Development and Validation of Interpretable Machine Learning Models Incorporating Paraspinal Muscle Quality for to Predict Cage Subsidence Risk Followingposterior Lumbar Interbody Fusion

Development and Validation of Interpretable Machine Learning Models Incorporating Paraspinal Muscle Quality for to Predict Cage Subsidence Risk Followingposterior Lumbar Interbody Fusion

The study focuses on identifying risk factors for cage subsidence after posterior lumbar interbody fusion (PLIF) and developing an interpretable machine learning model to predict these risks. It analyzes patients from two large teaching hospitals, using clinical, radiographic, and surgical parameters, including paraspinal muscle indices and bone density markers. A web-based application was developed to facilitate real-time clinical risk assessments using the machine learning model, enhancing surgical planning and reducing subsidence risks.

Inclusion Criteria:

1. confirmed lumbar disc herniation, spinal stenosis, or spondylolisthesis based on clinical and imaging findings;
2. patients who failed conservative treatment for ≥3 months or experienced recurrence and underwent surgery for the first time;
3. minimum 12-month follow-up.

Exclusion Criteria:

1. prior spinal surgery;
2. spinal deformity or severe instability;
3. lumbar tuberculosis, infection, tumor, or severe bone destruction;
4. incomplete or lost follow-up.