Letter to the Editor
Copyright ©The Author(s) 2024. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastrointest Surg. Oct 27, 2024; 16(10): 3358-3362
Published online Oct 27, 2024. doi: 10.4240/wjgs.v16.i10.3358
Can serious postoperative complications in patients with Crohn’s disease be predicted using machine learning?
Andrew Paul Zbar
Andrew Paul Zbar, Department of Neuroscience and Anatomy, University of Melbourne, Melbourne 3010, Victoria, Australia
Author contributions: Zbar AP wrote and conceived of this submission.
Conflict-of-interest statement: The author reports no relevant conflicts of interest for this article.
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: Andrew Paul Zbar, FRCS (Ed), MBBS, MD, Doctor, Full Professor, Surgeon, Department of Neuroscience and Anatomy, University of Melbourne, Parkville Campus, Grattan Street, Melbourne 3010, Victoria, Australia. apzbar1355@yahoo.com
Received: March 19, 2024
Revised: July 16, 2024
Accepted: September 5, 2024
Published online: October 27, 2024
Processing time: 192 Days and 18.6 Hours
Core Tip

Core Tip: Significant postoperative complications continue to be a challenge in those who come to operation for Crohn’s disease. Modern management with immunosuppressive treatment has only significantly delayed surgery rather than prevented the need for operation. Machine learning provides new algorithms that supersede logistic regression of prognostic outcome factors in retrospective analyses. Multi-institutional prospective studies are required to better identify those patients where major complications are likely and where there will be a requirement for postoperative critical care and higher health care expenditure.