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Predictive factors of symptomatic lumbar pseudoarthrosis following multilevel primary lumbar fusion.
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https://doi.org/10.1016/j.xnsj.2023.100302Abstract
BACKGROUND: Lumbar spinal fusion surgery is a well-established treatment for various spinal disorders. However, one of its complications, pseudoarthrosis, poses a significant concern. This study aims to explore the incidence, time and predictive factors contributing to pseudoarthrosis in patients who have undergone lumbar fusion surgery over a 10-year period. METHODS: Data for this research was sourced from the PearlDiver database where insurance claims of patients who underwent multilevel lumbar spinal fusion between 01/01/2010 and 10/31/2022 were examined for claims of pseudoarthrosis within the 10 years of their index procedure. A variety of demographic, comorbid, and surgical factors were assessed, including age, gender, Elixhauser Comorbidity Index (ECI), surgical approach, substance use disorders and history of spinal disorders. Statistical analyses, including chi-squared tests, multivariate analysis, and cox survival analysis were employed to determine significant associations. RESULTS: Among the 76,337 patients included in this retrospective study, 2.70% were diagnosed with symptomatic lumbar pseudoarthrosis at an average of 7.38 years in a 10-year follow-up. Multivariate and Cox hazard analyses revealed that significant predictors of symptomatic pseudoarthrosis development following multilevel primary lumbar fusion include vitamin D deficiency, osteoarthritis, opioid and NSAID use, tobacco use, and a prior history of congenital spine disorders. CONCLUSIONS: In summary, this study revealed a 2.70% incidence of symptomatic lumbar pseudoarthrosis within 10 years of the index procedure. It highlighted several potential predictive factors, including comorbidities, surgical approaches, and substance use disorders, associated with the development of symptomatic pseudoarthrosis. Future research should focus on refining our understanding of these factors to improve patient outcomes and optimize healthcare resource allocation.
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