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

NCT06888739 · Status: COMPLETED · Type: OBSERVATIONAL · Enrollment: 720

Last updated 2025-03-21

No results posted yet for this study

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

PROCEDURE

MR4

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

Sponsors & Collaborators

  • Hao Liu

    lead OTHER

Eligibility

Sex
ALL
Healthy Volunteers
Yes

Timeline & Regulatory

Start
2025-03-01
Primary Completion
2025-03-10
Completion
2025-03-15

Countries

  • China

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT06888739 on ClinicalTrials.gov