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.

This retrospective study included patients with degenerative lumbar diseases who underwent single-level PLIF at the First and Second Affiliated Hospitals of Soochow University (January 2018-October 2023).A total of 620 patients from the First Affiliated Hospital comprised the training set, while 100 patients from the Second Affiliated Hospital formed the external validation set. The study adhered to the Declaration of Helsinki and was approved by the institutional ethics committee.

Before January 1,2026;in the form of paper. The original data of the trial will be uploaded to Genome Sequence Archive (GSA) (ngdc.cncb.ac.cn)