Development and validation of a predictive scoring system for post-transarterial chemoembolization pain management in liver cancer patients

Abstract

Background Patients experiencing severe postoperative pain often show lower adherence to prescribed treatments, highlighting the clinical need for effective pain prediction and management strategies. This study aims to address this gap by identifying key risk factors associated with post-transarterial chemoembolization (TACE) pain and developing a predictive scoring system. Methods We retrospectively analyzed data from liver cancer patients who underwent their first TACE procedure at our institution between January 2019 and December 2020. Pain levels were assessed using an 11-point numerical rating scale (NRS-11). Patients were randomly assigned to training and validation cohorts. In the training cohort, logistic regression was used to evaluate the correlation between various parameters and post-TACE pain, leading to the development of a risk prediction model. This model’s performance was subsequently assessed in the validation cohort. Results The study included 255 patients. Univariate analysis in the training cohort identified tumor number, size, microsphere volume, and operation time as factors associated with postoperative pain. These factors were included in a multivariate model, which demonstrated areas under the receiver operating characteristic (ROC) curve (AUCs) of 0.71 in the training cohort and 0.74 in the validation cohort for predicting moderate to severe pain. A nomogram was also developed for clinical application, categorizing patients with scores above 72.90 as high risk for moderate to severe pain. Conclusions Our research successfully developed and validated a novel scoring system capable of predicting moderate to severe pain following initial TACE treatment. However, the study’s predictive accuracy, as reflected by AUC values, suggests that further refinement and validation in larger, diverse cohorts are necessary to enhance its clinical utility. This work underscores the importance of predictive tools in improving postoperative pain management and patient outcomes.