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

Hao Liu720 enrolled

Overview

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.

Study Type

OBSERVATIONAL

Enrollment

720

Conditions

Degenerative Lumbar Diseases Cage Machine Learning

Interventions

MR4PROCEDURE

The study is a clinical retrospective study and does not involve any interventional measures.

Eligibility

Sex: ALLHealthy volunteers:

Medical Language ↔ Plain English

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.

Outcomes

Primary Outcomes

Bone density imaging indicator: Vertebral Bone Quality (VBQ)

VBQ is calculated by dividing the mean T1 signal intensity (L1-L4) by the cerebrospinal fluid (CSF) signal at L3

Time frame: Preoperative measurement for PLIF (Posterior Lumbar Interbody Fusion)