Journal of the American Geriatrics Society · 2013
Predicting on-road assessment pass and fail outcomes in older drivers with cognitive impairment using a battery of computerized sensory-motor and cognitive tests.
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Hoggarth Petra A, Innes Carrie R H, Dalrymple-Alford John C, Jones Richard D
Аннотация
OBJECTIVES: To generate a robust model of computerized sensory-motor and cognitive test performance to predict on-road driving assessment outcomes in older persons with diagnosed or suspected cognitive impairment.
DESIGN: A logistic regression model classified pass–fail outcomes of a blinded on-road driving assessment. Generalizability of the model was tested using leave-one-out cross-validation.
SETTING: Three specialist clinics in New Zealand.
PARTICIPANTS: Drivers (n=279; mean age 78.4, 65% male) with diagnosed or suspected dementia, mild cognitive impairment, unspecified cognitive impairment, or memory problems referred for a medical driving assessment.
MEASUREMENTS: A computerized battery of sensory-motor and cognitive tests and an on-road medical driving assessment.
RESULTS: One hundred fifty-five participants (55.5%) received an on-road fail score. Binary logistic regression correctly classified 75.6% of the sample into on-road pass and fail groups. The cross-validation indicated accuracy of the model of 72.0% with sensitivity for detecting on-road fails of 73.5%, specificity of 70.2%, positive predictive value of 75.5%, and negative predictive value of 68%.
CONCLUSION: The off-road assessment prediction model resulted in a substantial number of people who were assessed as likely to fail despite passing an on-road assessment and vice versa. Thus, despite a large multicenter sample, the use of off-road tests previously found to be useful in other older populations, and a carefully constructed and tested prediction model, off-road measures have yet to be found that are sufficiently accurate to allow acceptable determination of on-road driving safety of cognitively impaired older drivers.
DESIGN: A logistic regression model classified pass–fail outcomes of a blinded on-road driving assessment. Generalizability of the model was tested using leave-one-out cross-validation.
SETTING: Three specialist clinics in New Zealand.
PARTICIPANTS: Drivers (n=279; mean age 78.4, 65% male) with diagnosed or suspected dementia, mild cognitive impairment, unspecified cognitive impairment, or memory problems referred for a medical driving assessment.
MEASUREMENTS: A computerized battery of sensory-motor and cognitive tests and an on-road medical driving assessment.
RESULTS: One hundred fifty-five participants (55.5%) received an on-road fail score. Binary logistic regression correctly classified 75.6% of the sample into on-road pass and fail groups. The cross-validation indicated accuracy of the model of 72.0% with sensitivity for detecting on-road fails of 73.5%, specificity of 70.2%, positive predictive value of 75.5%, and negative predictive value of 68%.
CONCLUSION: The off-road assessment prediction model resulted in a substantial number of people who were assessed as likely to fail despite passing an on-road assessment and vice versa. Thus, despite a large multicenter sample, the use of off-road tests previously found to be useful in other older populations, and a carefully constructed and tested prediction model, off-road measures have yet to be found that are sufficiently accurate to allow acceptable determination of on-road driving safety of cognitively impaired older drivers.