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

Hao Liu

Status: Completed

Conditions: Machine Learning; Degenerative Lumbar Diseases; Cage

Treatments: Procedure: MR4

Study type: Observational

Funder types: Other

Identifiers

NCT06888739

（2025）Lun Yan Grant No. 220 (Other Identifier)

Details and patient eligibility

About

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.

Enrollment: 720 patients

Sex: All

Volunteers: Accepts Healthy Volunteers

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.

Trial design

720 participants in 2 patient groups

Non-Subsidence Group

Description:

Group A (n = 390): Lost IH value \<2 mm.Cage subsidence was defined as a lost IH value ≥2 mm during the follow-up period

Treatment:

Procedure: MR4

Subsidence Group

Description:

Lost IH value ≥2 mm.Cage subsidence was defined as a lost IH value ≥2 mm during the follow-up period

Treatment:

Procedure: MR4