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Devos H.,Georgia Regents University | Devos H.,Catholic University of Leuven | Tant M.,Belgian Road Safety Institute | Akinwuntan A.E.,Georgia Regents University
Cerebrovascular Diseases | Year: 2014

Background: Little is known about the critical on-road driving skills that get affected after a stroke. The purpose of this study was to investigate the key on-road driving impairments and their associated cognitive deficits after a stroke. A second aim was to investigate if lateralization of stroke impacts results of the cognitive and on-road driving tests. Methods: In this cross-sectional study, 99 participants with a first-ever stroke who were actively driving prior to stroke underwent a cognitive battery and a standardized road test that evaluated 13 specific on-road driving skills. These onroad driving skills were mapped onto an existing, theoretical framework that categorized the on-road items into hierarchic clusters of operational, tactical, visuo-integrative, and mixed driving skills. The total score on the road test and the on-road decision, made by a certified fitness-to-drive expert, decided the main outcome. The critical on-road driving skills predicting the on-road decision were identified using logistic regression analysis. Linear regression analysis was employed to determine the cognitive impairments leading to poor total on-road scores. Analyses were repeated for rightand left-sided strokes.Results: In all, 37 persons scored poorly on the road test. These participants performed worse in all hierarchic clusters of on-road driving. Performances on the operational cluster and the visuo-integrative cluster best predicted on-road decisions (R 2 = 0.60). 'Lane changing' and 'understanding, insight, and quality of traffic participation' were the critical skill deficits leading to poor performance on the road test (R2 = 0.65). Divided attention was the main determinant of on-road scores in the total group (R2 = 0.06). Participants with right-sided stroke performed worse on visual field, visual neglect, visual scanning, visuo-constructive skills, and divided attention compared with those with left-sided stroke. Divided attention was the main determinant of total on-road scores in the right-sided stroke group (R2 = 0.10). A combination of visual scanning, speed of processing, and executive dysfunction yielded the best model to predict on-road scores in left-sided strokes (R2 = 0.46).Conclusions: Poor performance in the road test after stroke is determined by critical operational and visuo-integrative driving impairments. Specific and different driving evaluation and training programs are needed for right- and leftsided strokes. © 2014 S. Karger AG, Basel. © 2014 S. Karger AG, Basel. Source

Van der Linden T.,Ghent University | Van der Linden T.,National Institute of Criminalistics and Criminology | Silverans P.,Belgian Road Safety Institute | Verstraete A.G.,Ghent University
Drug Testing and Analysis | Year: 2014

The objective of this study was to compare the number of drivers who self-reported cannabis use by questionnaires to the results of toxicological analysis. During roadside surveys, 2957 respondents driving a personal car or van completed a questionnaire to report their use of drugs and medicines during the previous two weeks and to indicate the time of their last intake. Cannabis was analyzed in oral fluid by ultra performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS), in blood by gas chromatography-mass spectrometry (GC-MS). Frequencies in the time categories were calculated and compared with toxicological results. Diagnostic values were calculated for the time categories in which positive findings were to be expected (<4 h and <2 4h, respectively for tetrahydrocannabinol (THC) and delta9-tetrahydrocannabinol (THCCOOH) in blood, <12 h for THC in oral fluid). Most self-reported cannabis use was more than 12 h before driving. The sensitivity of the questionnaire was low, while the specificity and accuracy were high. Kappa statistics revealed a fair agreement between self-report and positive findings for THC in oral fluid and blood and moderate agreement with THCCOOH in blood. Self-report largely underestimates driving under the influence of cannabis, particularly recent cannabis use; therefore analysis of biological samples is necessary. © 2013 John Wiley & Sons, Ltd. Self-reporting could be an indicative measurement for driving under the influence of cannabis, but analysis of biological samples is still necessary. © 2013 John Wiley & Sons, Ltd. Source

Legrand S.-A.,Ghent University | Silverans P.,Belgian Road Safety Institute | de Paepe P.,Ghent University | Buylaert W.,Ghent University | Verstraete A.G.,Ghent University
Traffic Injury Prevention | Year: 2013

Objective: To estimate the percentage of drivers involved in a traffic crash in Belgium who have alcohol and drugs in their blood. Methods: Blood samples of the drivers injured in a traffic crash and admitted to the emergency departments of 5 hospitals in Belgium between January 2008 and May 2010 were analyzed for ethanol (with an enzymatic method) and 22 other psychoactive substances (with ultra-performance liquid chromatography with tandem mass spectrometry or gas chromatography-mass spectrometry). Results: One thousand seventy-eight drivers were included in the study. Alcohol (≥0.1 g/L) was the most common substance (26.2%). A large majority of the drivers (64%) who were positive for alcohol had a blood alcohol concentration (BAC) ≥1.3 g/L (legal limit in Belgium: 0.5 g/L). These high BACs were most frequent among male injured drivers. Cannabis was the most prevalent illicit drug (5.3%) and benzodiazepines (5.3%) were the most prevalent medicinal drugs. Approximately 1 percent of the drivers were positive for cocaine and amphetamines. No drivers tested positive for illicit opioids. Medicinal drugs were more likely to be found among female drivers and drivers older than 35 years, and alcohol and illicit drugs were more likely to be found among male drivers and drivers younger than 35 years. Conclusion: A high percentage of the injured drivers were positive for a psychoactive substance at the time of injury. Alcohol was the most common substance, with 80 percent of the positive drivers having a BAC ≥0.5 g/L. Compared to a roadside survey in the same area, drivers/riders with high BACs and combinations of drugs were overrepresented. Efforts should be made to increase alcohol and drug enforcement. The introduction of a categorization and labeling system might reduce driving under the influence of medicinal drugs by informing health care professionals and patients. © 2013 Copyright Taylor and Francis Group, LLC. Source

Van der Linden T.,Ghent University | Van der Linden T.,National Institute of Criminalistics and Criminology | Legrand S.-A.,Ghent University | Silverans P.,Belgian Road Safety Institute | Verstraete A.G.,Ghent University
Journal of Analytical Toxicology | Year: 2012

The objective of this study was to compare the number of drivers with drug concentrations above the legal cutoffs for driving under the influence of illicit substances in paired samples of blood and oral fluid. Between January 2008 and September 2009, 2,949 randomly selected drivers participated in a roadside survey. Each was asked to provide blood and oral fluid. Samples were analyzed for 11 illicit substances or metabolites by ultra-performance liquid chromatography-tandem mass spectrometry and gas chromatography- tandem mass spectrometry. Out of the 2,750 drivers who gave both blood and oral fluid, 28 (1.0%) had drug concentrations above the legal cutoff in blood and 71 (2.6%) were above the legal cutoff in oral fluid. Fifteen (7.5%) of the 199 drivers who gave an oral fluid sample but refused to provide blood tested positive, significantly more than drivers who provided both samples. Based on oral fluid analysis, 2.6 times more subjects tested positive for drugs compared to blood analysis. Those that refused to give a blood sample were 3 times more likely to test positive for drugs. Even in a survey that guaranteed total anonymity, people fearing a positive test result might have been more likely to refuse to give a blood sample. © The Author [2012]. Published by Oxford University Press. All rights reserved. Source

Devos H.,Catholic University of Leuven | Akinwuntan A.E.,Georgia Regents University | Nieuwboer A.,Catholic University of Leuven | Truijen S.,University of Antwerp | And 2 more authors.
Neurology | Year: 2011

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 (dw). Statistical heterogeneity was determined with the I 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 (dw 1.22; 95% confidence interval [CI] 1.01-1.44; I, 58%), Compass (dw 1.06; 95% CI 0.74-1.39; I, 36%), and Trail Making Test B (TMT B; dw 0.81; 95% CI 0.48-1.15; I, 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. ©2011 American Academy of Neurology. Source

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