Cai LQ, Yang DQ, Wang RJ, Huang H, Shi YX. Establishing and clinically validating a machine learning model for predicting unplanned reoperation risk in colorectal cancer. World J Gastroenterol 2024; 30(23): 2991-3004 [PMID: 38946868 DOI: 10.3748/wjg.v30.i23.2991]
Corresponding Author of This Article
Yi-Xiong Shi, MD, Attending Doctor, Staff Physician, Department of Colorectal and Anorectal Surgery, The First Affiliated Hospital of Wenzhou Medical University, Nanbaixiang Street, Ouhai District, Wenzhou 325000, Zhejiang Province, China. danshiyixiong@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/
World J Gastroenterol. Jun 21, 2024; 30(23): 2991-3004 Published online Jun 21, 2024. doi: 10.3748/wjg.v30.i23.2991
Establishing and clinically validating a machine learning model for predicting unplanned reoperation risk in colorectal cancer
Li-Qun Cai, Da-Qing Yang, Rong-Jian Wang, He Huang, Yi-Xiong Shi
Li-Qun Cai, Da-Qing Yang, Rong-Jian Wang, He Huang, Department of Colorectal and Anal Surgery, Whenzhou Central Hospital, Wenzhou 325000, Zhejiang Province, China
Yi-Xiong Shi, Department of Colorectal and Anorectal Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, Zhejiang Province, China
Author contributions: Cai LQ designed the study; Cai LQ, Yang DQ, Wang RJ, and He H performed the study; Yang DQ, Wang RJ collected the data; Cai LQ and Shi YX analysed the data and wrote the manuscript; all authors read and approved the final manuscript.
Institutional review board statement: This study has been reviewed and approved by the Clinical Research Ethics Committee of Wenzhou Central Hospital and the First Hospital Affiliated to Wenzhou Medical University, No. KY2024-R016.
Informed consent statement: The present study was retrospective and a dispensation from informed consent has been requested.
Conflict-of-interest statement: All authors declare that there is no conflict of interest involved in this study.
Data sharing statement: Data can be obtained by contacting 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: Yi-Xiong Shi, MD, Attending Doctor, Staff Physician, Department of Colorectal and Anorectal Surgery, The First Affiliated Hospital of Wenzhou Medical University, Nanbaixiang Street, Ouhai District, Wenzhou 325000, Zhejiang Province, China. danshiyixiong@163.com
Received: March 25, 2024 Revised: May 7, 2024 Accepted: May 20, 2024 Published online: June 21, 2024 Processing time: 87 Days and 14.5 Hours
Abstract
BACKGROUND
Colorectal cancer significantly impacts global health, with unplanned reoperations post-surgery being key determinants of patient outcomes. Existing predictive models for these reoperations lack precision in integrating complex clinical data.
AIM
To develop and validate a machine learning model for predicting unplanned reoperation risk in colorectal cancer patients.
METHODS
Data of patients treated for colorectal cancer (n = 2044) at the First Affiliated Hospital of Wenzhou Medical University and Wenzhou Central Hospital from March 2020 to March 2022 were retrospectively collected. Patients were divided into an experimental group (n = 60) and a control group (n = 1984) according to unplanned reoperation occurrence. Patients were also divided into a training group and a validation group (7:3 ratio). We used three different machine learning methods to screen characteristic variables. A nomogram was created based on multifactor logistic regression, and the model performance was assessed using receiver operating characteristic curve, calibration curve, Hosmer-Lemeshow test, and decision curve analysis. The risk scores of the two groups were calculated and compared to validate the model.
RESULTS
More patients in the experimental group were ≥ 60 years old, male, and had a history of hypertension, laparotomy, and hypoproteinemia, compared to the control group. Multiple logistic regression analysis confirmed the following as independent risk factors for unplanned reoperation (P < 0.05): Prognostic Nutritional Index value, history of laparotomy, hypertension, or stroke, hypoproteinemia, age, tumor-node-metastasis staging, surgical time, gender, and American Society of Anesthesiologists classification. Receiver operating characteristic curve analysis showed that the model had good discrimination and clinical utility.
CONCLUSION
This study used a machine learning approach to build a model that accurately predicts the risk of postoperative unplanned reoperation in patients with colorectal cancer, which can improve treatment decisions and prognosis.
Core Tip: This study developed a machine learning model to predict unplanned reoperations in colorectal cancer patients, using data from two hospitals over two years. It employed support vector machine, least absolute shrinkage and selection operator, and extreme gradient boosting for feature selection and logistic regression to identify key risk factors. The model showed good predictive accuracy, validated by receiver operating characteristic curves, calibration curves, and decision curve analysis. Key predictors included age, gender, prior surgeries, and nutritional status. This predictive tool aims to enhance clinical decision-making, reduce reoperation rates, and improve patient outcomes in colorectal cancer care.
