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苯二氮䓬类药物相关驾驶损害的行为和神经生理学特征

Behavioral and Neurophysiological Signatures of Benzodiazepine-Related Driving Impairments.

作者信息

Stone Bradly T, Correa Kelly A, Brown Timothy L, Spurgin Andrew L, Stikic Maja, Johnson Robin R, Berka Chris

机构信息

Advanced Brain Monitoring, Inc., Carlsbad, CA USA.

National Advanced Driving Simulator, Center for Computer Aided Design, The University of Iowa Iowa City, IA, USA.

出版信息

Front Psychol. 2015 Nov 26;6:1799. doi: 10.3389/fpsyg.2015.01799. eCollection 2015.

Abstract

Impaired driving due to drug use is a growing problem worldwide; estimates show that 18-23.5% of fatal accidents, and up to 34% of injury accidents may be caused by drivers under the influence of drugs (Drummer et al., 2003; Walsh et al., 2004; NHTSA, 2010). Furthermore, at any given time, up to 16% of drivers may be using drugs that can impair one's driving abilities (NHTSA, 2009). Currently, drug recognition experts (DREs; law enforcement officers with specialized training to identify drugged driving), have the most difficult time with identifying drivers potentially impaired on central nervous system (CNS) depressants (Smith et al., 2002). The fact that the use of benzodiazepines, a type of CNS depressant, is also associated with the greatest likelihood of causing accidents (Dassanayake et al., 2011), further emphasizes the need to improve research tools in this area which can facilitate the refinement of, or additions to, current assessments of impaired driving. Our laboratories collaborated to evaluate both the behavioral and neurophysiological effects of a benzodiazepine, alprazolam, in a driving simulation (miniSim(TM)). This drive was combined with a neurocognitive assessment utilizing time synched neurophysiology (electroencephalography, ECG). While the behavioral effects of benzodiazepines are well characterized (Rapoport et al., 2009), we hypothesized that, with the addition of real-time neurophysiology and the utilization of simulation and neurocognitive assessment, we could find objective assessments of drug impairment that could improve the detection capabilities of DREs. Our analyses revealed that (1) specific driving conditions were significantly more difficult for benzodiazepine impaired drivers and (2) the neurocognitive tasks' metrics were able to classify "impaired" vs. "unimpaired" with up to 80% accuracy based on lane position deviation and lane departures. While this work requires replication in larger studies, our results not only identified criteria that could potentially improve the identification of benzodiazepine intoxication by DREs, but also demonstrated the promise for future studies using this approach to improve upon current, real-world assessments of impaired driving.

摘要

因药物使用导致的酒驾问题在全球范围内日益严重;据估计,18% - 23.5%的致命事故以及高达34%的伤人事故可能是由受药物影响的驾驶员造成的(德鲁默等人,2003年;沃尔什等人,2004年;美国国家公路交通安全管理局,2010年)。此外,在任何给定时间,高达16%的驾驶员可能正在使用会损害驾驶能力的药物(美国国家公路交通安全管理局,2009年)。目前,药物识别专家(DREs;经过专门培训以识别酒驾的执法人员)在识别可能受中枢神经系统(CNS)抑制剂影响的驾驶员方面面临最大困难(史密斯等人,2002年)。使用苯二氮䓬类药物(一种CNS抑制剂)也与引发事故的最大可能性相关这一事实(达萨纳亚克等人,2011年),进一步强调了改进该领域研究工具的必要性,这些工具可以促进对当前酒驾评估的完善或补充。我们的实验室合作在驾驶模拟(miniSim(TM))中评估了苯二氮䓬类药物阿普唑仑的行为和神经生理效应。此次驾驶与利用时间同步神经生理学(脑电图、心电图)的神经认知评估相结合。虽然苯二氮䓬类药物的行为效应已得到充分表征(拉波波特等人,2009年),但我们假设,通过增加实时神经生理学以及利用模拟和神经认知评估,我们可以找到药物损害的客观评估方法,从而提高药物识别专家的检测能力。我们的分析表明:(1)特定驾驶条件对受苯二氮䓬类药物影响的驾驶员来说难度显著更高;(2)基于车道位置偏差和车道偏离,神经认知任务的指标能够以高达80%的准确率对“受损”与“未受损”进行分类。虽然这项工作需要在更大规模的研究中进行重复验证,但我们的结果不仅确定了可能有助于药物识别专家改进对苯二氮䓬类药物中毒识别的标准,还展示了未来使用这种方法改进当前现实世界中酒驾评估的前景。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ae6f/4659917/ae42aa614353/fpsyg-06-01799-g001.jpg

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