Machine learning-based identification of distinct risk factors for moderate versus severe proximal junctional kyphosis after adult spinal deformity surgery.

BACKGROUND CONTEXT: Proximal junctional kyphosis (PJK) is a well-recognized complication of adult spinal deformity 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 adult spinal deformity 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, support vector machine, random forest, extreme gradient boosting, 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. Body mass index and L1 tilt were universally selected for moderate PJK. SHAP analysis showed that high relative lumbar lordosis (relative hyperlordosis) and L1 tilt were associated with increased predicted risk, whereas iliac screws were protective. The maldistributed lordosis distribution index and number of rods were consensus predictors of severe PJK. SHAP associated maldistributed lordosis distribution index, excessive postoperative lumbar lordosis, and high cement volume with an increased predicted risk of severe PJK.

CONCLUSIONS

Moderate PJK appears to be driven by geometric stress concentration (eg, L1 tilt and relative hyperlordosis), whereas severe PJK stems from structural/distributional mismatch (eg, lordosis maldistribution and construct rigidity). Prevention strategies should be stratified according to these distinct mechanisms.