Hannes Devos

Time course of trunk, arm, leg, and functional recovery after ischemic stroke

Background. Patterns of recovery provide useful information concerning the potential of physical recovery over time and therefore the setting of realistic goals for rehabilitation programs. Objective. To compare the time course of trunk recovery with the patterns of recovery of arm, leg, and functional ability. Methods . Consecutive stroke patients were recruited in 2 acute neurology wards. Participants were evaluated at 1 week, 1 month, and 3 and 6 months after stroke. Patients were assessed with the Trunk Impairment Scale, Fugl-Meyer arm and leg test, and Barthel Index. Results. Thirty-two patients were included in the study. There were no dropouts. Repeated measures analysis of the recovery patterns of motor and functional performance revealed the most striking improvement for all measures from 1 week to 1 month (P value between .0021 and <.0001) and a significant improvement from 1 month to 3 months after stroke (P value ranges from .0008 to <.0001). No significant improvement was found between 3 and 6 months after stroke for any of the measures. Statistical analysis revealed no significant difference between time course of trunk, arm, leg, and functional recovery (P = .2565). No significant differences in level of motor and functional recovery were found at the different time points. Conclusions. Separate analyses of motor and functional recovery patterns after stroke confirm the importance of the first month for recovery. Contrary to common belief, the time course of recovery of the trunk is similar to the recovery of arm, leg, and functional ability.

Predictors of fitness to drive in people with Parkinson disease

Objective: To develop an efficient clinical screening battery to accurately predict the fitness to drive in people with Parkinson disease (PD). Methods: This prospective study included 80 participants: 40 patients with PD and 40 healthy age- and sex-matched control subjects. All participants were assessed using a driving simulator, a driving history survey, and the Clinical Dementia Rating. The patients with PD also underwent a clinical test battery and an evaluation of fitness to drive performed by an official center, which included visual, cognitive, and on-road tests. A two-class decision from this driving assessment center was the main outcome measure. Results: A screening battery assessing four clinical variables (disease duration, contrast sensitivity, Clinical Dementia Rating, and motor part of the Unified Parkinson’s Disease Rating Scale) provided the best model (R2 = 0.52) to predict the fitness to drive and correctly classified 36 (90%) of the patients with PD as pass or fail (sensitivity = 91%, specificity = 90%). The Test Ride for Investigating Practical fitness to drive (TRIP) driving simulator score discriminated significantly between drivers with PD and their healthy peers (p = 0.0008). When the TRIP driving simulator score was added to the clinical model, the total explained variance increased (R2 = 0.60) and correctly classified 39 (97.5%) of drivers with PD into the pass/fail category (sensitivity = 91%, specificity = 100%). Conclusions: A short clinical screening battery that measures disease duration, contrast sensitivity, cognitive and motor functions can predict fitness to drive in people with Parkinson disease with a high degree of accuracy. GLOSSARY: ADL = activities of daily living; CDR = Clinical Dementia Rating; CS = contrast sensitivity; DBS = deep brain stimulator; ESS = Epworth Sleepiness Scale; IQR = interquartile range (Q1–Q3); NA = not applicable; PD = Parkinson disease; rb = biserial correlation coefficient; rrb = rank biserial correlation coefficient; rs = Spearman rank correlation coefficient; TRIP = Test Ride for Investigating Practical fitness to drive; UFOV = useful field of view; UPDRS II = Unified Parkinson’s Disease Rating Scale, activities of daily living; UPDRS III = Unified Parkinson’s Disease Rating Scale, motor scale; w = Wilcoxon rank sum test.

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 (dw). 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 (dw 1.22; 95% confidence interval [CI] 1.01–1.44; I2, 58%), Compass (dw 1.06; 95% CI 0.74–1.39; I2, 36%), and Trail Making Test B (TMT B; dw 0.81; 95% CI 0.48–1.15; I2, 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.

Comparison of the Effect of Two Driving Retraining Programs on On-Road Performance After Stroke:

Background. Several driving retraining programs have been developed to improve driving skills after stroke. Those programs rely on different rehabilitation concepts. Objectives. The current study sought to examine the specific carryover effect of driving skills of a comprehensive training program in a driving simulator when compared with a cognitive training program. Methods. Further analysis from a previous randomized controlled trial that investigated the effect of simulator training on driving after stroke. Forty-two participants received simulator-based driving training, whereas 41 participants received cognitive training for 15 hours. Overall performance in the on-road test and each of its 13 items were compared between groups immediately posttraining and at 6 months poststroke. Results. Generalized estimating equation analysis showed that the total score on the on-road test and each item score improved significantly over time for both groups. Those who received driving simulator training achieved better results when compared with the cognitive training group in the overall on-road score and the items of anticipation and perception of signs, visual behavior and communication, quality of traffic participation, and turning left. Most of the differences in improvement between the 2 interventions were observed at 6 months poststroke. Conclusions . Contextual training in a driving simulator appeared to be superior to cognitive training to treat impaired on-road driving skills after stroke. The effects were primarily seen in visuointegrative driving skills. Our results favor the implementation of driving simulator therapy in the conventional rehabilitation program of subacute stroke patients with mild deficits.

Explaining freezing of gait in Parkinson's disease: Motor and cognitive determinants

Freezing of gait (FOG) is part of a complex clinical picture in Parkinsons disease (PD) and is largely refractory to standard care. Diverging hypotheses exist about its origins, but a consolidated view on what determines FOG is lacking. The aim of this study was to develop an integrative model of FOG in people with PD. This cross‐sectional study included 51 Parkinson subjects: 24 patients without FOG and 27 with FOG matched for age, gender, and disease severity. Subjects underwent an extensive clinical test battery evaluating general disease characteristics, gait and balance, nongait freezing, and cognitive functions. The relative contribution of these outcomes to FOG was determined using logistic regression analysis. The combination of the following four independent contributors provided the best explanatory model of FOG (R2 = 0.49): nongait freezing; levodopa equivalent dose (LED); cognitive impairment; and falls and balance problems. The model yields a high‐risk profile for FOG (P > 95%) when Parkinson patients are affected by at least one type of nongait freezing (e.g., freezing of other repetitive movements), falls or balance problems during the last 3 months, and a Scales for Outcomes in Parkinsons Disease‐Cognition score below 28. A high LED further increases the risk of FOG to 99%. Nongait freezing, increased dopaminergic drug dose, cognitive deficits, and falls and balance problems are independent determinants of FOG in people with PD and may play a synergistic role in its manifestation.

Effects of deep brain stimulation of the subthalamic nucleus on freezing of gait in Parkinson's disease: a prospective controlled study

Background Freezing of gait (FOG) is a debilitating gait disorder in Parkinsons disease (PD) with partial responsiveness to dopaminergic medication. To date, notions about the effects of subthalamic deep brain stimulation (STN-DBS) on FOG remain controversial. Objectives To compare the effects of bilateral STN-DBS and continued best medical treatment (BMT) on FOG occurrence, FOG severity and clinical outcomes in PD patients at 6 and 12 months follow-up. Methods In this prospective, controlled study, 41 PD patients with at least 5 years disease duration participated. Twenty-four subjects (20 with FOG) were treated with STN-DBS and seventeen (15 with FOG) continued BMT. The primary outcome was the New Freezing of Gait Questionnaire (NFOGQ) at 6 months postsurgery. Other outcomes were the NFOGQ at 12 months and clinical outcomes (Unified Parkinsons Disease Rating Scale III (UPDRS III), timed gait, falls and quality of life) at both time points. Results STN-DBS increased the likelihood to convert from being a freezer to a non-freezer at 6 and 12 months follow-up (relative risk reduction=0.4). However, 45% of baseline freezers still experienced FOG 6 and 12 months postsurgery although with reduced severity. Three baseline non-freezers (1/2 BMT-treated, 2/4 STN-DBS-treated) developed FOG during follow-up. STN-DBS-induced benefits on FOG were mostly mediated by baseline levodopa equivalent dose, altered medication-intake and reduced motor fluctuations. Conclusions In contrast to continued BMT, STN-DBS reduced FOG occurrence and severity at 6 months postsurgery with largely sustained effects at 12 months follow-up. Longer follow-up periods are needed to test whether FOG improvements after STN-DBS persist with disease progression.

Driving and off‐road impairments underlying failure on road testing in Parkinson's disease

Parkinsons disease (PD) affects driving ability. We aimed to determine the most critical impairments in specific road skills and in clinical characteristics leading to failure on a road test in PD. In this cross‐sectional study, certified driving assessment experts evaluated specific driving skills in 104 active, licensed drivers with PD using a standardized, on‐road checklist and issued a global decision of pass/fail. Participants also completed an off‐road evaluation assessing demographic features, disease characteristics, motor function, vision, and cognition. The most important driving skills and off‐road predictors of the pass/fail outcome were identified using multivariate stepwise regression analyses. Eighty‐six (65%) passed and 36 (35%) failed the on‐road driving evaluation. Persons who failed performed worse on all on‐road items. When adjusted for age and gender, poor performances on lateral positioning at low speed, speed adaptations at high speed, and left turning maneuvers yielded the best model that determined the pass/fail decision (R2 = 0.56). The fail group performed poorer on all motor, visual, and cognitive tests. Measures of visual scanning, motor severity, PD subtype, visual acuity, executive functions, and divided attention were independent predictors of pass/fail decisions in the multivariate model (R2 = 0.60). Our study demonstrated that failure on a road test in PD is determined by impairments in specific driving skills and associated with deficits in motor, visual, executive, and visuospatial functions. These findings point to specific driving and off‐road impairments that can be targeted in multimodal rehabilitation programs for drivers with PD.

Determinants of fitness to drive in Huntington disease

Objectives: To identify the most accurate clinical predictors of fitness to drive (FTDr) in Huntington disease (HD). Methods: This cross-sectional study included 60 active drivers: 30 patients with manifest HD (8 women) and 30 age- and gender-matched healthy controls. Mean (SD) age of the HD group was 50 (12) years and median (Q1–Q3) disease duration was 24 (12–48) months. A clinical battery consisting of a driving history questionnaire, the cognitive section of the Unified Huntingtons Disease Rating Scale (UHDRS), Trail Making Test, and Mini-Mental State Examination, as well as a driving simulator evaluation, were administered to all participants. Additionally, the subjects with HD completed the motor, behavioral, and Total Functional Capacity sections of the UHDRS and underwent an official FTDr evaluation comprising visual, neuropsychological, and on-road tests. The blinded neurologists appraisal of FTDr and the 3 most predictive clinical tests were compared with the official pass/fail FTDr decision. Results: The patients with HD performed worse on all tests of the clinical battery and driving simulator than the healthy controls. Fifteen patients with HD (50) failed the FTDr evaluation. The blinded neurologist correctly classified 21 patients (70%). The Symbol Digit Modalities Test, Stroop word reading, and Trail Making Test B provided the best model (R2 = 0.49) to predict FTDr, correctly classifying 26 patients (87%). Conclusions: Half of active drivers with HD fail a driving evaluation and pose a potential hazard on the road. Our results suggest that those at risk can be accurately identified using a clinical screening tool.

Effect of Simulator Training on Fitness-to-Drive after Stroke: a 5-Year Follow-up of a Randomized Controlled Trial

Background. No long-term studies have been reported on the effect of training programs on driving after stroke. Objectives. The authors’ primary aim was to determine the effect of simulator versus cognitive rehabilitation therapy on fitness-to-drive at 5 years poststroke. A second aim was to investigate differences in clinical characteristics between stroke survivors who resumed and stopped driving. Methods. In a previously reported randomized controlled trial, 83 stroke survivors received 15 hours of simulator training (n = 42) or cognitive therapy (n = 41). In this 5-year follow-up study, 61 participants were reassessed. Fitness-to-drive decisions were obtained from medical, visual, neuropsychological, and on-road tests; 44 participants (simulator group, n = 21; cognitive group, n = 23) completed all assessments. The primary outcome measures were fitness-to-drive decision and current driving status. Results. The authors found that 5 years after stroke, 18 of 30 participants (60%) in the simulator group were considered fit to drive, compared with 15 of 31 (48%) in the cognitive group (P = .36); 34 of 61 (56%) participants were driving. Current drivers were younger (P = .04), had higher Barthel scores (P = .008), had less comorbidity (P = .01), and were less severely depressed (P = .02) than those who gave up driving. Conclusions. The advantage of simulator-based driving training over cognitive rehabilitation therapy, evident at 6 months poststroke, had faded 5 years later. Poststroke drivers were younger and less severely affected and depressed than nondrivers.

FIve-yeAR MORTALITy AND ReLATeD pROgNOSTIC FACTORS AFTeR INpATIeNT STROke RehABILITATION: A eUROpeAN MULTI- CeNTRe STUDy

OBJECTIVE To determine 5-year mortality and its association with baseline characteristics and functional status 6 months post-stroke for patients who received inpatient rehabilitation. DESIGN A prospective rehabilitation-based cohort study. SUBJECTS A total of 532 consecutive stroke patients from 4 European rehabilitation centres. METHODS Predictors were recorded on admission. Barthel Index was assessed at 6 months (BI6mths) and patients were followed for 5 years post-stroke. Survival probability was computed using Kaplan-Meier analysis and compared across 3 BI6mths-classes (0-60, 65-90, 95-100) (log-rank test). Significant independent predictors were determined using multivariate Cox regression analysis (hazard ratio (HR)). RESULTS Five-year cumulative risk of death was 29.12% (95% confidence interval (CI): 22.86-35.38). Age (HR=1.06, 95% CI: 1.04-1.09), cognitive impairment (HR=1.77, 95% CI: 1.21-2.57), diabetes mellitus (HR=1.68, 95% CI: 1.16-2.41) and atrial fibrillation (HR=1.52, 95% CI: 1.08-2.14) were independent predictors of increased mortality. Hyperlipidaemia (HR=0.66, 95% CI: 0.46-0.94), and higher BI6mths (HR=0.98, 95% CI: 0.97-0.99) were independent predictors of decreased mortality. Five-year survival probability was 0.85 (95% CI: 0.80-0.89) for patients in BI6mths-class: 95-100, 0.72 (95% CI: 0.63-0.79) in BI6mths-class: 65-90 and 0.50 (95% CI: 0.40-0.60) in BI6mths-class: 0-60 (p<0.0001). CONCLUSION Nearly one-third of rehabilitation patients died during the first 5 years following stroke. Functional status at 6 months was a powerful predictor of long-term mortality. Maximum functional independence at 6 months post-stroke should be promoted through medical interventions and rehabilitation. Future studies are recommended to evaluate the direct effect of rehabilitation on long-term survival.