■复发性髌骨脱位(RPD)极大地影响活跃的年轻人,有必要识别风险因素,以便更好地了解其原因。先前的研究已将RPD与下肢对准(LEA)异常联系起来,比如股骨前倾增加,胫骨外旋,膝盖外翻,和屈曲。本研究旨在利用EOS技术检测与RPD相关的LEA异常,能够在负载条件下进行三维评估。
■总共100条肢体(RPD组中有50条,对照组50例)进行回顾性分析。在RPD组中,我们纳入了复发性髌骨脱位的肢体,以位错为特征的至少发生两次,健康四肢作为对照组。我们使用了EOS技术,包括2D和3D成像,测量和比较两组在站立姿势下的以下参数:股骨颈轴角(NSA),机械股骨胫骨角(MFTA),机械股骨远端外侧角(mLDFA),胫骨近端内侧角(MPTA),解剖股骨前倾(AFA),胫骨外扭转(ETT),和股骨胫骨旋转(FTR)。
■两组之间的显着差异在NSA3/2D中显示,MFTA3/2D,mLDFA3/2D,MPTA3D,AFA,FTR.在MPTA2D中没有显着差异,RPD组与对照组之间的ETT。进一步进行二元Logistic回归分析。进一步对影响上述RPD的危险因素进行二元logistic回归分析。并发现了二元逻辑回归分析的四个危险因素:mLDFA(3D),AFA,NSA(3D),和FTR。
■EOS成像识别出异常的LEA参数,包括国安局,MFTA,mLDFA,MPTA,AFA,和FTR,作为RPD的危险因素。有这些危险因素的儿童应接受适度的膝关节保护。
UNASSIGNED: Recurrent patellar dislocation (RPD) greatly affects active young individuals, necessitating the identification of risk factors for a better understanding of its cause. Previous research has connected RPD to lower limb alignment (LEA) abnormalities, such as increased femoral anteversion, tibial external rotation, knee valgus, and flexion. This study aims to use EOS technology to detect RPD-related LEA anomalies, enabling three-dimensional assessment under load conditions.
UNASSIGNED: A total of 100 limbs (50 in the RPD group, 50 in the control group) were retrospectively analyzed. In the RPD group, we included limbs with recurrent patellar dislocation, characterized by dislocations occurs at least two times, while healthy limbs served as the control group. We used EOS technology, including 2D and 3D imaging, to measure and compare the following parameters between the two groups in a standing position: Femoral neck shaft angle (NSA), Mechanical femoral tibial angle (MFTA), Mechanical lateral distal femoral angle (mLDFA), Medial proximal tibial angle (MPTA), Anatomical femoral anteversion (AFA), External tibial torsion (ETT), and Femorotibial rotation (FTR).
UNASSIGNED: The significant differences between the two groups were shown in NSA 3/2D, MFTA 3/2D, mLDFA 3/2D, MPTA 3D, AFA, FTR. No significant difference was shown in MPTA 2D, ETT between the RPD group and the control group. Further binary logistic regression analysis. Further binary logistic regression analysis was conducted on the risk factors affecting RPD mentioned above. and found four risk factors for binary logistic regression analysis: mLDFA (3D), AFA, NSA(3D), and FTR.
UNASSIGNED: EOS imaging identified abnormal LEA parameters, including NSA, MFTA, mLDFA, MPTA, AFA, and FTR, as risk factors for RPD. Children with these risk factors should receive moderate knee joint protection.