OBJECTIVE: Brain disorders can lead to a decreased ability to perform the physical and cognitive functions necessary for safe driving. This study aimed to determine how accurately a battery of computerized sensory-motor and cognitive tests (SMCTests) could predict driving abilities in persons with brain disorders. METHODS: SMCTests and an independent on-road driving assessment were applied to 50 experienced drivers with brain disorders referred to a hospital-based driving assessment service. The patients comprised 36 males and 14 females, a mean age of 71.3 years (range 43-85 years) and diagnoses of 35 stroke, 4 traumatic brain injury, 4 Alzheimer's disease, and 7 other. Binary logistic regression (BLR) and nonlinear causal resource analysis (NCRA) were used to build model equations for prediction of on-road driving ability based on SMCTests performance. RESULTS: BLR and NCRA correctly classified 94% and 90% of referrals respectively as on-road pass or fail. Leave-one-out cross-validation estimated that BLR and NCRA would correctly predict the classification of 86% and 76% respectively of an independent referral group as on-road pass or fail. CONCLUSIONS: Compared with other studies, SMCTests have shown the highest predictive accuracy against true on-road driving ability as estimated in an independent data set and in persons with brain disorders. SMCTests also have the advantage of being able to comprehensively and objectively assess both sensory-motor and higher cognitive functions related to driving.
OBJECTIVE:Brain disorders can lead to a decreased ability to perform the physical and cognitive functions necessary for safe driving. This study aimed to determine how accurately a battery of computerized sensory-motor and cognitive tests (SMCTests) could predict driving abilities in persons with brain disorders. METHODS: SMCTests and an independent on-road driving assessment were applied to 50 experienced drivers with brain disorders referred to a hospital-based driving assessment service. The patients comprised 36 males and 14 females, a mean age of 71.3 years (range 43-85 years) and diagnoses of 35 stroke, 4 traumatic brain injury, 4 Alzheimer's disease, and 7 other. Binary logistic regression (BLR) and nonlinear causal resource analysis (NCRA) were used to build model equations for prediction of on-road driving ability based on SMCTests performance. RESULTS: BLR and NCRA correctly classified 94% and 90% of referrals respectively as on-road pass or fail. Leave-one-out cross-validation estimated that BLR and NCRA would correctly predict the classification of 86% and 76% respectively of an independent referral group as on-road pass or fail. CONCLUSIONS: Compared with other studies, SMCTests have shown the highest predictive accuracy against true on-road driving ability as estimated in an independent data set and in persons with brain disorders. SMCTests also have the advantage of being able to comprehensively and objectively assess both sensory-motor and higher cognitive functions related to driving.