Spine 2025;50:1375–1385
This multicenter retrospective study developed and validated an interpretable LightGBM machine learning model incorporating paraspinal muscle quality and bone metrics to accurately predict cage subsidence risk after PLIF. Key risk factors included lower psoas muscle index, higher fat infiltration, reduced bone density, and suboptimal cage parameters.
• A machine learning model (LightGBM) was developed to predict cage subsidence risk after PLIF, achieving high accuracy (AUC 0.9752, 92% accuracy, F1 score 0.92).
• Key independent risk factors include lower psoas muscle index (PMI), higher fat infiltration (FI), reduced bone density (HU value, VBQ), suboptimal cage position/height, and greater postoperative changes in intervertebral height (IH) and segmental angle (SA).
• Paraspinal muscle quality was a major contributor; removing muscle indicators reduced model accuracy substantially.
• Patients with cage subsidence had poorer paraspinal muscle and bone quality compared to those without subsidence.
• The model was externally validated and deployed as a web-based tool for real-time, individualized clinical risk assessment.
• Findings support personalized surgical planning and risk mitigation strategies for PLIF patients.
• The study emphasizes a multifactorial approach, integrating skeletal, muscular, and surgical parameters for optimal prediction.
• Limitations include retrospective design, use of a single cage type, and lack of comorbidity indices; further prospective studies are needed.
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