Multivariate Analysis and Risk Factors Were Used to Predict the Short-term Postoperative Pain of Degenerative Lumbar Spine
NCT ID: NCT06628583
Last Updated: 2024-10-08
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
Get a concise snapshot of the trial, including recruitment status, study phase, enrollment targets, and key timeline milestones.
COMPLETED
936 participants
OBSERVATIONAL
2024-09-20
2024-09-28
Brief Summary
Review the sponsor-provided synopsis that highlights what the study is about and why it is being conducted.
Related Clinical Trials
Explore similar clinical trials based on study characteristics and research focus.
Multivariate Analysis and Machine Learning Model Risk Prediction of Recurrent Pain After PLIF Surgery for Degenerative Lumbar Spine Disease At Long-term Follow-up
NCT06622356
Risk Factors of Paraspinal Muscles in Degenerative Lumbar Spondylolisthesis
NCT06797700
Development and Validation of Interpretable Machine Learning Models Incorporating Paraspinal Muscle Quality for to Predict Cage Subsidence Risk Followingposterior Lumbar Interbody Fusion
NCT06888739
Screening and Prospective Cohort Study of Risk Factors for Enhanced Recovery After Spinal Fusion Surgery in Advanced Age Patient
NCT06591442
The Correlation Between Degeneration of Paraspinal Muscle and Outcome of Patients With Lumbar Spinal Stenosis
NCT04688944
Detailed Description
Dive into the extended narrative that explains the scientific background, objectives, and procedures in greater depth.
Conditions
See the medical conditions and disease areas that this research is targeting or investigating.
Study Design
Understand how the trial is structured, including allocation methods, masking strategies, primary purpose, and other design elements.
CASE_CONTROL
RETROSPECTIVE
Study Groups
Review each arm or cohort in the study, along with the interventions and objectives associated with them.
Rehabilitation Group
mild/non pain group with VAS\<3 at 3th months after PLIF.N=501
Magnetic Resonance Imaging with Contrast
The study was retrospective and did not set interventions
Recurrent pain group
Recurrent pain group: recurrent pain group with VAS≥3 at 3th months after PLIF.N=435
Magnetic Resonance Imaging with Contrast
The study was retrospective and did not set interventions
Interventions
Learn about the drugs, procedures, or behavioral strategies being tested and how they are applied within this trial.
Magnetic Resonance Imaging with Contrast
The study was retrospective and did not set interventions
Eligibility Criteria
Check the participation requirements, including inclusion and exclusion rules, age limits, and whether healthy volunteers are accepted.
Inclusion Criteria
2. lumbar spinal fusion surgical treatment with PLIF;
3. grouping based on the presence or absence of a lumbar or limb pain with VAS ≥3 at the 3th month postoperative follow-up;
4. observational indicators including individual factors, surgical factors, spine-pelvis sagittal balance parameters, and paravertebral muscle parameters.
Exclusion Criteria
ALL
Yes
Sponsors
Meet the organizations funding or collaborating on the study and learn about their roles.
Hao Liu
OTHER
Responsible Party
Identify the individual or organization who holds primary responsibility for the study information submitted to regulators.
Hao Liu
Chief orthopaedic surgeon, First Affiliated Hospital of Suzhou University, China
Locations
Explore where the study is taking place and check the recruitment status at each participating site.
Medical record system and imaging system of the First Affiliated Hospital of Suzhou University, China
Suzhou, Jiangsu, China
Countries
Review the countries where the study has at least one active or historical site.
Other Identifiers
Review additional registry numbers or institutional identifiers associated with this trial.
(2024) Lun Yan Grant No. 518
Identifier Type: -
Identifier Source: org_study_id
More Related Trials
Additional clinical trials that may be relevant based on similarity analysis.