Journal
EUROPEAN SPINE JOURNAL
Volume -, Issue -, Pages -Publisher
SPRINGER
DOI: 10.1007/s00586-023-07818-x
Keywords
Degenerative spondylolisthesis; Classification system; Sagittal balance; Lumbar spinal fusion; Spinal stability
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Reviews existing classification systems for degenerative spondylolisthesis (DS), proposes a novel classification system that better addresses clinically relevant radiographic and clinical features, and determines the reliability of this new system. The new UCSF DS classification system proves to be simple and easy to use, demonstrating high inter- and intraobserver reliability. This classification system may lead to improved treatment strategies and outcomes for patients with DS.
PurposeTo review existing classification systems for degenerative spondylolisthesis (DS), propose a novel classification designed to better address clinically relevant radiographic and clinical features of disease, and determine the inter- and intraobserver reliability of this new system for classifying DS.MethodsThe proposed classification system includes four components: 1) segmental dynamic instability, 2) location of spinal stenosis, 3) sagittal alignment, and 4) primary clinical presentation. To establish the reliability of this system, 12 observers graded 10 premarked test cases twice each. Kappa values were calculated to assess the inter- and intraobserver reliability for each of the four components separately.ResultsInterobserver reliability for dynamic instability, location of stenosis, sagittal alignment, and clinical presentation was 0.94, 0.80, 0.87, and 1.00, respectively. Intraobserver reliability for dynamic instability, location of stenosis, sagittal alignment, and clinical presentation were 0.91, 0.88, 0.87, and 0.97, respectively.ConclusionThe UCSF DS classification system provides a novel framework for assessing DS based on radiographic and clinical parameters with established implications for surgical treatment. The almost perfect interobserver and intraobserver reliability observed for all components of this system demonstrates that it is simple and easy to use. In clinical practice, this classification may allow subclassification of similar patients into groups that may benefit from distinct treatment strategies, leading to the development of algorithms to help guide selection of an optimal surgical approach. Future work will focus on the clinical validation of this system, with the goal of providing for more evidence-based, standardized approaches to treatment and improved outcomes for patients with DS.
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