Publication Name	World Journal of Gastrointestinal Surgery
Manuscript ID	94489
Country	Australia

Category	Surgery
Manuscript Type	Letter to the Editor
Article Title	Can serious postoperative complications in patients with Crohn’s disease be predicted using machine learning?
Manuscript Source	Invited Manuscript
All Author List	Andrew Paul Zbar
Funding Agency and Grant Number
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
Key Words	Crohn’s disease; Postoperative complications; Clavien-Dindo; Machine learning; Outcome
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.
Citation	Zbar AP. Can serious postoperative complications in patients with Crohn’s disease be predicted using machine learning? World J Gastrointest Surg 2024; In press

Received	2024-03-19 10:03
Peer-Review Started	2024-03-19 10:03
To Make the First Decision
Return for Revision	2024-06-27 06:09
Revised	2024-07-16 07:02
Second Decision	2024-09-05 02:40
Accepted by Journal Editor-in-Chief
Accepted by Company Editor-in-Chief	2024-09-05 06:44
Articles in Press	2024-09-05 06:44
Publication Fee Transferred
Edit the Manuscript by Language Editor
Typeset the Manuscript

ISSN	1948-9366 (online)
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