Cage, Degenerative Lumbar Diseases, Machine Learning
Conditions
Keywords
Degenerative lumbar disease, Cage subsidence, Machine learning
Brief 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.
Interventions
PROCEDUREMR4
The study is a clinical retrospective study and does not involve any interventional measures.
Sponsors
Hao Liu
Study design
Observational model
CASE_CONTROL
Time perspective
RETROSPECTIVE
Eligibility
Sex/Gender
ALL
Healthy volunteers
Yes
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.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Bone density imaging indicator: Vertebral Bone Quality (VBQ) | Preoperative measurement for PLIF (Posterior Lumbar Interbody Fusion) | VBQ is calculated by dividing the mean T1 signal intensity (L1-L4) by the cerebrospinal fluid (CSF) signal at L3 |
Countries
China
Outcome results
None listed