Mabray Marc C, Talbott Jason F, Whetstone William D, Dhall Sanjay S, Phillips David B, Pan Jonathan Z, Manley Geoffrey T, Bresnahan Jacqueline C, Beattie Michael S, Haefeli Jenny, Ferguson Adam R
1 Department of Radiology and Biomedical Imaging, University of California San Francisco and San Francisco General Hospital , San Francisco, California.
5 Brain and Spinal Injury Center, San Francisco General Hospital , San Francisco, California.
J Neurotrauma. 2016 May 15;33(10):954-62. doi: 10.1089/neu.2015.4093. Epub 2016 Feb 1.
Literature examining magnetic resonance imaging (MRI) in acute spinal cord injury (SCI) has focused on cervical SCI. Reproducible systems have been developed for MRI-based grading; however, it is unclear how they apply to thoracic SCI. Our hypothesis is that MRI measures will group as coherent multivariate principal component (PC) ensembles, and that distinct PCs and individual variables will show discriminant validity for predicting early impairment in thoracic SCI. We undertook a retrospective cohort study of 25 patients with acute thoracic SCI who underwent MRI on admission and had American Spinal Injury Association Impairment Scale (AIS) assessment at hospital discharge. Imaging variables of axial grade, sagittal grade, length of injury, thoracolumbar injury classification system (TLICS), maximum canal compromise (MCC), and maximum spinal cord compression (MSCC) were collected. We performed an analytical workflow to detect multivariate PC patterns followed by explicit hypothesis testing to predict AIS at discharge. All imaging variables loaded positively on PC1 (64.3% of variance), which was highly related to AIS at discharge. MCC, MSCC, and TLICS also loaded positively on PC2 (22.7% of variance), while variables concerning cord signal abnormality loaded negatively on PC2. PC2 was highly related to the patient undergoing surgical decompression. Variables of signal abnormality were all negatively correlated with AIS at discharge with the highest level of correlation for axial grade as assessed with the Brain and Spinal Injury Center (BASIC) score. A multiple variable model identified BASIC as the only statistically significant predictor of AIS at discharge, signifying that BASIC best captured the variance in AIS within our study population. Our study provides evidence of convergent validity, construct validity, and clinical predictive validity for the sampled MRI measures of SCI when applied in acute thoracic and thoracolumbar SCI.
研究急性脊髓损伤(SCI)的磁共振成像(MRI)的文献主要聚焦于颈髓损伤。基于MRI的分级系统已经得到了完善;然而,这些系统如何应用于胸髓损伤尚不清楚。我们的假设是,MRI测量值将归为连贯的多变量主成分(PC)集合,并且不同的主成分和单个变量将显示出对预测胸髓损伤早期损伤的判别效度。我们对25例急性胸髓损伤患者进行了一项回顾性队列研究,这些患者入院时接受了MRI检查,并在出院时进行了美国脊髓损伤协会损伤量表(AIS)评估。收集了轴位分级、矢状位分级、损伤长度、胸腰段损伤分类系统(TLICS)、最大椎管狭窄(MCC)和最大脊髓压迫(MSCC)等影像变量。我们执行了一个分析流程来检测多变量PC模式,然后进行明确的假设检验以预测出院时的AIS。所有影像变量在PC1上均呈正负荷(占方差的64.3%),这与出院时的AIS高度相关。MCC、MSCC和TLICS在PC2上也呈正负荷(占方差的22.7%),而与脊髓信号异常相关的变量在PC2上呈负负荷。PC2与接受手术减压的患者高度相关。信号异常变量与出院时的AIS均呈负相关,其中轴位分级与脑和脊髓损伤中心(BASIC)评分评估的相关性最高。一个多变量模型确定BASIC是出院时AIS的唯一具有统计学意义的预测因子,这表明BASIC在我们的研究人群中最能捕捉AIS的方差。我们的研究为在急性胸髓和胸腰段脊髓损伤中应用的SCI抽样MRI测量提供了收敛效度、结构效度和临床预测效度的证据。