Retrospective Cohort Study
Copyright ©The Author(s) 2023. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastrointest Surg. Mar 27, 2023; 15(3): 374-386
Published online Mar 27, 2023. doi: 10.4240/wjgs.v15.i3.374
Pain management in patients with hepatocellular carcinoma after transcatheter arterial chemoembolisation: A retrospective study
Yan Guan, Ye Tian, Ya-Wei Fan
Yan Guan, Ye Tian, Ya-Wei Fan, Hepatic Surgery Center, Department of Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China
Author contributions: Guan Y and Tian Y contributed equally to this work; Fan YW designed the research study; Guan Y and Tian Y performed the research; Guan Y, Tian Y and Fan YW analyzed the data and wrote the manuscript; and all authors have read and approve the final manuscript.
Institutional review board statement: This study has been approved by the Ethics Committee of Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology.
Informed consent statement: All patients with hepatocellular carcinoma who participated in this study signed an informed consent form.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
Data sharing statement: No additional data are available.
STROBE statement: The authors have read the STROBE Statement-a checklist of items, and the manuscript was prepared and revised according to the STROBE Statement-a 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: Ya-Wei Fan, MD, Nurse, Hepatic Surgery Center, Department of Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, No. 116 Zhuodaoquan South Road, Hongshan District, Wuhan 430030, Hubei Province, China. fanyaweitj2022@163.com
Received: December 21, 2022
Peer-review started: December 21, 2022
First decision: January 3, 2023
Revised: January 11, 2023
Accepted: February 15, 2023
Article in press: February 15, 2023
Published online: March 27, 2023
ARTICLE HIGHLIGHTS
Research background

Pain after transcatheter arterial chemoembolisation (TACE) can seriously affect the prognosis of patients and the insertion of additional medical resources.

Research motivation

To develop a practical model for predicting pain after TACE.

Research objectives

This study aimed to predict pain after TACE to enable the implementation of preventive analgesic measures.

Research methods

Of 857 patients (from January 2016 to January 2020) and prospectively enrolled 368 patients (from February 2020 to October 2022; as verification cohort) with hepatocellular carcinoma (HCC) who received TACE were collected from the Hepatic Surgery Center of Tongji Hospital. Five predictive models were established using machine learning algorithms were used to predicting postoperative pain and receiver operating characteristic curve analysis, decision curve analysis and clinical impact curve analysis were used to evaluate the effectiveness of the model.

Research results

Of 24 candidate variables were to build prediction model, among them, the age, preoperative pain, number of embolised tumours, distance from the liver capsule, dosage of iodised oil and preoperative prothrombin activity were closely associated with postoperative pain. The random forest model (RFM) had the best predictive efficiency [training cohort: Area under the curve (AUC) = 0.869, 95% confidence interval (CI): 0.816-0.922; internal verification cohort: AUC = 0.871; 95%CI: 0.818-0.924].

Research conclusions

The five prediction models based on advanced machine learning algorithms are extremely suitable for the pain management of liver cancer patients after TACE, especially the RFM can accurately classify the pain risk of patients.

Research perspectives

Machine learning-based pre-warning models can be developed to predict post-TACE pain for hierarchical management of patients at high risk of moderate and severe pain after TACE. Alarmingly, the RFM can be used for early prediction of the risk of postoperative pain, which can facilitate prompt pain management after TACE and improve the prognosis of patients.