CompletedNot Applicable

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

China720 participantsStarted 2025-03-01

Plain-language summary

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.

Who can participate

SexALL

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Inclusion criteria

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

Exclusion criteria

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

What they're measuring

Bone density imaging indicator: Vertebral Bone Quality (VBQ)

Timeframe: Preoperative measurement for PLIF (Posterior Lumbar Interbody Fusion)

Trial details

NCT IDNCT06888739

SponsorHao Liu

Sponsor typeOTHER

Study typeOBSERVATIONAL

Primary completion2025-03-10

View on ClinicalTrials.gov More Degenerative Lumbar Diseases trials