Li J, Jia YM, Zhang ZL, Liu CY, Jiang ZW, Hao ZW, Peng L. Development and validation of a machine learning-based early prediction model for massive intraoperative bleeding in patients with primary hepatic malignancies. World J Gastrointest Oncol 2024; 16(1): 90-101 [PMID: 38292843 DOI: 10.4251/wjgo.v16.i1.90]
Corresponding Author of This Article
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
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 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
ARTICLE HIGHLIGHTS
Research background
Surgical removal is the primary treatment for hepatic malignancies, but intraoperative bleeding poses a significant risk to patients with hepatic malignancies. Accurate prediction of intraoperative bleeding is crucial for preventing complications and improving treatment outcomes.
Research motivation
This study aimed to develop a predictive model for significant intraoperative blood loss in patients with primary liver malignancies. By identifying preoperative factors associated with intraoperative bleeding, we can improve surgical planning and provide safer and more effective treatment for these patients.
Research objectives
This study aimed to identify risk factors for intraoperative bleeding in primary liver malignancies and develop a predictive model to estimate significant intraoperative blood loss.
Research methods
A retrospective analysis was conducted on primary liver malignancy patients who underwent surgery at the Hepatobiliary Surgery Department of the Fourth Hospital of Hebei Medical University between 2010 and 2020. Logistic regression analysis was used to identify risk factors for intraoperative bleeding. A predictive model was developed using Python programming. The model’s accuracy was evaluated using receiver operating characteristic (ROC) curve analysis.
Research results
Among 406 patients with primary liver cancer, 16.0% (65/406) experienced significant intraoperative bleeding. Logistic regression analysis revealed four variables that were significantly associated with intraoperative bleeding: Ascites, alcohol consumption history, TNM staging, and albumin-bilirubin score. These variables were utilized to construct a predictive model. The model demonstrated good predictive accuracy, as evidenced by area under the ROC curve values of 0.844 in the training set and 0.80 in the prediction set.
Research conclusions
This study successfully developed and validated a predictive model for significant intraoperative blood loss in patients with primary liver malignancies, using preoperative clinical factors. Implementation of this model has the potential to enhance personalized surgical planning and improve patient outcomes.
Research perspectives
Further research is needed to assess the impact of implementing the predictive model on surgical decision-making, patient safety, and overall clinical outcomes to determine its real-world effectiveness and benefits.
