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Articles Published Processes
10/31/2025 3:02:47 PM | Browse: 1 | Download: 1
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Received |
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2025-03-17 04:02 |
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Peer-Review Started |
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2025-03-17 04:02 |
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To Make the First Decision |
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Return for Revision |
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2025-04-03 03:13 |
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Revised |
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2025-04-11 22:38 |
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Second Decision |
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2025-06-04 02:38 |
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Accepted by Journal Editor-in-Chief |
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Accepted by Executive Editor-in-Chief |
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2025-06-04 09:51 |
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Articles in Press |
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2025-06-04 09:51 |
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Publication Fee Transferred |
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Edit the Manuscript by Language Editor |
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Typeset the Manuscript |
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2025-10-20 07:32 |
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Publish the Manuscript Online |
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2025-10-31 15:02 |
| ISSN |
2219-2808 (online) |
| 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/ |
| Copyright |
The Author(s) 2025. Published by Baishideng Publishing Group Inc. All rights reserved. |
| Article Reprints |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/247
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| Permissions |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/207
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| Publisher |
Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA |
| Website |
http://www.wjgnet.com |
| Category |
Critical Care Medicine |
| Manuscript Type |
Minireviews |
| Article Title |
Use of continuous glucose monitoring systems in pediatric patients in the perioperative environment: challenges and machine learning opportunities
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| Manuscript Source |
Invited Manuscript |
| All Author List |
Tara Doherty, Ashley Kelley, Elizabeth Kim and Irim Salik |
| ORCID |
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| Funding Agency and Grant Number |
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| Corresponding Author |
Irim Salik, Associate Professor, MD, Department of Anesthesiology, Westchester Medical Center, 100 Woods Road, Valhalla, NY 10595, United States. irim.salik@wmchealth.org |
| Key Words |
Continuous glucose monitor; Continuous glucose monitoring system; Type 1 diabetes mellitus; Artificial intelligence; Electronic health records |
| 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 (EHR). This facilitates earlier interventions, improves glycemic control, and supports clinical workflows by reducing data overload. |
| Publish Date |
2025-10-31 15:02 |
| Citation |
<p>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. <i>World J Clin Pediatr</i> 2025; 14(4): 107127</p> |
| URL |
https://www.wjgnet.com/2219-2808/full/v14/i4/107127.htm |
| DOI |
https://dx.doi.org/10.5409/wjcp.v14.i4.107127 |
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