Schepens Eye Research Institute, Massachusetts Eye and Ear, Boston, MA, USA; Department of Ophthalmology, Harvard Medical School, Boston, MA, USA.
Accid Anal Prev. 2013 Oct;59:537-47. doi: 10.1016/j.aap.2013.06.037. Epub 2013 Jul 16.
To conduct a pilot study to evaluate the predictive value of the Montreal Cognitive Assessment test (MoCA) and a brief test of multiple object tracking (MOT) relative to other tests of cognition and attention in identifying at-risk older drivers, and to determine which combination of tests provided the best overall prediction.
Forty-seven currently licensed drivers (58-95 years), primarily from a clinical driving evaluation program, participated. Their performance was measured on: (1) a screening test battery, comprising MoCA, MOT, Mini-Mental State Examination (MMSE), Trail-Making Test, visual acuity, contrast sensitivity, and Useful Field of View (UFOV) and (2) a standardized road test.
Eighteen participants were rated at-risk on the road test. UFOV subtest 2 was the best single predictor with an area under the curve (AUC) of .84. Neither MoCA nor MOT was a better predictor of the at-risk outcome than either MMSE or UFOV, respectively. The best four-test combination (MMSE, UFOV subtest 2, visual acuity and contrast sensitivity) was able to identify at-risk drivers with 95% specificity and 80% sensitivity (.91 AUC).
Although the best four-test combination was much better than a single test in identifying at-risk drivers, there is still much work to do in this field to establish test batteries that have both high sensitivity and specificity.
进行一项初步研究,评估蒙特利尔认知评估测试(MoCA)和多项物体追踪测试(MOT)的预测价值,与其他认知和注意力测试相比,这两种测试在识别高风险老年驾驶员方面的作用,并确定哪种测试组合能提供最佳的整体预测。
47 名目前持照的驾驶员(58-95 岁),主要来自临床驾驶评估计划,参与了该研究。他们的表现通过以下方式进行测量:(1)一个筛选测试组合,包括 MoCA、MOT、简易精神状态检查(MMSE)、连线测试、视力、对比敏感度和有用视野(UFOV),以及(2)标准化的道路测试。
18 名参与者在道路测试中被评为高风险。UFOV 子测试 2 是最佳的单一预测指标,曲线下面积(AUC)为 0.84。MoCA 和 MOT 都不如 MMSE 或 UFOV 分别预测高风险结果。最佳的四项测试组合(MMSE、UFOV 子测试 2、视力和对比敏感度)能够以 95%的特异性和 80%的敏感性(AUC 为 0.91)识别高风险驾驶员。
尽管最佳的四项测试组合在识别高风险驾驶员方面比单一测试要好得多,但在这个领域仍有很多工作要做,以建立具有高灵敏度和特异性的测试组合。