ArticleJournal of orthopaedic case reports2026
Predictive Factors for Unfavorable Outcomes in Degenerative Lumbar Spondylolisthesis Surgery.
Article in Journal of orthopaedic case reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Introduction: Degenerative lumbar spondylolisthesis (DLS) is a common spinal disorder in the aging population, characterized by the forward displacement of a vertebral body due to degenerative changes. Lumbar interbody fusion (LIF) is the standard surgical treatment, yet outcomes remain variable, with a significant proportion of patients experiencing unfavorable results. Identifying predictive factors influencing these outcomes is critical for improving surgical planning, patient selection, and post-operative care. Materials and Methods: An observational cohort study included 52 patients aged 45-75 years diagnosed with single-level degenerative spondylolisthesis. Patients underwent LIF, and clinical outcomes were measured using the Visual Analog Scale (VAS) and Oswestry Disability Index (ODI). Radiological parameters, including facet angle (FA), disc height (DH), and lateral listhesis (LLS), were assessed preoperatively and postoperatively. Logistic regression and multivariate analysis were conducted to evaluate predictors of unfavorable outcomes, with significance set at P < 0.05. Results: FA (P = 0.039), LLS (P = 0.021), and DH (P = 0.032) were significant pre-operative predictors of unfavorable outcomes. Patients with inadequate post-operative DH restoration exhibited slower recovery, while sustained DH (P = 0.012) was critical for long-term success. Favorable outcomes demonstrated significant reductions in VAS (7.0 to 2.5) and ODI (28 to 8) scores at 6 months compared to the unfavorable cohort. Conclusion: FA, LLS, and DH are pivotal predictors of clinical outcomes in LIF for DLS. Monitoring these parameters enables personalized surgical planning and improved patient outcomes. Future studies should validate these findings in multicenter settings and explore machine learning for enhanced predictive modeling.
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