Risk factors with nomogram construction for moderate to severe pain after endoscopic full-thickness resection.

BACKGROUND: Endoscopic full-thickness resection (EFTR) is an effective treatment for gastrointestinal lesions. Compared with endoscopic submucosal dissection, EFTR is associated with a higher incidence of postoperative pain, particularly moderate to severe pain, which can significantly impact patients' quality of life and recovery. Although some studies have focused on postoperative analgesia, clinical evidence regarding the underlying mechanisms and risk factors of pain after EFTR - especially moderate to severe pain following upper gastrointestinal EFTR - remains limited. AIM: To identify risk factors for moderate to severe pain following EFTR and to construct a predictive nomogram for clinical use. METHODS: We conducted a retrospective analysis of patients who underwent EFTR at our center between October 1, 2019, and June 1, 2025. Univariate and multivariate logistic regression analyses were performed to identify risk factors associated with postoperative moderate to severe pain following EFTR. A nomogram was subsequently constructed based on a multivariate logistic regression model to predict the risk of moderate to severe pain following EFTR. The discrimination and calibration of the nomogram were evaluated by estimating the area under the receiver operator characteristic curve and by bootstrap resampling and visual inspection of the calibration curve. The clinical utility of the nomogram was assessed using decision curve analysis. RESULTS: A total of 172 patients who underwent EFTR were included in the study, of whom 27 (15.7%) experienced moderate to severe postoperative pain. Based on multivariate logistic regression analysis, higher body mass index was significantly associated with a reduced risk of moderate to severe postoperative pain [odds ratio (OR) = 0.83, 95% confidence interval (CI): 0.72-0.95, P = 0.0091], while a lesion size ≥ 3 cm (OR = 12.01, 95%CI: 3.03-47.68, P = 0.0004) and benign lesions (OR = 12.12, 95%CI: 2.70-54.49, P = 0.0011) were significantly associated with an increased risk. The nomogram demonstrated excellent discriminatory ability, with an area under the curve of 0.792 (95%CI: 0.690-0.894), a sensitivity of 63%, and a specificity of 84%. The calibration curve showed excellent agreement between predicted and observed probabilities (mean absolute error = 0.022). Subsequent decision curve analysis further confirmed the nomogram's clinical utility. CONCLUSION: In this study, we successfully developed a predictive nomogram for identifying the risk of moderate to severe pain following EFTR surgery.