Machine Learning–Based Prediction of Proximal Junctional Kyphosis in Adult Degenerative Scoliosis After Long-Segment Fusion: A Multicenter Training–Validation Study

Study DesignRetrospective case-control study.ObjectivesTo identify risk factors for proximal junctional kyphosis (PJK) after long-segment fusion in adult degenerative scoliosis (ADS) and to develop a machine learning-based prediction model with external validation.MethodsWe retrospectively analyzed 142 ADS patients from two institutions undergoing posterior long-segment fusion with ≥24 months follow-up. Patients from center A (n = 105) formed the training cohort, and those from center B (n = 37) served as the external validation cohort. Demographic, radiographic, and surgical parameters were compared between patients with and without PJK. Independent predictors were determined with multivariate logistic regression. Least absolute shrinkage and selection operator (LASSO) regression identified key variables. Six supervised machine learning algorithms were trained using center A data and validated on center B data. Model interpretability was assessed using Local Interpretable Model-agnostic Explanations (LIME).ResultsPJK occurred in 24 patients (16.9%). Logistic regression identified lower T-score, higher T1-pelvic angle, and female sex as independent predictors, with ASA grade III showing a marginal effect. LASSO retained five features: T score, ASA grade, T1PA, sacral slope, and pelvic incidence. Among algorithms, the back-propagation neural network with LASSO feature selection yielded the best discrimination (external validation AUC = 0.882). LIME analysis confirmed T score, T1PA, and PI as the most influential predictors.ConclusionsReduced bone density, impaired sagittal balance, and higher ASA grade increase PJK risk after long-segment fusion in ADS. A neural network combined with LASSO feature selection demonstrated superior predictive performance, supporting its potential for individualized preoperative risk assessment and surgical planning.