Faculty of Medicine, University of New South Wales, Sydney, Australia.
Department of Orthopaedics, St George Hospital, Suite 201, Level 2, 131 Princes Highway, Kogarah, Sydney, NSW, 2217, Australia.
BMC Musculoskelet Disord. 2022 Nov 22;23(1):1006. doi: 10.1186/s12891-022-05920-7.
The purpose of this study was to define the features of scapular morphology that are associated with changes in the critical shoulder angle (CSA) by developing the best predictive model for the CSA based on multiple potential explanatory variables, using a completely 3D assessment.
3D meshes were created from CT DICOMs using InVesalius (Vers 3.1.1, RTI [Renato Archer Information Technology Centre], Brazil) and Meshmixer (3.4.35, Autodesk Inc., San Rafael, CA). The analysis included 17 potential angular, weighted linear and area measurements. The correlation of the explanatory variables with the CSA was investigated with the Pearson's correlation coefficient. Using multivariable linear regression, the approach for predictive model-building was leave-one-out cross-validation and best subset selection.
Fifty-three meshes were analysed. Glenoid inclination (GI) and coronal plane angulation of the acromion (CPAA) [Pearson's r: 0.535; -0.502] correlated best with CSA. The best model (adjusted R-squared value 0.67) for CSA prediction contained 10 explanatory variables including glenoid, scapular spine and acromial factors. CPAA and GI were the most important based on their distribution, estimate of coefficients and loss in predictive power if removed.
The relationship between scapular morphology and CSA is more complex than the concept of it being dictated solely by GI and acromial horizontal offset and includes glenoid, scapular spine and acromial factors of which CPAA and GI are most important. A further investigation in a closely defined cohort with rotator cuff tears is required before drawing any clinical conclusions about the role of surgical modification of scapular morphology.
Level 4 retrospective observational cohort study with no comparison group.
本研究旨在通过基于多个潜在解释变量的最佳预测模型来定义与关键肩角 (CSA) 变化相关的肩胛形态特征,从而开发出一种完全 3D 评估方法。
使用 InVesalius(版本 3.1.1,巴西 Renato Archer 信息技术中心)和 Meshmixer(3.4.35,Autodesk Inc.,加利福尼亚州圣拉斐尔)从 CT DICOM 中创建 3D 网格。分析包括 17 个潜在的角度、加权线性和面积测量。使用 Pearson 相关系数研究解释变量与 CSA 的相关性。使用多变量线性回归,预测模型构建的方法是留一法交叉验证和最佳子集选择。
共分析了 53 个网格。关节盂倾斜角(GI)和肩峰冠状面角(CPAA)[Pearson r:0.535;-0.502]与 CSA 相关性最好。最佳 CSA 预测模型(调整后的 R-squared 值为 0.67)包含 10 个解释变量,包括关节盂、肩胛脊柱和肩峰因素。基于其分布、系数估计值和预测能力损失,如果去除 CPAA 和 GI 是最重要的。
肩胛形态与 CSA 之间的关系比仅由 GI 和肩峰水平偏移决定的概念更为复杂,包括关节盂、肩胛脊柱和肩峰因素,其中 CPAA 和 GI 最重要。在具有肩袖撕裂的紧密定义队列中进行进一步研究之前,不应根据肩胛形态的手术修改对其在临床中的作用得出任何结论。
无对照组的 4 级回顾性观察队列研究。