Department of Orthopedic Surgery, Hospital for Special Surgery, New York.
Department of Orthopedic Surgery, Northwell Health, Great Neck.
Clin Spine Surg. 2022 Jun 1;35(5):E504-E509. doi: 10.1097/BSD.0000000000001297. Epub 2022 Mar 7.
This was a retrospective review of a prospectively collected database.
The aim of this study was to delineate radiographic parameters that distinguish severe cervical spine deformity (CSD).
Our objective was to define parameters that distinguish severe CSD using a consensus approach combined with discriminant analysis as no system currently exists in the literature.
Twelve CSD surgeons reviewed preoperative x-rays from a CSD database. A consensus was reached for categorizing patients into a severe cervical deformity (sCD), non-severe cervical deformity (non-sCD), or an indeterminate cohort. Radiographic parameters were found including classic cervical and spinopelvic parameters in neutral/flexion/extension alignment. To perform our discriminant analysis, we selected for parameters that had a significant difference between the sCD and non-sCD groups using the Student t test. A discriminant function analysis was used to determine which variables discriminate between the sCD versus non-sCD. A stepwise analysis was performed to build a model of parameters to delineate sCD.
A total of 146 patients with cervical deformity were reviewed (60.5±10.5 y; body mass index: 29.8 kg/m2; 61.3% female). There were 83 (56.8%) classified as sCD and 51 (34.9%) as non-sCD. The comparison analysis led to 16 radiographic parameters that were different between cohorts, and 5 parameters discriminated sCD and non-sCD. These parameters were cervical sagittal vertical axis, T1 slope, maximum focal kyphosis in extension, C2 slope in extension, and number of kyphotic levels in extension. The canonical coefficient of correlation was 0.689, demonstrating a strong association between our model and cervical deformity classification. The accuracy of classification was 87.0%, and cross-validation was 85.2% successful.
More than one third of a series of CSD patients were not considered to have a sCD. Analysis of an initial 17 parameters showed that a subset of 5 parameters can discriminate between sCD versus non-sCD with 85% accuracy. Our study demonstrates that flexion/extension images are critical for defining severe CD.
这是一项前瞻性收集数据库的回顾性研究。
本研究旨在描绘出区分严重颈椎畸形（CSD）的影像学参数。
我们的目标是使用共识方法结合判别分析来定义区分严重 CSD 的参数，因为目前文献中尚无此类系统。
12 名 CSD 外科医生对 CSD 数据库中的术前 X 光片进行了回顾。通过共识将患者分为严重颈椎畸形（sCD）、非严重颈椎畸形（non-sCD）或不确定队列。在中立/屈伸/伸展对线中发现了包括经典颈椎和脊柱骨盆参数在内的影像学参数。为了进行判别分析，我们使用学生 t 检验选择了在 sCD 和 non-sCD 组之间具有显著差异的参数。使用判别函数分析来确定区分 sCD 与 non-sCD 的变量。进行逐步分析以建立一个用于描绘 sCD 的参数模型。
共回顾了 146 例颈椎畸形患者（60.5±10.5 岁；体重指数：29.8kg/m2；61.3%为女性）。其中 83 例（56.8%）被归类为 sCD，51 例（34.9%）为 non-sCD。对比分析导致了 16 个影像学参数在队列之间存在差异，其中 5 个参数区分了 sCD 和 non-sCD。这些参数包括颈椎矢状垂直轴、T1 斜率、伸展时最大焦点后凸、伸展时 C2 斜率和伸展时后凸水平数。典型相关系数为 0.689，表明我们的模型与颈椎畸形分类之间存在很强的关联。分类的准确率为 87.0%，交叉验证的成功率为 85.2%。
超过三分之一的 CSD 患者系列被认为没有 sCD。对最初的 17 个参数的分析表明，5 个参数子集可以以 85%的准确率区分 sCD 与 non-sCD。我们的研究表明，屈伸位图像对于定义严重 CD 至关重要。
