NCT06888739

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

• Status: Completed
• Phase: —
• Study type: Observational
• Enrollment: 720 (actual)
• Sponsor: Hao Liu · Academic / Other
• Sex: All
• Age: —
• Healthy volunteers: Accepted

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.

Conditions

• Degenerative Lumbar Diseases
• Cage
• Machine Learning

Interventions

Type	Name	Description
PROCEDURE	MR4	The study is a clinical retrospective study and does not involve any interventional measures.

Timeline

• Start date: 2025-03-01
• Primary completion: 2025-03-10
• Completion: 2025-03-15
• First posted: 2025-03-21
• Last updated: 2025-03-21

Locations

1 site across 1 country: China