Abstract
INTRODUCTION: Axial pain is a common complication following expansive unilateral open-door laminoplasty (ELAP); however, traditional statistical methods are unable to effectively predict this complication. This study developed machine learning (ML) models to predict post-ELAP axial pain and identify key predictors. METHODS: This retrospective study enrolled 851 cervical spondylotic myelopathy (CSM) patients undergoing ELAP, split into training (n = 714) and temporal validation sets (n = 137). Demographic, imaging, clinical, and surgical data were collected. Predictive features were selected by the least absolute shrinkage and selection operator (Lasso) regression, followed by ML model development with grid search optimizing hyperparameters. The top-performing model underwent temporal validation, and SHapley Additive exPlanations (SHAP) analyzed predictor contributions. RESULTS: The training set included 218 axial pain cases; the test set had 47. Key predictors (C7 laminoplasty, cervical kyphosis, etc.) were identified to develop ML model. Post-optimization, extreme gradient boosting (XGBoost) achieved superior performance (internal validation area under the receiver [AUC] = 0.948; 95% confidence interval [CI] 0.918-0.978), maintained in temporal validation (AUC = 0.906; 95% CI 0.858-0.954). Through SHAP analysis, the predictors were ranked in descending order of importance as follows: C7 laminoplasty, quantity-based surgical segment classification, cervical kyphosis, angle of lamina open-door, cervical lordosis, and occupying rate of cervical spinal canal. CONCLUSIONS: ML models coupled with SHAP analysis effectively predict post-ELAP axial pain, identifying the key predictors. Performing segment-selective ELAP, avoiding unnecessary C7 laminoplasty, and maintaining optimal open-door angle are critical factors in avoiding the occurrence of axial pain following ELAP.