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
Abstract
BACKGROUND
Due to the complexity and numerous comorbidities associated with Crohn’s disease (CD), the incidence of postoperative complications is high, significantly impacting the recovery and prognosis of patients. Consequently, additional studies are required to precisely predict short-term major complications following intestinal resection (IR), aiding surgical decision-making and optimizing patient care.
AIM
To construct novel models based on machine learning (ML) to predict short-term major postoperative complications in patients with CD following IR.
METHODS
A retrospective analysis was performed on clinical data derived from a patient cohort that underwent IR for CD from January 2017 to December 2022. The study participants were randomly allocated to either a training cohort or a validation cohort. The logistic regression and random forest (RF) were applied to construct models in the training cohort, with model discrimination evaluated using the area under the curves (AUC). The validation cohort assessed the performance of the constructed models.
RESULTS
Out of the 259 patients encompassed in the study, 5.0% encountered major postoperative complications (Clavien-Dindo ≥ III) within 30 d following IR for CD. The AUC for the logistic model was 0.916, significantly lower than the AUC of 0.965 for the RF model. The logistic model incorporated a preoperative CD activity index (CDAI) of ≥ 220, a diminished preoperative serum albumin level, conversion to laparotomy surgery, and an extended operation time. A nomogram for the logistic model was plotted. Except for the surgical approach, the other three variables ranked among the top four important variables in the novel ML model.
CONCLUSION
Both the nomogram and RF exhibited good performance in predicting short-term major postoperative complications in patients with CD, with the RF model showing more superiority. A preoperative CDAI of ≥ 220, a diminished preoperative serum albumin level, and an extended operation time might be the most crucial variables. The findings of this study can assist clinicians in identifying patients at a higher risk for complications and offering personalized perioperative management to enhance patient outcomes.
Core Tip: Given the unique characteristics of Crohn’s disease (CD), the incidence of postoperative complications is notably high. Previous studies, while identifying risk factors influencing these complications, often yield inconsistent results due to the heterogeneity of patient populations. This machine learning-based study included data from a single center over a relatively short period in China. Novel models employing logistic regression and random forest were developed to inform individualized perioperative management of patients with CD. The models, particularly the random forest, demonstrated robust performance, highlighting the significance of preoperative CD activity index, serum albumin levels, and operation time as crucial predictors.
