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Predictive model for acute abdominal pain after transarterial chemoembolization for liver cancer

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 postembolizati...

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Autores principales: Bian, Li-Fang, Zhao, Xue-Hong, Gao, Bei-Lei, Zhang, Sheng, Ge, Guo-Mei, Zhan, Dong-Di, Ye, Ting-Ting, Zheng, Yan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Baishideng Publishing Group Inc 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7438199/
https://www.ncbi.nlm.nih.gov/pubmed/32874056
http://dx.doi.org/10.3748/wjg.v26.i30.4442
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author Bian, Li-Fang
Zhao, Xue-Hong
Gao, Bei-Lei
Zhang, Sheng
Ge, Guo-Mei
Zhan, Dong-Di
Ye, Ting-Ting
Zheng, Yan
author_facet Bian, Li-Fang
Zhao, Xue-Hong
Gao, Bei-Lei
Zhang, Sheng
Ge, Guo-Mei
Zhan, Dong-Di
Ye, Ting-Ting
Zheng, Yan
author_sort Bian, Li-Fang
collection PubMed
description 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.
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spelling pubmed-74381992020-08-31 Predictive model for acute abdominal pain after transarterial chemoembolization for liver cancer Bian, Li-Fang Zhao, Xue-Hong Gao, Bei-Lei Zhang, Sheng Ge, Guo-Mei Zhan, Dong-Di Ye, Ting-Ting Zheng, Yan World J Gastroenterol Retrospective Cohort Study 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. Baishideng Publishing Group Inc 2020-08-14 2020-08-14 /pmc/articles/PMC7438199/ /pubmed/32874056 http://dx.doi.org/10.3748/wjg.v26.i30.4442 Text en ©The Author(s) 2020. Published by Baishideng Publishing Group Inc. All rights reserved. http://creativecommons.org/licenses/by-nc/4.0/ 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.
spellingShingle Retrospective Cohort Study
Bian, Li-Fang
Zhao, Xue-Hong
Gao, Bei-Lei
Zhang, Sheng
Ge, Guo-Mei
Zhan, Dong-Di
Ye, Ting-Ting
Zheng, Yan
Predictive model for acute abdominal pain after transarterial chemoembolization for liver cancer
title Predictive model for acute abdominal pain after transarterial chemoembolization for liver cancer
title_full Predictive model for acute abdominal pain after transarterial chemoembolization for liver cancer
title_fullStr Predictive model for acute abdominal pain after transarterial chemoembolization for liver cancer
title_full_unstemmed Predictive model for acute abdominal pain after transarterial chemoembolization for liver cancer
title_short Predictive model for acute abdominal pain after transarterial chemoembolization for liver cancer
title_sort predictive model for acute abdominal pain after transarterial chemoembolization for liver cancer
topic Retrospective Cohort Study
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7438199/
https://www.ncbi.nlm.nih.gov/pubmed/32874056
http://dx.doi.org/10.3748/wjg.v26.i30.4442
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