Visual search and target detection during simulated driving in Parkinson's disease.

Patients with Parkinson's Disease (PD) often exhibit difficulties with visual search that may impede their ability to recognize landmarks and cars while driving. The main objective of this study was to investigate visual search performances of both billboards and cars in patients with PD using a driving simulator. A second objective was to examine the role of cognitive functions in performing the visual search task while driving. Nineteen patients with PD (age: 68 ±â€¯8yo, sex (Men/Women): 15/4) and 14 controls (age: 60 ±â€¯11yo, sex: 7/7) first performed a battery of cognitive tests. They then drove in a simulator and were instructed to follow a lead vehicle while searching for billboards with the letter A (stationary target) or red cars (moving target) among other distractors. Accuracy and response times of visual search were the main outcome variables. Standard deviation of lateral position (SDLP) was the secondary outcome. During driving, patients were less accurate in identifying the targets, particularly for the stationary billboards located in the outer periphery. Within the group of patients, significant correlations were found between several measures of cognitive tests and simulator-based visual search accuracy. By contrast, only the score on the MOCA test correlated significantly with visual search accuracy in controls. Findings suggest that patients with PD have impaired visual search for more eccentric stationary targets while driving a simulator, which is likely due to cognitive deficits. Difficulties identifying objects in the outer periphery may have implications for driving safety. Decreased functional field of view under increased cognitive load may have attributed to the difficulties identifying these landmarks. This may impact the ability to identify, anticipate, and respond to important information (e.g., pedestrians, navigation signs, landmarks), especially in complex driving situations (e.g. urban driving or intersections).Future studies should be conducted in a larger sample size to determine whether a visual search task on a driving simulator may predict on-road driving performances.

Validation of a short cognitive battery to screen for fitness-to-drive of people with multiple sclerosis.

BACKGROUND AND PURPOSE: Some symptoms of multiple sclerosis (MS) affect driving. In a recent study, performance on five cognitive tests predicted the on-road test performance of individuals with relapsing-remitting MS with 91% accuracy, 70% sensitivity and 97% specificity. However, the accuracy with which the battery will predict the driving performance of a different cohort that includes all types of MS is unknown. METHODS: Participants (n = 118; 48 ± 9 years of age; 97 females) performed a comprehensive off-road evaluation that lasted about 3 h and a standardized on-road test that lasted approximately 45 min over a 2-day period within the same week. Performance on the five cognitive tests was used to predict participants' performance on the standardized on-road test. RESULTS: Performance on the five tests together predicted outcome of the on-road test with 82% accuracy, 42% sensitivity and 90% specificity. CONCLUSIONS: The accuracy of predicting the on-road performance of a new MS cohort using performance on the battery of five cognitive tests remained very high (82%). The battery, which was administrable in <45 min and cost ~$150, was better at identifying those who actually passed the on-road test (90% specificity). The sensitivity (42%) of the battery indicated that it should not be used as the sole determinant of poor driving-related cognitive skills. A fail performance on the battery should only imply that more comprehensive testing is warranted.

Pupillary response to cognitive workload during saccadic tasks in Parkinson's disease.

The relationship between cognitive workload and cognitive impairments in Parkinson's disease (PD) is currently not well known. This study compared cognitive workload during saccadic tasks between patients with PD and controls. Sixteen controls, 11 patients with no obvious cognitive impairment (PD-NCI) (MOCA score≥26), and 10 PD patients with cognitive impairment (PD-CI) (MOCA score<26) performed prosaccade and antisaccade tasks. Cognitive workload, extracted via pupil recording, and other eye metrics were compared between the three groups. PD-NCI patients exhibited greater cognitive workload than controls in the prosaccade task. PD-CI patients also showed more cognitive workload in the prosaccade task than PD-NCI patients and controls. No other differences in eye metrics were found between the three groups. Cognitive workload could be used to differentiate between different cognitive states in PD. The causal relationship between increased cognitive workload and cognitive decline in PD-NCI should be the focus of future studies.

Fitness-to-drive agreements after stroke: medical versus practical recommendations.

BACKGROUND AND PURPOSE: Physicians often struggle to screen out patients who are no longer fit to drive after stroke. The agreement between the recommendations of physicians and on-road assessors with regard to fitness to drive after stroke was investigated. METHODS: In this retrospective study, 735 patients with stroke underwent medical, visual and road tests at an official fitness-to-drive center of the Belgian Road Safety Institute. Physicians provided medical fitness-to-drive recommendations using one of three categories (favorable, reserved or unfavorable). On-road assessors used the same three categories to make practical fitness-to-drive recommendations. Agreement between the medical and practical fitness-to-drive recommendations was calculated using the percentage of agreement and prevalence and bias adjusted kappa (PABAK). Area under the curve (AUC) was used to predict the medical and practical recommendations after stepwise logistic regression analyses. RESULTS: The percentage of agreement was 73% and the PABAK was 0.60 (P < 0.0001). Physicians disagreed on 92% of patients classified as unfavorable and 80% of those classified as reserved by the on-road assessor. Previous visits to the driving center and number of comorbidities predicted medical fitness-to-drive recommendations (AUC = 0.68). Age, previous visits to the center, binocular acuity and driving experience constituted the best model to predict practical fitness-to-drive recommendations (AUC = 0.70). CONCLUSIONS: Although there was a moderate agreement between the medical and practical fitness-to-drive recommendations, physicians were less likely to screen out those patients who may pose an actual risk on the road. Demographic, clinical and driving factors differently affected the medical and practical fitness-to-drive recommendations.

Screening for fitness to drive after stroke: a systematic review and meta-analysis.

OBJECTIVE: To identify the best determinants of fitness to drive after stroke, following a systematic review and meta-analysis. METHODS: Twenty databases were searched, from inception until May 1, 2010. Potentially relevant studies were reviewed by 2 authors for eligibility. Methodologic quality was assessed by Newcastle-Ottawa scores. The fitness-to-drive outcome was a pass-fail decision following an on-road evaluation. Differences in off-road performance between the pass and fail groups were calculated using weighted mean effect sizes (d(w)). Statistical heterogeneity was determined with the I² statistic. Random-effects models were performed when the assumption of homogeneity was not met. Cutoff scores of accurate determinants were estimated via receiver operating characteristic analyses. RESULTS: Thirty studies were included in the systematic review and 27 in the meta-analysis. Out of 1,728 participants, 938 (54%) passed the on-road evaluation. The best determinants were Road Sign Recognition (d(w) 1.22; 95% confidence interval [CI] 1.01-1.44; I(2), 58%), Compass (d(w) 1.06; 95% CI 0.74-1.39; I², 36%), and Trail Making Test B (TMT B; d(w) 0.81; 95% CI 0.48-1.15; I(2), 49%). Cutoff values of 8.5 points for Road Sign Recognition, 25 points for Compass, and 90 seconds for TMT B were identified to classify unsafe drivers with accuracies of 84%, 85%, and 80%, respectively. Three out of 4 studies found no increased risk of accident involvement in persons cleared to resume driving after stroke. CONCLUSIONS: The Road Sign Recognition, Compass, and TMT B are clinically administrable office-based tests that can be used to identify persons with stroke at risk of failing an on-road assessment.

Prediction of driving after stroke: a prospective study.

BACKGROUND: The process of determining whether patients with stroke should drive again often involves off-road evaluations and road tests that usually take about 2 to 3 h to complete. OBJECTIVES: This prospective study sought to identify the combination of tests that best predicts fitness to drive after stroke. The main aim was to develop a short and predictive predriving assessment battery. METHODS: Sixty-eight consecutive stroke patients were studied who performed a mandatory predriving assessment at the Belgian Road Safety Institute, Brussels, within 18 months. Performance in a predriving assessment included medical examination (when needed), visual and neuropsychological evaluations, and an on-road test. Based on these assessments, a physician, psychologist, and the driving safety expert who administered the tests decided if a subject was either "fit to drive,""temporarily unfit to drive," or "unfit to drive." RESULTS: Logistic regression analysis revealed a combination of visual neglect, figure of Rey, and on-road tests as the model that best predicted (R(2) = 0.73) fitness to drive after stroke. Using a discriminant function that included the 3 tests of the logistic model, the fitness to drive judgments of 59 (86.8%) subjects were correctly predicted. The sensitivity and specificity of the predictions were 79.4% and 94.1%, respectively. CONCLUSION: Fitness to drive after stroke can be predicted from performance on a few road-related tests with a high degree of accuracy. However, some individuals require extended assessments and further tests.

Effect of simulator training on driving after stroke: a randomized controlled trial.

BACKGROUND: Neurologically impaired persons seem to benefit from driving-training programs, but there is no convincing evidence to support this notion. The authors therefore investigated the effect of simulator-based training on driving after stroke. METHODS: Eighty-three first-ever subacute stroke patients entered a 5-week 15-hour training program in which they were randomly allocated to either an experimental (simulator-based training) or control (driving-related cognitive tasks) group. Performance in off-road evaluations and an on-road test were used to assess the driving ability of subjects pre- and post-training. Outcome of an official predriving assessment administered 6 to 9 months poststroke was also considered. RESULTS: Both groups significantly improved in a visual and many neuropsychological evaluations and in the on-road test after training. There were no significant differences between both groups in improvements from pre- to post-training except in the "road sign recognition test" in which the experimental subjects improved more. Significant improvements in the three-class decision ("fit to drive," "temporarily unfit to drive," and "unfit to drive") were found in favor of the experimental group post-training. Academic qualification and overall disability together determined subjects that benefited most from the simulator-based driving training. Significantly more experimental subjects (73%) than control subjects (42%) passed the follow-up official predriving assessment and were legally allowed to resume driving. CONCLUSIONS: Simulator-based driving training improved driving ability, especially for well educated and less disabled stroke patients. However, the findings of the study may have been modified as a result of the large number of dropouts and the possibility of some neurologic recovery unrelated to training.