Abstract
A systematic retrospective analysis of patients with lumbar disc herniation treated with percutaneous endoscopic discectomy was performed to identify key risk factors for postoperative recurrence, and a Nomogram prediction model was constructed based on them. The data of patients with lumbar disc herniation who were treated in our hospital between January 2021 and December 2023 were included in this study. Statistical tools, including univariate and multivariate logistic regression analyses, were used to accurately screen independent risk factors significantly associated with postoperative recurrence. Based on this, a nomogram prediction model was constructed to enable personalized prediction of postoperative recurrence risk. The model performance was evaluated by plotting the receiver operating characteristic curve and calculating the area under the curve, supplemented by calibration curve and decision curve analysis, to ensure the predictive accuracy and clinical practicability of the model. 286 patients with lumbar disc herniation were included in the study, and 29 patients had a postoperative recurrence, with a recurrence rate of 10.14%. After univariate and multivariate logistic regression analyses, a total of 5 variables were identified as independent risk factors for postoperative recurrence of lumbar disc herniation: age > 60 years (OR = 2.831; 95% CI = 1.089-5.430), body mass index (BMI) > 24 kg/m2 (OR = 4.632; 95% CI = 1.183-14.337), The type of lumbar disc herniation was herniation (OR = 5.064; 95% CI = 1.198-15.364), degeneration grade III-IV (OR = 5.916; 95% CI = 1.357-16.776), and postoperative high-intensity activity (OR = 4.731; 95% CI = 1.341-14.024). The nomogram constructed in this study for postoperative recurrence of lumbar disc herniation by percutaneous endoscopic discectomy has good predictive accuracy, and this tool can effectively assist orthopedic surgeons in identifying high-risk patients with recurrence after percutaneous endoscopic discectomy, providing a scientific basis for early intervention and individualized management strategies, thus optimizing patient prognosis.