Published online Mar 27, 2023. doi: 10.4240/wjgs.v15.i3.374
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
Pain after transcatheter arterial chemoembolisation (TACE) can seriously affect the prognosis of patients and the insertion of additional medical resources.
To develop a practical model for predicting pain after TACE.
This study aimed to predict pain after TACE to enable the implementation of preventive analgesic measures.
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.
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].
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.
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.