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

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.

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Key information

Conditions

Degenerative Lumbar Diseases Cage Machine Learning

Sex eligibility

All sexes

Study type

Observational

Primary location

The First Affiliated Hospital of Soochow University Medical Record and Imaging System

Jiangsu, SuZhou, 215006, China

Who can participate

Healthy volunteers accepted: Yes

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

confirmed lumbar disc herniation, spinal stenosis, or spondylolisthesis based on clinical and imaging findings;
patients who failed conservative treatment for ≥3 months or experienced recurrence and underwent surgery for the first time;
minimum 12-month follow-up.

Exclusion criteria

prior spinal surgery;
spinal deformity or severe instability;
lumbar tuberculosis, infection, tumor, or severe bone destruction;
incomplete or lost follow-up.

Treatment and study plan

MR4

Procedure

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

Primary outcomes

Bone density imaging indicator: Vertebral Bone Quality (VBQ)

Time frame: 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

Sponsors and collaborators

Lead sponsor

Hao Liu

Other

Registry information

Important dates

Study start: 2025
Primary completion: 2025
Study completion: 2025
First posted: Mar 21, 2025
Registry last updated: Mar 21, 2025

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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