一种基于眼动追踪的脑震荡生物标志物的敏感性和特异性。
Sensitivity and specificity of an eye movement tracking-based biomarker for concussion.
作者信息
Samadani Uzma, Li Meng, Qian Meng, Laska Eugene, Ritlop Robert, Kolecki Radek, Reyes Marleen, Altomare Lindsey, Sone Je Yeong, Adem Aylin, Huang Paul, Kondziolka Douglas, Wall Stephen, Frangos Spiros, Marmar Charles
机构信息
Department of Neurosurgery, New York Harbor Health Care System, NY, USA.
Department of Neurosurgery, New York University, School of Medicine, NY, USA.
出版信息
Concussion. 2015 Aug 6;1(1):CNC3. doi: 10.2217/cnc.15.3. eCollection 2016 Mar.
OBJECT
The purpose of the current study is to determine the sensitivity and specificity of an eye tracking method as a classifier for identifying concussion.
METHODS
Brain injured and control subjects prospectively underwent both eye tracking and Sport Concussion Assessment Tool 3. The results of eye tracking biomarker based classifier models were then validated against a dataset of individuals not used in building a model. The area under the curve (AUC) of receiver operating characteristics was examined.
RESULTS
An optimal classifier based on best subset had an AUC of 0.878, and a cross-validated AUC of 0.852 in CT- subjects and an AUC of 0.831 in a validation dataset. The optimal misclassification rate in an external dataset (n = 254) was 13%.
CONCLUSION
If one defines concussion based on history, examination, radiographic and Sport Concussion Assessment Tool 3 criteria, it is possible to generate an eye tracking based biomarker that enables detection of concussion with reasonably high sensitivity and specificity.
目的
本研究旨在确定一种眼动追踪方法作为识别脑震荡分类器的敏感性和特异性。
方法
脑损伤患者和对照受试者前瞻性地接受了眼动追踪和运动性脑震荡评估工具3的检查。然后，基于眼动追踪生物标志物的分类器模型的结果在一个未用于构建模型的个体数据集上进行了验证。检查了受试者工作特征曲线下面积（AUC）。
结果
基于最佳子集的最优分类器在CT阴性受试者中的AUC为0.878，交叉验证的AUC为0.852，在验证数据集中的AUC为0.831。外部数据集（n = 254）中的最优误分类率为13%。
结论
如果根据病史、检查、影像学和运动性脑震荡评估工具3标准来定义脑震荡，则有可能生成一种基于眼动追踪的生物标志物，能够以相当高的敏感性和特异性检测脑震荡。
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