Development and Validation of Interpretable Machine Learning Models Incorporating Paraspinal Muscle Quality for to Predict Cage Subsidence Risk Followingposterior Lumbar Interbody Fusion
NCT ID: NCT06888739
Last Updated: 2025-03-21
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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COMPLETED
720 participants
OBSERVATIONAL
2025-03-01
2025-03-15
Brief Summary
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Detailed Description
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Conditions
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Study Design
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CASE_CONTROL
RETROSPECTIVE
Study Groups
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Non-Subsidence Group
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
MR4
The study is a clinical retrospective study and does not involve any interventional measures.
Subsidence Group
Lost IH value ≥2 mm.Cage subsidence was defined as a lost IH value ≥2 mm during the follow-up period
MR4
The study is a clinical retrospective study and does not involve any interventional measures.
Interventions
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MR4
The study is a clinical retrospective study and does not involve any interventional measures.
Eligibility Criteria
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Inclusion Criteria
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
2. spinal deformity or severe instability;
3. lumbar tuberculosis, infection, tumor, or severe bone destruction;
4. incomplete or lost follow-up.
ALL
Yes
Sponsors
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Hao Liu
OTHER
Responsible Party
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Hao Liu
Department of Orthopedics, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu Province, China
Locations
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The First Affiliated Hospital of Soochow University Medical Record and Imaging System
Jiangsu, SuZhou, China
Countries
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Other Identifiers
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(2025)Lun Yan Grant No. 220
Identifier Type: OTHER
Identifier Source: secondary_id
(2025)Lun Yan Grant No. 220
Identifier Type: -
Identifier Source: org_study_id
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