Abstract
OBJECTIVE: To develop and validate a nomogram prediction model for chronic pain after hysterectomy for uterine fibroids. METHODS: A retrospective study was conducted on 315 patients who visited our hospital from January 2022 to July 2024. The patients were stochastically assigned into training dataset (n=220) and validation dataset (n=95) in a 7:3 ratio. The training dataset was assigned into non chronic pain group (n=164) and chronic pain group (n=56) based on whether chronic pain persists for more than 3 months after surgery. Multivariate logistic regression was used to screen for predictive factors. R software was used to construct nomogram models. The calibration curve was used to evaluate the calibration degree of nomogram. ROC curve was used to evaluate the discrimination of nomogram. The clinical decision curve analysis was used to discuss and evaluate the net profit of the nomogram. RESULTS: Preoperative pain, history of abdominal or pelvic surgery, endometriosis, anxiety, and high Pain Catastrophic Scale (PCS) score were independent risk factors for chronic pain after hysterectomy for uterine fibroids (P<0.05). The nomogram model showed high predictive performance in both the training and validation datasets, and the calibration curves showed good consistency and calibration degree, the Hosmer-Lemeshow test showed χ (2)=1.654, 3.181, P=0.990, 0.922; the AUC values in ROC curve were 0.841 (95% CI: 0.781~0.900) and 0.825 (95% CI: 0.762~0.887). The clinical decision curve analysis indicated that decisions based on the nomogram model could provide higher net benefits for patients undergoing hysterectomy for uterine fibroids within a prediction probability threshold range of 0.08~0.75. CONCLUSION: The nomogram developed in this study accurately predicts the risk of chronic pain after hysterectomy for uterine fibroids, which is beneficial for preoperative planning and patient consultation.