Retrospective Cohort Study
Copyright ©The Author(s) 2020. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastroenterol. Aug 14, 2020; 26(30): 4442-4452
Published online Aug 14, 2020. doi: 10.3748/wjg.v26.i30.4442
Predictive model for acute abdominal pain after transarterial chemoembolization for liver cancer
Li-Fang Bian, Xue-Hong Zhao, Bei-Lei Gao, Sheng Zhang, Guo-Mei Ge, Dong-Di Zhan, Ting-Ting Ye, Yan Zheng
Li-Fang Bian, Bei-Lei Gao, Guo-Mei Ge, Dong-Di Zhan, Ting-Ting Ye, Yan Zheng, Department of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310003, Zhejiang Province, China
Xue-Hong Zhao, Department of Nursing, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310003, Zhejiang Province, China
Sheng Zhang, Department of Hospital Infection Control, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310003, Zhejiang Province, China
Author contributions: Bian LF and Zhao XH contributed equally to this work; Bian LF and Zhao XH designed the research; Bian LF, Gao BL, Zhan DD, Ge GM, Ye TT, and Zheng Y performed the research; Zhang S analyzed the data; Bian LF and Zhao XH wrote the paper.
Supported by Medical Health Science and Technology Project of Zhejiang Provincial Health Commission, China, No. 2020372769.
Institutional review board statement: This single-center retrospective study was approved as an expedited chart review study and obtained ethical approval from the institutional review board of the First Affiliated Hospital, Zhejiang University School of Medicine.
Informed consent statement: This was a retrospective study and exemption from the need for signed informed consent was approved by the Institutional Review Board of the First Affiliated Hospital, Zhejiang University School of Medicine.
Conflict-of-interest statement: All authors have no conflict of interest related to this manuscript.
Data sharing statement: No additional data are available.
STROBE statement: The authors have read the STROBE Statement-checklist of items, and the manuscript was prepared and revised according to the STROBE Statement-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: http://creativecommons.org/licenses/by-nc/4.0/
Corresponding author: Li-Fang Bian, MSN, Associate Chief Nurse, Department of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital and Zhejiang University School of Medicine, No. 79 Qingchun Road, Hangzhou 310003, Zhejiang Province, China. doggie_cc@zju.edu.cn
Received: April 8, 2020
Peer-review started: April 9, 2020
First decision: May 26, 2020
Revised: July 8, 2020
Accepted: July 18, 2020
Article in press: July 18, 2020
Published online: August 14, 2020
Processing time: 127 Days and 8.6 Hours
Abstract
BACKGROUND

Transarterial chemoembolization (TACE) is the first-line treatment for patients with unresectable liver cancer; however, TACE is associated with postembolization pain.

AIM

To analyze the risk factors for acute abdominal pain after TACE and establish a predictive model for postembolization pain.

METHODS

From January 2018 to September 2018, all patients with liver cancer who underwent TACE at our hospital were included. General characteristics; clinical, imaging, and procedural data; and postembolization pain were analyzed. Postembolization pain was defined as acute moderate-to-severe abdominal pain within 24 h after TACE. Logistic regression and a classification and regression tree were used to develop a predictive model. Receiver operating characteristic curve analysis was used to examine the efficacy of the predictive model.

RESULTS

We analyzed 522 patients who underwent a total of 582 TACE procedures. Ninety-seven (16.70%) episodes of severe pain occurred. A predictive model built based on the dataset from classification and regression tree analysis identified known invasion of blood vessels as the strongest predictor of subsequent performance, followed by history of TACE, method of TACE, and history of abdominal pain after TACE. The area under the receiver operating characteristic curve was 0.736 [95% confidence interval (CI): 0.682-0.789], the sensitivity was 73.2%, the specificity was 65.6%, and the negative predictive value was 92.4%. Logistic regression produced similar results by identifying age [odds ratio (OR) = 0.971; 95%CI: 0.951-0.992; P = 0.007), history of TACE (OR = 0.378; 95%CI: 0.189-0.757; P = 0.007), history of abdominal pain after TACE (OR = 6.288; 95%CI: 2.963-13.342; P < 0.001), tumor size (OR = 1.978; 95%CI: 1.175-3.330; P = 0.01), multiple tumors (OR = 2.164; 95%CI: 1.243-3.769; P = 0.006), invasion of blood vessels (OR = 1.756; 95%CI: 1.045-2.950; P = 0.034), and TACE with drug-eluting beads (DEB-TACE) (OR = 2.05; 95%CI: 1.260-3.334; P = 0.004) as independent predictive factors for postembolization pain.

CONCLUSION

Blood vessel invasion, TACE history, TACE with drug-eluting beads, and history of abdominal pain after TACE are predictors of acute moderate-to-severe pain. The predictive model may help medical staff to manage pain.

Keywords: Liver cancer; Predictive model; Pain; Transarterial chemoembolization; Postembolization syndrome

Core tip: Transarterial chemoembolization (TACE) is associated with postembolization pain. We analyzed the risk factors for acute abdominal pain after TACE and established a predictive model for it. The predictive model built based on the dataset from a classification and regression tree identified known invasion of blood vessels as the strongest predictor of subsequent performance, followed by history of TACE, method of TACE, and history of abdominal pain after TACE. Our predictive model is simple to use and provides a more rational reference to improve the quality of pain management after TACE.