Ziwei Fan

Purpose To compare clinical outcomes and safety of minimally invasive transforaminal lumbar interbody fusion (MIS-TLIF), unilateral biportal endoscopic lumbar interbody fusion (ULIF), and modified MIS-TLIF via unilateral biportal endoscopic assistance (UBE-MIS-TLIF) for single-segment Schizas C/D LSS. Methods A retrospective analysis of 205 patients (UBE-MIS-TLIF: 37, MIS-TLIF: 93, ULIF: 75) evaluated clinical outcomes using visual analogue scale (VAS), Oswestry Disability Index (ODI), Japanese Orthopaedic Association (JOA) scores, modified MacNab criteria, fusion rates, and complications over 12 months. Surgical parameters and deep ligamentum flavum (LF) preservation were also analyzed. Results All groups demonstrated significant postoperative improvements in VAS [low back pain (LBP)/leg pain], ODI, and JOA scores. MIS-TLIF demonstrated higher VAS scores for incision pain compared to the other two groups. UBE-MIS-TLIF and ULIF exhibited lower LBP VAS and ODI within one week postoperatively. At final follow-up, no significant differences were observed in VAS, ODI, JOA, modified MacNab criteria, complications, intervertebral height index, or fusion rates among these groups. UBE-MIS-TLIF demonstrated significant improvements in intraoperative blood loss, wound drainage volume, CRP level, and postoperative hospital stay compared to MIS-TLIF, while showing no statistically significant differences from ULIF. However, UBE-MIS-TLIF required longer operative time and more fluoroscopy than MIS-TLIF. UBE-MIS-TLIF showed superior performance in preserving the deep LF. Conclusion UBE-MIS-TLIF, MIS-TLIF, and ULIF demonstrated comparable long-term efficacy for single-segment Schizas C/D LSS. As a modified approach, UBE-MIS-TLIF reduced invasiveness versus MIS-TLIF, achieved shorter operative time compared to ULIF, and could preserve the deep LF, offering a viable alternative for severe LSS management.

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.