BACKGROUND: We previously developed a short clinical battery, consisting of contrast sensitivity, Clinical Dementia Rating, the Unified Parkinson's Disease Rating Scale-motor section (UPDRS III), and disease duration, which correctly classified 90% of drivers with Parkinson's Disease (PD). The aim of this study was to validate that screening battery in a different sample of PD drivers. METHODS: Sixty drivers with PD were enrolled to validate our original screening battery to predict driving fitness decisions (pass-fail) by a state agency where drivers underwent detailed visual, cognitive, and on-road testing. RESULTS: Twenty-four participants (40%) failed the driving evaluation. The screening battery correctly classified 46 (77%) participants (sensitivity and negative predictive value = 96%; specificity and positive predictive value = 64%). Adding other clinical predictors (e.g., age of onset, Hoehn-Yahr stage instead of UPDRS III) failed to improve the specificity of the model when the sensitivity was kept constant at 96%. However, a driving simulator evaluation improved the specificity of the model to 94%. CONCLUSIONS: The original clinical battery proved to be a valid screening tool that accurately identifies fit drivers with PD and select those who need more detailed testing at specialized centers.
BACKGROUND: We previously developed a short clinical battery, consisting of contrast sensitivity, Clinical Dementia Rating, the Unified Parkinson's Disease Rating Scale-motor section (UPDRS III), and disease duration, which correctly classified 90% of drivers with Parkinson's Disease (PD). The aim of this study was to validate that screening battery in a different sample of PD drivers. METHODS: Sixty drivers with PD were enrolled to validate our original screening battery to predict driving fitness decisions (pass-fail) by a state agency where drivers underwent detailed visual, cognitive, and on-road testing. RESULTS: Twenty-four participants (40%) failed the driving evaluation. The screening battery correctly classified 46 (77%) participants (sensitivity and negative predictive value = 96%; specificity and positive predictive value = 64%). Adding other clinical predictors (e.g., age of onset, Hoehn-Yahr stage instead of UPDRS III) failed to improve the specificity of the model when the sensitivity was kept constant at 96%. However, a driving simulator evaluation improved the specificity of the model to 94%. CONCLUSIONS: The original clinical battery proved to be a valid screening tool that accurately identifies fit drivers with PD and select those who need more detailed testing at specialized centers.