Family Medicine, University of Alberta, 205 8215 112 Street, Edmonton, Alberta, Canada T6G 2C8.
Age Ageing. 2013 Sep;42(5):577-81. doi: 10.1093/ageing/aft073. Epub 2013 Jul 28.
the medical community plays an important role in identifying drivers who may no longer be competent to drive due to illnesses such as dementia. Several office-based cognitive screening tools are currently used by the medical community, e.g. Mini-Mental State Examination, Trail Making Test (TMT), to assist in the identification of cognitively impaired (CI) at-risk drivers. However, the predictive validity of these tools is questionable.
to examine the predictive power of the TMT for on-road driving performance.
data from a prospective sample of CI and healthy older drivers were collected. TMT-A and -B (time and errors) served as predictor variables, with pass/fail on a scientifically based on-road assessment used as the criterion variable. Receiver operating characteristic (ROC) curve analysis was used to assess overall 'diagnostic' accuracy of TMT-A and -B for driving competency. Cut points from previous studies/guidelines were used to assess predictive power.
a total of 134 older drivers (mean age = 75.30; SD = 7.83) participated: 87 healthy controls and 47 CI individuals. All predictor variables, with the exception of TMT-A errors, were significantly correlated with driving outcome. However, results from ROC curve analyses indicated that only TMT-A and -B total time had moderate discriminative abilities. Results also indicate that the power of the TMT is the lowest where physicians need it most (e.g. identifying CI patients whose driving skills have declined to an unsafe level).
TMT-A and -B outcomes are most likely to be inaccurate in those whose driving competency has declined to an unsafe level, resulting in risks to both individual and public safety.
医学界在识别因痴呆等疾病而可能不再有驾驶能力的驾驶员方面发挥着重要作用。目前,医学界使用了几种基于办公室的认知筛查工具,例如简易精神状态检查、连线测试(TMT),以协助识别认知受损(CI)的有风险驾驶员。然而,这些工具的预测效度存在疑问。
检验 TMT 对道路驾驶表现的预测能力。
收集了一组 CI 和健康老年驾驶员的前瞻性样本数据。TMT-A 和 -B(时间和错误)作为预测变量,以基于科学的道路评估的通过/失败作为标准变量。使用接收者操作特征(ROC)曲线分析来评估 TMT-A 和 -B 对驾驶能力的总体“诊断”准确性。使用先前的研究/指南中的切点来评估预测能力。
共有 134 名老年驾驶员(平均年龄=75.30;SD=7.83)参与:87 名健康对照组和 47 名 CI 个体。除 TMT-A 错误外,所有预测变量均与驾驶结果显著相关。然而,ROC 曲线分析结果表明,只有 TMT-A 和 -B 总时间具有中等的区分能力。结果还表明,TMT 的效力在医生最需要的地方最低(例如,识别驾驶技能下降到不安全水平的 CI 患者)。
在驾驶能力下降到不安全水平的人群中,TMT-A 和 -B 的结果最有可能不准确,从而对个人和公共安全构成风险。