Innes Carrie R H, Jones Richard D, Dalrymple-Alford John C, Hayes Sarah, Hollobon Sue, Severinsen Julie, Smith Gwyneth, Nicholls Angela, Anderson Tim J
Van der Veer Institute for Parkinson's and Brain Research, Christchurch, New Zealand.
J Neurol Sci. 2007 Sep 15;260(1-2):188-98. doi: 10.1016/j.jns.2007.04.052. Epub 2007 Jun 4.
Brain disorders can lead to a decreased ability to perform the physical and cognitive functions necessary for safe driving. This study aimed to determine how accurately a battery of computerized sensory-motor and cognitive tests (SMCTests) could predict driving abilities in persons with brain disorders.
SMCTests and an independent on-road driving assessment were applied to 50 experienced drivers with brain disorders referred to a hospital-based driving assessment service. The patients comprised 36 males and 14 females, a mean age of 71.3 years (range 43-85 years) and diagnoses of 35 stroke, 4 traumatic brain injury, 4 Alzheimer's disease, and 7 other. Binary logistic regression (BLR) and nonlinear causal resource analysis (NCRA) were used to build model equations for prediction of on-road driving ability based on SMCTests performance.
BLR and NCRA correctly classified 94% and 90% of referrals respectively as on-road pass or fail. Leave-one-out cross-validation estimated that BLR and NCRA would correctly predict the classification of 86% and 76% respectively of an independent referral group as on-road pass or fail.
Compared with other studies, SMCTests have shown the highest predictive accuracy against true on-road driving ability as estimated in an independent data set and in persons with brain disorders. SMCTests also have the advantage of being able to comprehensively and objectively assess both sensory-motor and higher cognitive functions related to driving.
脑部疾病会导致执行安全驾驶所需的身体和认知功能的能力下降。本研究旨在确定一组计算机化的感觉运动和认知测试（SMCTests）能多准确地预测脑部疾病患者的驾驶能力。
对50名转诊至一家基于医院的驾驶评估服务机构的有脑部疾病的经验丰富驾驶员进行了SMCTests和独立的道路驾驶评估。患者包括36名男性和14名女性，平均年龄71.3岁（范围43 - 85岁），诊断包括35例中风、4例创伤性脑损伤、4例阿尔茨海默病和7例其他疾病。使用二元逻辑回归（BLR）和非线性因果资源分析（NCRA）基于SMCTests表现建立预测道路驾驶能力的模型方程。
BLR和NCRA分别将94%和90%的转诊者正确分类为道路驾驶通过或不通过。留一法交叉验证估计，BLR和NCRA分别能正确预测独立转诊组中86%和76%的人道路驾驶通过或不通过的分类。
与其他研究相比，在独立数据集和脑部疾病患者中，SMCTests针对实际道路驾驶能力显示出最高的预测准确性。SMCTests还具有能够全面、客观地评估与驾驶相关的感觉运动和高级认知功能的优势。
