Case Control Study
Copyright ©The Author(s) 2023. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastroenterol. May 21, 2023; 29(19): 2979-2991
Published online May 21, 2023. doi: 10.3748/wjg.v29.i19.2979
Machine learning model for prediction of low anterior resection syndrome following laparoscopic anterior resection of rectal cancer: A multicenter study
Zhang Wang, Sheng-Li Shao, Lu Liu, Qi-Yi Lu, Lei Mu, Ji-Chao Qin
Zhang Wang, Sheng-Li Shao, Lu Liu, Qi-Yi Lu, Lei Mu, Ji-Chao Qin, Department of Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China
Zhang Wang, Sheng-Li Shao, Lu Liu, Qi-Yi Lu, Lei Mu, Ji-Chao Qin, Molecular Medicine Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China
Author contributions: Wang Z and Shao SL contributed equally to this work; Wang Z contributed to methodology, formal analysis, data extraction, follow-up, writing, reviewing, and editing; Shao SL contributed to data extraction, data curation, follow-up, formal analysis, writing, reviewing, and editing; Liu L was involved in supervision and software; Lu QY contributed to follow-up and data curation; Mu L performed data curation; Qin JC contributed to conceptualization, funding acquisition, methodology, writing, reviewing, and editing; All authors contributed to the interpretation of the study and approved the final version to be published.
Supported by the National Natural Science Foundation of China, No. 82173368 and 81903047.
Institutional review board statement: This study was supported by the Ethics Committee of Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, and the Central Hospital of Enshi Tujia and Miao Autonomous Prefecture.
Informed consent statement: The requirement for informed consent was waived due to the retrospective nature of the study.
Conflict-of-interest statement: All the authors report having no relevant conflicts of interest for this article.
Data sharing statement: The dataset used during the current study is available from the corresponding author on reasonable request. E-mail: jcqin@tjh.tjmu.edu.cn.
STROBE statement: The authors have read the STROBE statement-checklist of items, and the manuscript was prepared and revised according to the STROBE statement-checklist of items.
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: Ji-Chao Qin, MD, PhD, Professor, Researcher, Surgeon, Department of Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, No. 1095 Jiefang Avenue, Wuhan 430030, Hubei Province, China. jcqin@tjh.tjmu.edu.cn
Received: February 23, 2023
Peer-review started: February 23, 2023
First decision: March 23, 2023
Revised: April 2, 2023
Accepted: April 25, 2023
Article in press: April 25, 2023
Published online: May 21, 2023
Processing time: 82 Days and 5.3 Hours
ARTICLE HIGHLIGHTS
Research background

Low anterior resection syndrome (LARS) severely impairs patient postoperative quality of life, especially major LARS. However, there are few tools that can accurately predict major LARS in clinical practice.

Research motivation

To stratify patients with LARS and predict patients at high risk of developing major LARS, improve patient counseling, and highlight patients who may need additional support after surgery.

Research objectives

The study aimed to identify the risk factors associated with major LARS and develop a prediction model that helps improve patient counseling and highlight patients who may need additional support after surgery.

Research methods

Clinical data and follow-up information of patients from two medical centers (one discovery cohort and one external validation cohort) were analyzed to identify independent factors associated with major LARS. For the discovery cohort, the machine learning prediction algorithms were developed and internally validated. In the external validation cohort, we evaluated the trained model using various performance metrics. Further, the clinical utility of the model was tested by decision curve analysis.

Research results

Eight factors, such as anastomotic height, neoadjuvant therapy, diverting stoma, body mass index, clinical stage, specimen length, tumor size, and age, were selected as significantly relevant to major LARS. A machine learning-based prediction model that integrated eight risk factors as input features was developed, externally validated, and demonstrated an acceptable predictive performance.

Research conclusions

We have developed and validated a robust tool for predicting major LARS. This model could potentially be used in the clinic to identify patients with a high risk of developing major LARS and then improve their quality of life.

Research perspectives

A prospective study including more medical centers is proposed to assess the model’s predictive ability.