Establishment and Validation of a Risk Prediction Model for the Superior Cluneal Nerve Entrapment Neuropathy after Thoracic or Lumbar Fracture: A Case-Comparative Study

Background: Superior cluneal nerve entrapment neuropathy (SCNEN) is a known complication following spinal fractures. There is a lack of comprehensive understanding of this condition, and clinical management strategies remain underdeveloped. Methods: This study analysed the demographic and radiological data of 340 patients to develop a risk prediction model for SCNEN following surgery for thoracic or lumbar vertebral fractures. Patients were randomized into a derivation cohort (70%) and a validation cohort (30%). The least absolute shrinkage and selection operator (LASSO) regression was applied for variable selection, followed by tenfold cross-validation. Multivariate logistic regression was performed on the identified predictive variables. The model's accuracy and clinical utility were assessed through the receiver operating characteristic (ROC) curves, the calibration curves, and the decision curve analysis (DCA). A rationality analysis was also performed to evaluate the model's diagnostic performance. Results: This study analysed 19 variables; following analysis using LASSO regression and multivariate logistic regression, it was found that the surgical segment (OR=4.993, 95% Cl 2.053-14.200, P=0.001), surgical method (OR=0.549, 95% Cl 0.322-0.936, P=0.027), the anteroposterior bone cement distribution ratio (OR=0.956, 95% Cl 0.928-0.981, P=0.001), and Cobb angle restoration ratio (OR=0.973, 95% Cl 0.952-0.993, P=0.011) were independent predictors of SCNEN (P < 0.05). We developed a nomogram. The AUC for the derivation cohort was 0.795, while the AUC for the validation cohort was 0.806. The Hosmer-Lemeshow test indicated good model calibration, with P-values of 0.9685 and 0.2422 for the two groups. The DCA demonstrated that the model provided greater net benefit. Further rationality analysis confirmed that combining multiple predictors resulted in better diagnostic performance than using a single predictor. Conclusion: The nomogram identifies 4 independent risk factors for SCNEN: segment (lumbar vertebra), surgical method (pedicle screw internal fixation), the anteroposterior bone cement distribution ratio, and Cobb angle restoration ratio.