Doherty T, Kelley A, Kim E, Salik I. Use of continuous glucose monitoring systems in pediatric patients in the perioperative environment: Challenges and machine learning opportunities. World J Clin Pediatr 2025; 14(4): 107127 [DOI: 10.5409/wjcp.v14.i4.107127]
Corresponding Author of This Article
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
Research Domain of This Article
Critical Care Medicine
Article-Type of This Article
Minireviews
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
Dec 9, 2025 (publication date) through Oct 31, 2025
Times Cited of This Article
Times Cited (0)
Journal Information of This Article
Publication Name
World Journal of Clinical Pediatrics
ISSN
2219-2808
Publisher of This Article
Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA
Share the Article
Doherty T, Kelley A, Kim E, Salik I. Use of continuous glucose monitoring systems in pediatric patients in the perioperative environment: Challenges and machine learning opportunities. World J Clin Pediatr 2025; 14(4): 107127 [DOI: 10.5409/wjcp.v14.i4.107127]
World J Clin Pediatr. Dec 9, 2025; 14(4): 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
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.
Open Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
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: 229 Days and 14.9 Hours
Abstract
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.
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.
