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
Processing time: 96 Days and 6.6 Hours
Abstract
BACKGROUND

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

AIM

To develop an early warning model for predicting pain after TACE to enable the implementation of preventive analgesic measures.

METHODS

We retrospectively collected the clinical data 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 in the Hepatic Surgery Center of Tongji Hospital. Five predictive models were established using machine learning algorithms, namely, random forest model (RFM), support vector machine model, artificial neural network model, naive Bayes model and decision tree model. The efficacy of these models in predicting postoperative pain was evaluated through receiver operating characteristic curve analysis, decision curve analysis and clinical impact curve analysis.

RESULTS

A total of 24 candidate variables were included in the predictive models using the iterative algorithms. 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 accuracy of the predictive model was compared between the training [area under the curve (AUC) = 0.798; 95% confidence interval (CI): 0.745-0.851] and verification (AUC = 0.871; 95%CI: 0.818-0.924) cohorts, with RFM having the best predictive efficiency (training cohort: AUC = 0.869, 95%CI: 0.816-0.922; internal verification cohort: AUC = 0.871; 95%CI: 0.818-0.924).

CONCLUSION

The five predictive models based on advanced machine learning algorithms, especially RFM, can accurately predict the risk of pain after TACE in patients with HCC. RFM can be used to assess the risk of pain for facilitating preventive treatment and improving the prognosis.

Keywords: Hepatocellular carcinoma; Transcatheter arterial chemoembolization; Pain; Machine learning algorithm; Prediction

Core Tip: Machine learning-based pre-warning models can be used to predict post-transcatheter arterial chemoembolisation (TACE) pain for hierarchical management of patients at high risk of moderate and severe pain after TACE. In particular, random forest model (RFM) combined with preoperative predictors (i.e., age, preoperative pain, distance from liver capsule ≤ 2 cm, prothrombin activity, iodine oil dose and increased number of emboli) has optimal discriminating power and high predictive accuracy. Therefore, RFM can be used for early prediction of the risk of pain, which can facilitate prompt pain management after TACE and improve the prognosis of patients.