Constructing a nomogram for forecasting incomplete clinical recovery following unilateral biportal endoscopy in lumbar disc herniation cases.

Background: Unilateral dual channel endoscopy (UBE) has unique advantages in lumbar degenerative lesions, but some patients show incomplete clinical symptom relief postoperatively, prolonging hospital stay and delaying recovery. In the existing studies, there is no design study on the nomogram model of incomplete clinical improvement after UBE surgery for lumbar disc herniation. Therefore, in order to accurately predict the occurrence of incomplete clinical improvement after UBE, this study constructed a nomogram model, which has significant predictive ability and clinical utility, and can provide great help for clinical decision-making. Methods: This study was based on the data of UBE operation in Jiangxi Province Hospital of Integrated Chinese & Western Medicine for lumbar disc herniation from January 2021 to December 2024. In this study, 290 patients' data were divided into training set and validation set according to the ratio of 7:3. In the training set, we screened out the risk factors related to clinical research through statistical analysis, further identified the relevant independent risk factors through multiple logistic regression, and constructed a nomogram model based on the results. In the verification process of the model, ROC curve, decision curve DCA and calibration curve are used to determine whether it has reliability in the actual scene. Results: Through statistical analysis, this study screened out four indicators that were not completely related to the clinical improvement after UBE and built a nomogram model based on this: Increased body mass index (BMI), higher preoperative visual analogue scale (VAS-B) for back pain, increased preoperative Oswestry disability index (ODI) score, and presence of facet joint osteoarthritis (FJOA). The C-index of the training set and the validation set were 0.86 [95% confidence interval (CI): 0.76-0.95] and 0.92 (95% CI: 0.85-0.99). Conclusions: The key predictors identified in this study included high body mass index (BMI), elevated preoperative visual analogue scale (VAS-B) for back pain, elevated preoperative Oswestry disability index (ODI), and the presence of small joint osteoarthritis (FJOA). The prediction ability of this model is excellent, which can help clinicians make clinical decisions to a certain extent, so that timely intervention can be carried out to optimize the postoperative outcome.