealth.org
Received: March 17, 2025
Revised: April 11, 2025
Accepted: June 4, 2025
Published online: December 9, 2025
Processing time: 230 Days and 1.8 Hours
Revised: April 11, 2025
Accepted: June 4, 2025
Published online: December 9, 2025
Processing time: 230 Days and 1.8 Hours
Core Tip
Core Tip: Continuous glucose monitoring systems (CGMS) have become the standard of care for many diabetic patients, offering more precise disease management and reducing the risk of hypo- and hyperglycemia. While CGMs provide continuous data, their accuracy and reliability during periods of surgical stress and anesthetic agent use can be variable and warrants further research to validate their use perioperatively. AI algorithms can analyze CGM trends, detect patterns, and provide personalized insights directly within the electronic health record. This facilitates earlier interventions, improves glycemic control, and supports clinical workflows by reducing data overload.