School of Medicine, University of California, San Francisco, San Francisco, CA, USA.
Department of Orthopaedic Surgery, University of California, San Francisco, San Francisco, CA, USA.
Eur Spine J. 2024 May;33(5):1762-1772. doi: 10.1007/s00586-023-07818-x. Epub 2023 Aug 6.
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.
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.
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.
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.
回顾退行性脊柱滑脱（DS）的现有分类系统，提出一种新的分类方法，旨在更好地解决与疾病相关的影像学和临床特征，并确定该新的 DS 分类系统的观察者间和观察者内可靠性。
所提出的分类系统包括四个部分：1）节段性动力不稳定，2）椎管狭窄的位置，3）矢状面排列，4）主要临床表现。为了建立该系统的可靠性，12 名观察者对 10 个预标记的测试病例进行了两次评分。计算 Kappa 值以评估四个部分的观察者间和观察者内可靠性。
动力不稳定、狭窄位置、矢状面排列和临床表现的观察者间可靠性分别为 0.94、0.80、0.87 和 1.00。动力不稳定、狭窄位置、矢状面排列和临床表现的观察者内可靠性分别为 0.91、0.88、0.87 和 0.97。
UCSF DS 分类系统提供了一种基于影像学和临床参数评估 DS 的新框架，对手术治疗具有明确的影响。该系统所有部分的观察者间和观察者内可靠性几乎达到完美，表明其简单易用。在临床实践中，这种分类方法可以将类似的患者分为可能受益于不同治疗策略的组，从而制定算法来帮助选择最佳手术方法。未来的工作将集中在该系统的临床验证上，目标是为 DS 患者提供更基于证据的、标准化的治疗方法和改善的治疗效果。
