Machine Learning-Based Identification of Distinct Risk Factors for Moderate vs Severe Proximal Junctional Kyphosis After Adult Spinal Deformity Surgery.

BACKGROUND CONTEXT: Proximal junctional kyphosis (PJK) is a well-recognized complication of adult spinal deformity (ASD) surgery. However, traditional proximal junctional angle (PJA) thresholds (15°) and revision-predictive thresholds (28°) may represent distinct clinical entities with unique etiologies. PURPOSE: To analyze and compare predictive risk factors for moderate (PJA ≥15°) and severe (PJA ≥28°) PJK using machine learning. STUDY DESIGN/SETTING: Retrospective study PATIENT SAMPLE: A total of 374 patients who underwent ASD surgery with a minimum 2-year follow-up. OUTCOME MEASURES: Development of moderate (PJA ≥ 15°) and severe (PJA ≥ 28°) PJK. METHODS: Five machine learning algorithms (Logistic Regression, SVM, RF, XGBoost, AutoGluon) were trained to predict moderate and severe PJK. Feature stability analysis identified robust predictors across models, and SHAP analysis elucidated the feature directionality. RESULTS: The incidence of moderate and severe PJK was 17.4% (65 patients) and 11.0% (41 patients), respectively. BMI and L1 tilt were universally selected for moderate PJK. SHAP analysis showed that high RLL (relative hyperlordosis) and L1 tilt were associated with increased predicted risk, whereas iliac screws were protective. The maldistributed LDI and number of rods were consensus predictors of severe PJK. SHAP associated maldistributed LDI, excessive postoperative LL, and high cement volume with an increased predicted risk of severe PJK. CONCLUSIONS: Moderate PJK appears to be driven by geometric stress concentration (e.g., L1 tilt and relative hyperlordosis), whereas severe PJK stems from structural/distributional mismatch (e.g., lordosis maldistribution and construct rigidity). Prevention strategies should be stratified according to these distinct mechanisms.