Degenerative lumbar spondylolisthesis: review of current classifications and proposal of a novel classification system

医学 矢状面 可靠性(半导体) 椎管狭窄 脊椎滑脱 射线照相术 腰椎管狭窄症 介绍(产科) 临床实习 腰椎 卡帕 放射科 医学物理学 口腔正畸科 物理疗法 功率(物理) 物理 量子力学 语言学 哲学
作者
Khuzaima Rangwalla,Anna C. Filley,Ashraf El Naga,David Gendelberg,Avionna Baldwin,Ali M Maziad,Ayush Arora,Aboubacar Wague,Jennifer O’Donnell,Timothy Chryssikos,Rafid Kasir,Jash Shah,Alekos A. Theologis,Lee A. Tan,Praveen V. Mummaneni,Khuzaima Rangwalla
出处
期刊:European Spine Journal [Springer Nature]
标识
DOI:10.1007/s00586-023-07818-x
摘要

Abstract Purpose To 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. Methods The 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. Results Interobserver 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. Conclusion The 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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