Calderazzi Filippo, Donelli Davide, Galavotti Cristina, Nosenzo Alessandro, Bastia Paolo, Lunini Enricomaria, Paterlini Marco, Concari Giorgio, Maresca Alessandra, Marinelli Alessandro
Department of Medicine and Surgery, Orthopaedic Clinic, Maggiore Hospital-University of Parma, Parma, Italy.
Department of Cardiothoracic and Vascular Diseases, Cardiology Unit, Maggiore Hospital-University of Parma, Parma, Italy.
JSES Int. 2024 Nov 8;9(2):549-561. doi: 10.1016/j.jseint.2024.09.031. eCollection 2025 Mar.
Owing to the great variety of fracture patterns and limitations of the standard radiographic investigation, all the already available classification systems for radial head and neck fractures (RHNFs) are limited by a poor-to-moderate degree of intraobserver and interobserver reliability. Although computed tomography (CT) is being increasingly used to better understand the fracture characteristics, a CT-based classification system of RHNFs is still lacking. Therefore, in this agreement study, we aimed to propose a classification system based on two-dimensional and three-dimensional (2D/3D) CT to test the hypothesis that this classification has good intraobserver and interobserver reliability. We have also provided a treatment algorithm.
Our proposed classification-Proximal and Articular Radial fractures Management (PARMa)-is based on 2D/3D CT imaging. It is divided into four types based on different fractures patterns. The 2D/3D scans of 90 RHNFs were evaluated in a blinded fashion by eight orthopedic and one radiology consultant, according to the proposed classification. The first phase of observation aimed to estimate the interobserver agreement. The second phase involved a new observation, 4 weeks after the first analysis, and estimated the intraobserver reliability. The standard radiographs of these 90 fractures were also evaluated by the same observers, with the same timing and methods, based on the same classification. Cohen's Kappa was applied for intraobserver agreement. Fleiss's Kappa was used both within and among the evaluators. Kendall's coefficient of concordance was employed to determine the strength of association among the appraisers' rankings. Furthermore, Krippendorff's alpha was chosen as an adjunctive analysis to assess between evaluators' agreement.
For the intraobserver agreement, Fleiss' Kappa statistics confirmed the consistency (overall kappa values: 0.70-0.82). Cohen's Kappa statistics aligned with Fleiss' Kappa, with similar kappa values and significant values ( < .001). For interobserver agreement, Fleiss' Kappa statistics for between appraisers showed moderate-to-substantial agreement, with kappa values ranging from 0.54 to 0.82 for different responses. The results relating to the appraisers' observation of standard radiographs showed that the overall Fleiss' Kappa values for intraobserver agreement ranged from 0.34 to 0.82, whereas Fleiss' Kappa statistics for interobserver agreement ranged from 0.40 to 0.69.
The proposed classification system is expected to be reliable, reproducible, and useful for preoperative planning and surgical management. Both 2D and 3D CT allow the identification of the magnitude and position of displacement and articular surface involvement.
由于骨折类型繁多且标准放射学检查存在局限性,现有的所有桡骨头和颈骨折(RHNFs)分类系统在观察者内和观察者间的可靠性程度较差到中等,均受到限制。尽管计算机断层扫描(CT)越来越多地用于更好地了解骨折特征,但仍缺乏基于CT的RHNFs分类系统。因此,在本一致性研究中,我们旨在提出一种基于二维和三维(2D/3D)CT的分类系统,以检验该分类具有良好的观察者内和观察者间可靠性这一假设。我们还提供了一种治疗算法。
我们提出的分类方法——近端和关节桡骨骨折管理(PARMa)——基于2D/3D CT成像。根据不同的骨折类型分为四种类型。八名骨科医生和一名放射科顾问以盲法根据提出的分类对90例RHNFs的2D/3D扫描进行评估。第一阶段观察旨在评估观察者间的一致性。第二阶段在第一次分析4周后进行新的观察,并评估观察者内的可靠性。这90例骨折的标准X线片也由相同的观察者在相同的时间和方法下,基于相同的分类进行评估。采用Cohen's Kappa评估观察者内一致性。Fleiss's Kappa用于评估者内部和评估者之间。采用Kendall和谐系数确定评估者排名之间的关联强度。此外,选择Krippendorff's alpha作为辅助分析来评估评估者之间的一致性。
对于观察者内一致性,Fleiss' Kappa统计数据证实了一致性(总体kappa值:0.70 - 0.82)。Cohen's Kappa统计数据与Fleiss' Kappa一致,kappa值相似且具有显著性(< 0.001)。对于观察者间一致性,评估者之间的Fleiss' Kappa统计数据显示出中等至高度一致性,不同反应的kappa值范围为0.54至0.82。评估者对标准X线片观察的结果表明,观察者内一致性的总体Fleiss' Kappa值范围为0.34至0.82,而观察者间一致性的Fleiss' Kappa统计数据范围为0.40至0.69。
所提出的分类系统有望可靠、可重复,并有助于术前规划和手术管理。2D和3D CT均可识别移位的大小和位置以及关节面受累情况。