Published online Aug 14, 2020. doi: 10.3748/wjg.v26.i30.4442
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
Transarterial chemoembolization (TACE) is the first-line treatment for patients with unresectable liver cancer. However, approximately 60%-80% of patients complain of different levels of postembolization pain after TACE.
Clearly identifying factors associated with postembolization pain could help predict its occurrence. Prediction model could be used to predict the risk of abdominal pain after TACE, thus providing medical staff with a reference for pain management.
To analyze the risk factors for acute abdominal pain after TACE and establish a predictive model for postembolization pain.
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.
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, the sensitivity was 73.2%, the specificity was 65.6%, and the negative predictive value was 92.4%.
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. Our predictive model is simple to use and provides a more rational reference to improve the quality of pain management.
Future studies with a larger sample size, a multicenter design, and using an external cohort are needed to confirm our findings.