Clinical and statistical correlation of various lumbar pathological conditions.
Clinical and statistical correlation of various lumbar pathological conditions.
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各种腰椎病理状况的临床和统计相关性。
DOI：
10.1016/j.jbiomech.2012.11.043
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发表时间：
2013
影响因子：
2.4
通讯作者：
Komistek,RichardD
中科院分区：
文献类型：
--
作者：
Johnson,JMichael;Mahfouz,Mohamed;Battaglia,NicholasV;Sharma,Adrija;Cheng,JosephS;Komistek,RichardD
目前的临床评价通常依赖于静态解剖成像模式来诊断机械性腰痛，其提供解剖快照和功能性疾病的替代分析。三维体内运动可通过使用数字荧光透视来获得，该数字荧光透视用于捕获腰椎的运动学数据，以识别可帮助医生区分患者病理的运动系数。40名患者分布在4类腰椎退变（从健康到退变）中，接受了CT、MRI和数字X线透视检查。每例患者均由神经外科医生进行诊断。在患者进行侧弯（LB）、轴向旋转（AR）和屈伸（FE）时进行X线透视。将患者特定模型与荧光透视图像配准，以获得体内运动学数据。运动系数CLB、CAR、CFE计算为面内运动与总面外运动的比值。计算每次运动绕运动轴的运动范围（ROM）。检查每个系数的组间和组内统计量，并使用灵活的贝叶斯分类器来区分退行性变患者。运动系数CL和CF在6组比较中有4组有显著性差异（p<0.05）。在平面运动中，ROMLB仅在6组比较中的1组中存在显著差异。该分类器使用（CFE、CLB、ROMLB）作为输入特征实现了95%的灵敏度和特异性，并且使用ROM变量实现了40%的特异性和80%的灵敏度。新的系数与患者病理学的相关性优于ROM测量。这些系数表明病理学和测量的运动之间的关系，这是以前没有报道过的。
Current clinical evaluations often rely on static anatomic imaging modalities for diagnosis of mechanical low back pain, which provide anatomic snapshots and a surrogate analysis of a functional disease. Three dimensional in vivo motion is available with the use of digital fluoroscopy, which was used to capture kinematic data of the lumbar spine in order to identify coefficients of motion that may assist the physician in differentiating patient pathology. Forty patients distributed among 4 classes of lumbar degeneration, from healthy to degenerative, underwent CT, MRI, and digital x-ray fluoroscopy. Each patient underwent diagnosis by a neurosurgeon. Fluoroscopy was taken as the patient performed lateral bending (LB), axial rotation (AR) and flexion-extension (FE). Patient specific models were registered with the fluoroscopy images to obtain in vivo kinematic data. Motion coefficients, CLB, CAR, CFE, were calculated as the ratio of in-plane motion to total out-of-plane motion. Range of motion (ROM) was calculated about the axis of motion for each exercise. Inter- and Intra- group statistics were examined for each coefficient and a flexible Bayesian classifier was used to differentiate patients with degeneration. The motion coefficients CLBand CFEwere significantly different (p<0.05) in 4 of 6 group comparisons. In plane motion, ROMLB, was significantly different in only 1 of 6 group comparisons. The classifier achieved 95% sensitivity and specificity using (CFE, CLB, ROMLB) as input features, and 40% specificity and 80% sensitivity using ROM variables. The new coefficients were better correlated with patient pathology than ROM measures. The coefficients suggest a relationship between pathology and measured motion which has not been reported previously.