Nardone OM, Castiglione F, Maurea S. Advancing perioperative optimization in Crohn's disease surgery with machine learning predictions. World J Gastrointest Surg 2024; 16(10): 3091-3093 [PMID: 39575292 DOI: 10.4240/wjgs.v16.i10.3091]
Corresponding Author of This Article
Olga Maria Nardone, MD, PhD, Assistant Professor, Department of Public Health, University of Naples Federico II, Via Pansini 5, Naples 80131, Italy. olgamaria.nardone@unina.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. Oct 27, 2024; 16(10): 3091-3093 Published online Oct 27, 2024. doi: 10.4240/wjgs.v16.i10.3091
Advancing perioperative optimization in Crohn's disease surgery with machine learning predictions
Olga Maria Nardone, Fabiana Castiglione, Simone Maurea
Olga Maria Nardone, Department of Public Health, University of Naples Federico II, Naples 80131, Italy
Fabiana Castiglione, Department of Clinical Medicine and Surgery, University of Naples Federico II, Naples 80131, Italy
Simone Maurea, Department of Advanced Biomedical Sciences, University of Naples Federico II, Naples 80131, Italy
Author contributions: Nardone OM was responsible for conceptualization, writing original draft, review, and editing; Castiglione F and Maurea S were responsible for writing, review, editing, and supervision; all authors have read and approved the final manuscript.
Conflict-of-interest statement: All the authors declare that they have no conflict of interest.
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: Olga Maria Nardone, MD, PhD, Assistant Professor, Department of Public Health, University of Naples Federico II, Via Pansini 5, Naples 80131, Italy. olgamaria.nardone@unina.it
Received: May 8, 2024 Revised: June 28, 2024 Accepted: July 9, 2024 Published online: October 27, 2024 Processing time: 142 Days and 5.8 Hours
Abstract
This editorial offers commentary on the article which aimed to forecast the likelihood of short-term major postoperative complications (Clavien-Dindo grade ≥ III), including anastomotic fistula, intra-abdominal sepsis, bleeding, and intestinal obstruction within 30 days, as well as prolonged hospital stays following ileocecal resection in patients with Crohn’s disease (CD). This prediction relied on a machine learning (ML) model trained on a cohort that integrated a nomogram predictive model derived from logistic regression analysis and a random forest (RF) model. Both the nomogram and RF showed good performance, with the RF model demonstrating superior predictive ability. Key variables identified as potentially critical include a preoperative CD activity index ≥ 220, low preoperative serum albumin levels, and prolonged operation duration. Applying ML approaches to predict surgical recurrence have the potential to enhance patient risk stratification and facilitate the development of preoperative optimization strategies, ultimately aiming to improve post-surgical outcomes. However, there is still room for improvement, particularly by the inclusion of additional relevant clinical parameters, consideration of medical therapies, and potentially integrating molecular biomarkers in future research efforts.
Core Tip: Preoperative optimization for patients with Crohn's disease presents numerous challenges. Traditional research methods are insufficient to address the expanding array of questions in this field. Hence, there is a growing need to explore and develop new tools grounded in advanced technologies, such as computer simulation models and artificial intelligence. Employing machine learning approaches can significantly enhance patient risk stratification and the formulation of preoperative optimization strategies, ultimately aiming to improve post-surgical outcomes.
