Development and validation of a nomogram model based on spinopelvic parameters for predicting recurrent lumbar disc herniation

This study aimed to evaluate the predictive value of spinopelvic parameters and clinical variables for recurrent lumbar disc herniation (rLDH) following percutaneous endoscopic lumbar discectomy (PELD). We retrospectively collected data from 1,219 patients who underwent PELD surgery between January 2017 and January 2023, randomly divided into training (70%) and validation (30%) groups. Univariate and multivariate logistic regression analyses were performed in the training set to identify independent risk factors and construct a nomogram model. Model accuracy was assessed using the area under the receiver operating characteristic curve (AUC). Calibration was evaluated through calibration curves and goodness-of-fit tests. Decision curve analysis (DCA) was performed to assess net clinical benefit. Multivariate analysis identified BMI ≥25 kg/m 2 , high-intensity labor, pelvic incidence (PI) <45° or >60°, sacral slope (SS) <35°, Pfirrmann grade III, and Modic type II and III changes as independent predictors of rLDH (P<0.05). The model achieved an AUC of 0.836 (95%CI: 0.801–0.872) in the training set and 0.812 (95%CI: 0.735–0.888) in the validation set. Calibration curves showed good agreement between predicted and observed values. DCA demonstrated that the model provided significant net clinical benefit when intervention threshold probabilities were set at 2%-59% for the training set and 2%-45% for the validation set. The spinopelvic parameter-based nomogram prediction model demonstrated excellent predictive performance for rLDH after PELD and facilitates individualized risk assessment.