Retrospective Study
Copyright ©The Author(s) 2024. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastrointest Oncol. Jan 15, 2024; 16(1): 90-101
Published online Jan 15, 2024. doi: 10.4251/wjgo.v16.i1.90
Development and validation of a machine learning-based early prediction model for massive intraoperative bleeding in patients with primary hepatic malignancies
Jin Li, Yu-Ming Jia, Zhi-Lei Zhang, Cheng-Yu Liu, Zhan-Wu Jiang, Zhi-Wei Hao, Li Peng
Jin Li, Yu-Ming Jia, Zhi-Lei Zhang, Cheng-Yu Liu, Li Peng, Department of Hepatological Surgery, The Fourth Hospital of Hebei Medical University, Shijiazhuang 050011, Hebei Province, China
Zhan-Wu Jiang, Zhi-Wei Hao, Department of General Surgery II, Baoding First Central Hospital, Baoding 071000, Hebei Province, China
Author contributions: Li J, Peng L and Jia YM conceived and designed research; Zhang ZL, Liu CY and Jiang ZW collected data and conducted research; Li J, Peng L and Hao ZW analyzed and interpreted data; Li J, Jia YM and Peng L wrote the initial draft; Li J and Peng L revised the manuscript; Li J had primary responsibility for final content; all authors read and approved the final version of the manuscript.
Institutional review board statement: This study was approved by the Ethics Committee of the Fourth Hospital of Hebei Medical University. All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and national research committee and with the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards.
Informed consent statement: Due to the retrospective study design, the need for written, informed patient consent was waived by the hospital ethics committee.
Conflict-of-interest statement: The authors declare that they have no conflict of interest.
Data sharing statement: The datasets generated and analyzed during the current study are not publicly available due to limitations of ethical approval involving the patient data and anonymity but 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: Li Peng, MD, Director, Doctor, Department of Hepatological Surgery, The Fourth Hospital of Hebei Medical University, No. 12 Health Road, Chang’an District, Shijiazhuang 050011, Hebei Province, China. pengli5555555@163.com
Received: August 22, 2023
Peer-review started: August 22, 2023
First decision: September 26, 2023
Revised: October 12, 2023
Accepted: December 1, 2023
Article in press: December 1, 2023
Published online: January 15, 2024
Processing time: 142 Days and 0.8 Hours
Abstract
BACKGROUND

Surgical resection remains the primary treatment for hepatic malignancies, and intraoperative bleeding is associated with a significantly increased risk of death. Therefore, accurate prediction of intraoperative bleeding risk in patients with hepatic malignancies is essential to preventing bleeding in advance and providing safer and more effective treatment.

AIM

To develop a predictive model for intraoperative bleeding in primary hepatic malignancy patients for improving surgical planning and outcomes.

METHODS

The retrospective analysis enrolled patients diagnosed with primary hepatic malignancies who underwent surgery at the Hepatobiliary Surgery Department of the Fourth Hospital of Hebei Medical University between 2010 and 2020. Logistic regression analysis was performed to identify potential risk factors for intraoperative bleeding. A prediction model was developed using Python programming language, and its accuracy was evaluated using receiver operating characteristic (ROC) curve analysis.

RESULTS

Among 406 primary liver cancer patients, 16.0% (65/406) suffered massive intraoperative bleeding. Logistic regression analysis identified four variables as associated with intraoperative bleeding in these patients: ascites [odds ratio (OR): 22.839; P < 0.05], history of alcohol consumption (OR: 2.950; P < 0.015), TNM staging (OR: 2.441; P < 0.001), and albumin-bilirubin score (OR: 2.361; P < 0.001). These variables were used to construct the prediction model. The 406 patients were randomly assigned to a training set (70%) and a prediction set (30%). The area under the ROC curve values for the model’s ability to predict intraoperative bleeding were 0.844 in the training set and 0.80 in the prediction set.

CONCLUSION

The developed and validated model predicts significant intraoperative blood loss in primary hepatic malignancies using four preoperative clinical factors by considering four preoperative clinical factors: ascites, history of alcohol consumption, TNM staging, and albumin-bilirubin score. Consequently, this model holds promise for enhancing individualised surgical planning.

Keywords: Primary liver cancer; Intraoperative bleeding; Machine learning; Model

Core Tip: A prediction model for significant intraoperative blood loss in patients with primary hepatic malignancies was constructed in this retrospective analysis. Logistic regression analysis identified four preoperative clinical factors associated with intraoperative bleeding: ascites, history of alcohol consumption, TNM staging, and albumin-bilirubin score. These factors were used to construct a prediction model that demonstrated good accuracy in assessing the risk of intraoperative bleeding. Implementation of this model has the potential to enhance personalized surgical planning, leading to safer and more effective treatment for patients with hepatic malignancies.