European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2023
Degenerative lumbar spondylolisthesis: review of current classifications and proposal of a novel classification system.
СтатьяОбзорНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Rangwalla Khuzaima, Filley Anna, El Naga Ashraf, Gendelberg David, Baldwin Avionna, Maziad Ali, Arora Ayush, Wague Aboubacar, O'Donnell Jennifer, Chryssikos Timothy, Kasir Rafid, Shah Jay, Theologis Alekos, Tan Lee, Mummaneni Praveen, Alamin Todd, Berven Sigurd H
Аннотация
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.
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.