Retrospective Study
Copyright ©The Author(s) 2023. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastrointest Surg. Oct 27, 2023; 15(10): 2201-2210
Published online Oct 27, 2023. doi: 10.4240/wjgs.v15.i10.2201
Establishment and application of three predictive models of anastomotic leakage after rectal cancer sphincter-preserving surgery
Hui-Yuan Li, Jiang-Tao Zhou, Ya-Nan Wang, Ning Zhang, Shao-Fen Wu
Hui-Yuan Li, Jiang-Tao Zhou, Ya-Nan Wang, Ning Zhang, Department of General Surgery, Jincheng People’s Hospital of Shanxi Province, Jincheng 048026, Shanxi Province, China
Shao-Fen Wu, Department of Gastroenterology, Jincheng People’s Hospital of Shanxi Province, Jincheng 048026, Shanxi Province, China
Author contributions: Li HY designed the study and wrote the manuscript; Wu SF designed the study and reviewed the manuscript; Zhou JT, Wang YN, and Zhang N provided clinical advice.
Institutional review board statement: The study was reviewed and approved by the Jincheng People’s Hospital of Shanxi Province (JCPH.No20230407001).
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: All the authors report no relevant conflicts of interest for this article.
Data sharing statement: Dataset available from the corresponding author at wushaofen3322@163.com.
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: Shao-Fen Wu, BSc, Nurse, Department of Gastroenterology, Jincheng People’s Hospital of Shanxi Province, No. 1168 Baishui East Street, Jincheng 048026, Shanxi Province, China. wushaofen3322@163.com
Received: July 12, 2023
Peer-review started: July 12, 2023
First decision: August 2, 2023
Revised: August 9, 2023
Accepted: August 18, 2023
Article in press: August 18, 2023
Published online: October 27, 2023
ARTICLE HIGHLIGHTS
Research background

With advances in medical technology, the success rate of sphincter-preserving surgery in patients with rectal cancer is increasing. However, anastomotic leakage (AL) remains a devastating complication.

Research motivation

AL significantly lowers patients’ quality of life. This study examines the elements that influence AL and establishes models to help doctors predict whether patients will develop AL, allowing the timely adoption of preventive measures.

Research objectives

This study aimed to identify the characteristics that influence AL and utilize these factors to build a prediction model for AL after sphincter-preserving surgery for rectal cancer.

Research methods

The clinical data of patients with rectal cancer who underwent sphincter-preserving surgery at our institution in the past five years were examined to analyze the factors influencing AL; nomogram, decision tree, and random forest prediction models were established; and the predictive efficacy of the three models was compared.

Research results

The factors influencing AL after sphincter-preserving surgery for rectal cancer were sex, diabetes mellitus, albumin level, tumor size, and tumor location. To predict the probability of postoperative AL, we constructed nomogram, decision tree, and random forest models.

Research conclusions

This study compared the predictive efficacy of the three prediction models. The random forest model performed the best and may be a useful alternative tool for predicting patients at a high risk of AL.

Research perspectives

Future research will include larger and more comprehensive cohorts across multiple centers, and build a more complete prediction model.