Wang FT, Lin Y, Yuan XQ, Gao RY, Wu XC, Xu WW, Wu TQ, Xia K, Jiao YR, Yin L, Chen CQ. Predicting short-term major postoperative complications in intestinal resection for Crohn’s disease: A machine learning-based study. World J Gastrointest Surg 2024; 16(3): 717-730 [PMID: 38577067 DOI: 10.4240/wjgs.v16.i3.717]
Corresponding Author of This Article
Chun-Qiu Chen, MD, PhD, Associate Professor, Chief Doctor, Diagnostic and Treatment Center for Refractory Diseases of Abdomen Surgery, Shanghai Tenth People’s Hospital, Tongji University School of Medicine, No. 301 Yanchang Middle Road, Shanghai 200072, China. chenchunqiu6@126.com
Research Domain of This Article
Surgery
Article-Type of This Article
Retrospective Study
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/
Fang-Tao Wang, Yin Lin, Xiao-Qi Yuan, Ren-Yuan Gao, Xiao-Cai Wu, Wei-Wei Xu, Tian-Qi Wu, Kai Xia, Yi-Ran Jiao, Lu Yin, Chun-Qiu Chen, Diagnostic and Treatment Center for Refractory Diseases of Abdomen Surgery, Shanghai Tenth People’s Hospital, Tongji University School of Medicine, Shanghai 200072, China
Author contributions: Wang FT contributed to the manuscript writing, data collection and analysis; Lin Y, Yuan XQ, Gao RY, Wu XC, Xu WW, Wu TQ, Xia K, and Jiao YR collected data; Yin L and Chen CQ were involved in the conceptualization and supervision of this manuscript; and all authors approved the final manuscript.
Supported byHorizontal Project of Shanghai Tenth People’s Hospital, No. DS05!06!22016 and No. DS05!06!22017.
Institutional review board statement: This study was reviewed and approved by the Ethics Committee of the Shanghai Tenth People’s Hospital (SHSY-IEC-5.0/24K3/P01).
Informed consent statement: Patients were not required to give informed consent to the study because the analysis used anonymous clinical data that were obtained after each patient agreed to treatment by written consent.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
Data sharing statement: The datasets used and analyzed during the current study are available from the corresponding author on reasonable request.
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: Chun-Qiu Chen, MD, PhD, Associate Professor, Chief Doctor, Diagnostic and Treatment Center for Refractory Diseases of Abdomen Surgery, Shanghai Tenth People’s Hospital, Tongji University School of Medicine, No. 301 Yanchang Middle Road, Shanghai 200072, China. chenchunqiu6@126.com
Received: September 27, 2023 Peer-review started: September 27, 2023 First decision: December 28, 2023 Revised: January 12, 2024 Accepted: February 18, 2024 Article in press: February 18, 2024 Published online: March 27, 2024 Processing time: 176 Days and 23.5 Hours
ARTICLE HIGHLIGHTS
Research background
The high incidence of postoperative complications in Crohn’s disease (CD) has prompted an urgent need for predicting postoperative risks, especially in perioperative decision-making. The improved machine learning (ML)-based models have demonstrated high accuracy in predicting medical outcomes and identifying high-risk patients.
Research motivation
Short-term major postoperative complications in CD deserve particular attention to enhance the accuracy of perioperative decision-making and the expected patient recovery.
Research objectives
This study aimed to clearly identify the key risk factors for short-term major postoperative complications in CD patients, construct and verify the logistics regression model and random forest (RF) model based on ML, so as to enhance the accuracy of surgical decision-making.
Research methods
A retrospective analysis was conducted on surgical data from CD patients between 2017 and 2022. Patients underwent rigorous screening before being randomly assigned to training and validation groups. Independent risk factors for short-term postoperative major complications were determined by logistic regression analysis, and a nomogram prediction model was constructed. Concurrently, RF analysis was conducted to screen important factors for short-term postoperative major complications.
Research results
Among the included 259 CD patients, it was observed that 5.0% experienced major complications within 30 d postoperatively. CD activity index ≥ 220, longer operation time, and reduced preoperative albumin levels were identified as significant factors influencing the occurrence of major postoperative short-term complications in both models.
Research conclusions
Both the nomogram model and the RF model established in this study demonstrated good predictive performance, offering practical individualized risk assessment for clinical decision-making.
Research perspectives
The application of ML-based predictive models to assist personalized medical decision making in CD surgery is valuable. Predictive models across diverse clinical practices necessitate further integration, improvement, and promotion.
