Christine E. Lynn College of Nursing, Florida Atlantic University, Boca Raton, FL, USA.
Christine E. Lynn College of Nursing, Florida Atlantic University, Boca Raton, FL, USA.
J Am Med Dir Assoc. 2024 Aug;25(8):105054. doi: 10.1016/j.jamda.2024.105054. Epub 2024 Jun 4.
The purpose of this study was to identify the most parsimonious combination of cognitive tests that accurately predicts the likelihood of passing an on-road driving evaluation in order to develop a screening measure that can be administered as an in-office test.
This was a psychometric study of the new test's diagnostic accuracy.
The study was conducted at the Florida Atlantic University's Memory Center and Clinical Research Unit, both easily accessible to older drivers. Participants were older drivers who received a driving evaluation at the Memory Center and agreed to have their results included in the Driving Repository and community-based older drivers who volunteered to participate.
Mini-Mental State Exam (MMSE), Trail Making Tests A and B, Clock Test, Hopkins Verbal Learning Test, and Driving Health Inventory results were compared with an on-road driving evaluation to identify those tests that best predict the ability to pass the on-road evaluation.
Altogether, 412 older drivers, 179 men and 233 women, were included in the analysis. Fifty-four percent of Driving Repository participants failed the on-road evaluation compared with 8% of the community sample. The highest correlation to the on-road evaluation was Trails B time in seconds r = -0.713 (P < .001). Variables with high multicollinearity and/or low correlation with the on-road evaluation were eliminated and sets of receiver operating characteristics curves were generated to assess the predictive accuracy of the remaining tests. A linear combination of Trails B in seconds and MMSE using the highest of the Serial 7s or WORLD spelled backward scores accounted for the highest area under the curve of 0.915. Finally, an algorithm was created to rapidly generate the prediction for an individual patient.
The Fit2Drive algorithm demonstrated a strong 91.5% predictive accuracy. Usefulness in office-based patient consultations is promising but remains to be rigorously tested.
本研究旨在确定最简约的认知测试组合，以准确预测通过路考评估的可能性，从而开发一种可在办公室进行的筛选测试。
这是一项新测试诊断准确性的心理测量学研究。
该研究在佛罗里达大西洋大学记忆中心和临床研究单位进行，这些地方对老年驾驶员来说都很容易到达。参与者为在记忆中心接受驾驶评估并同意将其结果纳入驾驶档案的老年驾驶员，以及自愿参加的社区老年驾驶员。
比较简易精神状态检查（MMSE）、连线测试 A 和 B、时钟测试、霍普金斯言语学习测试和驾驶健康问卷的结果与路考评估，以确定哪些测试能最好地预测通过路考评估的能力。
共有 412 名老年驾驶员（179 名男性和 233 名女性）纳入分析。与社区样本的 8%相比，驾驶档案参与者中有 54%未能通过路考评估。与路考评估相关性最高的是连线测试 B 的秒数 r=-0.713（P<0.001）。具有高多重共线性和/或与路考评估低相关性的变量被排除，并生成了一系列接收者操作特征曲线，以评估剩余测试的预测准确性。Trails B 秒数和 MMSE 的线性组合，使用 Serial 7s 或 WORLD 倒拼的最高分，可得出最高的曲线下面积为 0.915。最后，创建了一个算法来快速为个体患者生成预测结果。
Fit2Drive 算法显示出了强大的 91.5%预测准确性。在基于办公室的患者咨询中具有一定的实用性，但仍需进行严格测试。
