BACKGROUND: We previously reported that performance on the Stroke Driver Screening Assessment (SDSA), a battery of four cognitive tests that takes less than 30 min to administer, predicted the driving performance of participants with multiple sclerosis (MS) on a road test with 86% accuracy, 80% sensitivity, and 88% specificity. OBJECTIVES: In this study, we further investigated if the addition of driving-related physical and visual tests and other previously identified cognitive predictors, including performance on the Useful Field of View test, will result in a better accuracy of predicting participants' on-road driving performance. METHODS: Forty-four individuals with relapsing-remitting MS (age = 46 ± 11 years, 37 females) and Expanded Disability Status Scale values between 1 and 7 were administered selected physical, visual and cognitive tests including the SDSA. The model that explained the highest variance of participants' performance on a standardized road test was identified using multiple regression analysis. A discriminant equation containing the tests included in the best model was used to predict pass or fail performance on the test. RESULTS: Performance on 12 cognitive and three visual tests were significantly associated with performance on the road test. Five of the tests together explained 59% of the variance and predicted the pass or fail outcome of the road test with 91% accuracy, 70% sensitivity, and 97% specificity. CONCLUSION: Participants' on-road performance was more accurately predicted by the model identified in this study than using only performance on the SDSA test battery. The five psychometric/off-road tests should be used as a screening battery, after which a follow-up road test should be conducted to finally decide the fitness to drive of individuals with relapsing-remitting MS. Future studies are needed to confirm and validate the findings in this study.
BACKGROUND: We previously reported that performance on the Stroke Driver Screening Assessment (SDSA), a battery of four cognitive tests that takes less than 30 min to administer, predicted the driving performance of participants with multiple sclerosis (MS) on a road test with 86% accuracy, 80% sensitivity, and 88% specificity. OBJECTIVES: In this study, we further investigated if the addition of driving-related physical and visual tests and other previously identified cognitive predictors, including performance on the Useful Field of View test, will result in a better accuracy of predicting participants' on-road driving performance. METHODS: Forty-four individuals with relapsing-remitting MS (age = 46 ± 11 years, 37 females) and Expanded Disability Status Scale values between 1 and 7 were administered selected physical, visual and cognitive tests including the SDSA. The model that explained the highest variance of participants' performance on a standardized road test was identified using multiple regression analysis. A discriminant equation containing the tests included in the best model was used to predict pass or fail performance on the test. RESULTS: Performance on 12 cognitive and three visual tests were significantly associated with performance on the road test. Five of the tests together explained 59% of the variance and predicted the pass or fail outcome of the road test with 91% accuracy, 70% sensitivity, and 97% specificity. CONCLUSION:Participants' on-road performance was more accurately predicted by the model identified in this study than using only performance on the SDSA test battery. The five psychometric/off-road tests should be used as a screening battery, after which a follow-up road test should be conducted to finally decide the fitness to drive of individuals with relapsing-remitting MS. Future studies are needed to confirm and validate the findings in this study.