Yuan JH, Jin YM, Xiang JY, Li SS, Zhong YX, Zhang SL, Zhao B. Machine learning-based prediction of postoperative mortality risk after abdominal surgery. World J Gastrointest Surg 2025; 17(4): 103696 [DOI: 10.4240/wjgs.v17.i4.103696]
Corresponding Author of This Article
Bin Zhao, MD, Chief Physician, Department of General Surgery, Seventh People’s Hospital of Shanghai University of Traditional Chinese Medicine, No. 358 Datong Road, Gaoqiao Town, Pudong New District, Shanghai 201317, China. zhaobinpwk@163.com
Research Domain of This Article
Gastroenterology & Hepatology
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/
Ji-Hong Yuan, Yong-Mei Jin, Bin Zhao, Department of General Surgery, Seventh People’s Hospital of Shanghai University of Traditional Chinese Medicine, Shanghai 201317, China
Jing-Ye Xiang, Department of Health Management, Zhenru Community Health Service Center of Putuo District, Shanghai 200333, China
Shuang-Shuang Li, Department of Oncology, Seventh People’s Hospital of Shanghai University of Traditional Chinese Medicine, Shanghai 201317, China
Ying-Xi Zhong, Department of Rehabilitation, Seventh People’s Hospital of Shanghai University of Traditional Chinese Medicine, Shanghai 201317, China
Shu-Liu Zhang, Department of Critical Care Medicine, The 960th Hospital of the PLA Joint Logistics Support Force, Jinan 250000, Shandong Province, China
Co-first authors: Ji-Hong Yuan and Yong-Mei Jin.
Author contributions: Yuan JH and Jin YM contributed equally to this work as co-first authors; Yuan JH and Zhao B contributed to conception and design; Jin YM contributed to administrative support; Xiang JY contributed to provision of study materials; Li SS contributed to collection and assembly of data; Zhong YX contributed to data analysis and interpretation; Yuan JH, Jin YM, Xiang JY, Li SS, Zhong YX, Zhang SL, Zhao B contributed to manuscript writing and final approval of manuscript.
Supported by the Shanghai Municipal Health Commission Project, No. 20214Y0284.
Institutional review board statement: The study was reviewed and approved by the Institutional Review Board of the Seventh People’s Hospital of Shanghai University of Traditional Chinese Medicine.
Informed consent statement: All study participants or their legal guardians provided written informed consent for personal and medical data collection before study enrolment.
Conflict-of-interest statement: The authors declare that they have no conflict of interest.
Data sharing statement: Data used in this study can be obtained from the corresponding author.
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: Bin Zhao, MD, Chief Physician, Department of General Surgery, Seventh People’s Hospital of Shanghai University of Traditional Chinese Medicine, No. 358 Datong Road, Gaoqiao Town, Pudong New District, Shanghai 201317, China. zhaobinpwk@163.com
Received: December 27, 2024 Revised: January 25, 2025 Accepted: February 18, 2025 Published online: April 27, 2025 Processing time: 91 Days and 23.3 Hours
Abstract
BACKGROUND
Preoperative risk assessments are vital for identifying patients at high risk of postoperative mortality. However, traditional scoring systems can be time consuming. We hypothesized that the use of machine learning models would enable rapid and accurate risk assessments to be performed.
AIM
To assess the potential of machine learning algorithms to develop predictive models of mortality risk after abdominal surgery.
METHODS
This retrospective study included 230 individuals who underwent abdominal surgery at the Seventh People’s Hospital of Shanghai University of Traditional Chinese Medicine between January 2023 and December 2023. Demographic and surgery-related data were collected and used to develop nomogram, decision-tree, random-forest, gradient-boosting, support vector machine, and naïve Bayesian models to predict 30-day mortality risk after abdominal surgery. Models were assessed using receiver operating characteristic curves and compared using the DeLong test.
RESULTS
Of the 230 included patients, 52 died and 178 survived. Models were developed using the training cohort (n = 161) and assessed using the validation cohort (n = 68). The areas under the receiver operating characteristic curves for the nomogram, decision-tree, random-forest, gradient-boosting tree, support vector machine, and naïve Bayesian models were 0.908 [95% confidence interval (CI): 0.824-0.992], 0.874 (95%CI: 0.785-0.963), 0.928 (95%CI: 0.869-0.987), 0.907 (95%CI: 0.837-0.976), 0.983 (95%CI: 0.959-1.000), and 0.807 (95%CI: 0.702-0.911), respectively.
CONCLUSION
Nomogram, random-forest, gradient-boosting tree, and support vector machine models all demonstrate strong performances for the prediction of postoperative mortality and can be selected based on the clinical circumstances.
Core Tip: Individuals vary in terms of surgical risk, and assessments must be performed to evaluate this risk in order to provide all patients with the most appropriate perioperative care. Current risk assessments can be time consuming; we therefore aimed to use artificial intelligence to develop a model to predict the risk of 30-day mortality in patients undergoing abdominal surgery. Data from patients that underwent abdominal surgery in our hospital were used to construct six separate models with different machine learning algorithms. Four of the models demonstrated strong predictive performance, suggesting their potential clinical application.
