Pellegrino R, Gravina AG. Machine learning as a tool predicting short-term postoperative complications in Crohn’s disease patients undergoing intestinal resection: What frontiers? World J Gastrointest Surg 2024; 16(9): 2755-2759 [PMID: 39351543 DOI: 10.4240/wjgs.v16.i9.2755]
Corresponding Author of This Article
Antonietta Gerarda Gravina, MD, PhD, Associate Professor, Division of Hepatogastroenterology, Department of Precision Medicine, University of Campania Luigi Vanvitelli, Via L. de Crecchio, Naples 80138, Italy. antoniettagerarda.gravina@unicampania.it
Research Domain of This Article
Surgery
Article-Type of This Article
Editorial
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/
World J Gastrointest Surg. Sep 27, 2024; 16(9): 2755-2759 Published online Sep 27, 2024. doi: 10.4240/wjgs.v16.i9.2755
Machine learning as a tool predicting short-term postoperative complications in Crohn’s disease patients undergoing intestinal resection: What frontiers?
Raffaele Pellegrino, Antonietta Gerarda Gravina
Raffaele Pellegrino, Antonietta Gerarda Gravina, Division of Hepatogastroenterology, Department of Precision Medicine, University of Campania Luigi Vanvitelli, Naples 80138, Italy
Author contributions: Pellegrino R and Gravina AG contributed equally to this work. Pellegrino R and Gravina AG collected the literature, wrote the initial manuscript, conceptualised the structure of the text, critically revised the manuscript for important intellectual content, and read and approved the final version of the manuscript.
Conflict-of-interest statement: All the authors report 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: Antonietta Gerarda Gravina, MD, PhD, Associate Professor, Division of Hepatogastroenterology, Department of Precision Medicine, University of Campania Luigi Vanvitelli, Via L. de Crecchio, Naples 80138, Italy. antoniettagerarda.gravina@unicampania.it
Received: February 21, 2024 Revised: May 19, 2024 Accepted: June 14, 2024 Published online: September 27, 2024 Processing time: 209 Days and 17.1 Hours
Abstract
The recent study, “Predicting short-term major postoperative complications in intestinal resection for Crohn’s disease: A machine learning-based study” investigated the predictive efficacy of a machine learning model for major postoperative complications within 30 days of surgery in Crohn’s disease (CD) patients. Employing a random forest analysis and Shapley Additive Explanations, the study prioritizes factors such as preoperative nutritional status, operative time, and CD activity index. Despite the retrospective design’s limitations, the model’s robustness, with area under the curve values surpassing 0.8, highlights its clinical potential. The findings align with literature supporting preoperative nutritional therapy in inflammatory bowel diseases, emphasizing the importance of comprehensive assessment and optimization. While a significant advancement, further research is crucial for refining preoperative strategies in CD patients.
Core Tip: In this editorial on the abovementioned study, a machine learning model predicts major postoperative complications within 30 days for Crohn’s disease (CD) patients undergoing intestinal resection. Prioritizing factors include preoperative nutritional status, operative time, and CD activity index. The model’s robustness, with area under the curve values exceeding 0.8, emphasizes the clinical significance of comprehensive preoperative assessment and nutritional optimization in CD. These findings, discussed in the editorial context, align with existing literature and endorse European Society for Clinical Nutrition and Metabolism guidelines. Further research is warranted to refine preoperative strategies for this patient population.
