Walking Performance: Correlation Between Energy Cost of Walking and Walking Participation. New Statistical Approach Concerning Outcome Measurement.

Walking Performance: Correlation between Energy Cost of Walking and Walking Participation. New Statistical Approach Concerning Outcome Measurement.

PLoS One. 2013; 8(2): e56669
Franceschini M, Rampello A, Agosti M, Massucci M, Bovolenta F, Sale P

Walking ability, though important for quality of life and participation in social and economic activities, can be adversely affected by neurological disorders, such as Spinal Cord Injury, Stroke, Multiple Sclerosis or Traumatic Brain Injury. The aim of this study is to evaluate if the energy cost of walking (CW), in a mixed group of chronic patients with neurological diseases almost 6 months after discharge from rehabilitation wards, can predict the walking performance and any walking restriction on community activities, as indicated by Walking Handicap Scale categories (WHS). One hundred and seven subjects were included in the study, 31 suffering from Stroke, 26 from Spinal Cord Injury and 50 from Multiple Sclerosis. The multivariable binary logistical regression analysis has produced a statistical model with good characteristics of fit and good predictability. This model generated a cut-off value of.40, which enabled us to classify correctly the cases with a percentage of 85.0%. Our research reveal that, in our subjects, CW is the only predictor of the walking performance of in the community, to be compared with the score of WHS. We have been also identifying a cut-off value of CW cost, which makes a distinction between those who can walk in the community and those who cannot do it. In particular, these values could be used to predict the ability to walk in the community when discharged from the rehabilitation units, and to adjust the rehabilitative treatment to improve the performance. HubMed – rehab


A double-blind, randomised, crossover trial of two botulinum toxin type a in patients with spasticity.

PLoS One. 2013; 8(2): e56479
Guarany FC, Picon PD, Guarany NR, Dos Santos AC, Chiella BP, Barone CR, Fendt LC, Schestatsky P

Botulinum toxin type A (btxA) is one of the main treatment choices for patients with spasticity. Prosigne® a new released botulinum toxin serotype A may have the same effectiveness as Botox® in focal dystonia. However, there are no randomized clinical trials comparing these formulations in spasticity treatment. The aim of our study was to compare the efficacy and safety of Prosigne® with Botox® in the treatment of spasticity. METHODOLOGYPRINCIPAL FINDINGS: We performed a double-blind, randomized, crossover study consisting of 57 patients with clinically meaningful spasticity. The patients were assessed at baseline, 4 and 12 weeks after Prosigne® or Botox® administration. The main outcomes were changes in the patients’ Modified Ashworth Scale (MAS), Functional Independence Measure (FIM) and Pediatric Evaluation of Disability Inventory (PEDI) scores and adverse effects related to the botulinum toxin. Both of the toxins were significantly effective in relieving the level of spasticity in adults and children. There were no significant differences found between the Prosigne® and Botox® treatments regarding their MAS, FIM and PEDI scores. Likewise, the incidence of adverse effects was similar between the two groups.Our results suggest that Prosigne® and Botox® are both efficient and comparable with respect to their efficacy and safety for the three month treatment of spasticity.ClinicalTrials.gov NCT00819065. HubMed – rehab


Who receives rehabilitation after stroke?: data from the quality assurance project “stroke register northwest Germany”.

Dtsch Arztebl Int. 2013 Feb; 110(7): 101-7
Unrath M, Kalic M, Berger K

Neurological rehabilitation after stroke lowers rates of death, dependency, and institutionalization. Little research has yet addressed the factors affecting the selection of ischemic stroke patients for rehabilitative treatment.The database for this study consisted of all cases of ischemic stroke (ICD-10 code I63) that occurred in 2010 and 2011 in the neurological inpatient care facilities participating in the “Stroke Register Northwest Germany” quality assurance project. A primary target group for rehabilitation was defined a priori (Barthel Index at discharge ? 65, no premorbid nursing dependency, no transfer to another acute-care hospital after initial treatment of stroke). Among these patients, factors potentially affecting the provision of rehabilitative treatment were studied with binary logistic regression and multilevel logistic regression.There were 96 955 cases of ischemic stroke in the 127 participating hospitals. 40.8% and 11.4% of these patients underwent neurological and geriatric rehabilitation, respectively. The primary target group for rehabilitation contained 14 486 patients, 14.9% of whom underwent no rehabilitation after their acute treatment. The chances of undergoing subsequent rehabilitation were higher for patients with paresis and dysarthria on admission. Female sex, older age, impaired consciousness at admission, prior history of stroke, and lack of counseling by the hospital social services were all associated with a lower probability of undergoing rehabilitation.In this study, 54.4% of all ischemic stroke patients and 85.1% of all patients in a primary target group for rehabilitation that was defined a priori underwent rehabilitation after acute care for stroke. Older patients and those who had had a previous stroke were less likely to undergo rehabilitation. Counseling by hospital social services increased the probability of rehabilitation. The potential exclusion of stroke patients from rehabilitation because of old age should be critically examined in every relevant case. HubMed – rehab


Development of a Computer-Based Clinical Decision Support Tool for Selecting Appropriate Rehabilitation Interventions for Injured Workers.

J Occup Rehabil. 2013 Mar 7;
Gross DP, Zhang J, Steenstra I, Barnsley S, Haws C, Amell T, McIntosh G, Cooper J, Zaiane O

Purpose To develop a classification algorithm and accompanying computer-based clinical decision support tool to help categorize injured workers toward optimal rehabilitation interventions based on unique worker characteristics. Methods Population-based historical cohort design. Data were extracted from a Canadian provincial workers’ compensation database on all claimants undergoing work assessment between December 2009 and January 2011. Data were available on: (1) numerous personal, clinical, occupational, and social variables; (2) type of rehabilitation undertaken; and (3) outcomes following rehabilitation (receiving time loss benefits or undergoing repeat programs). Machine learning, concerned with the design of algorithms to discriminate between classes based on empirical data, was the foundation of our approach to build a classification system with multiple independent and dependent variables. Results The population included 8,611 unique claimants. Subjects were predominantly employed (85 %) males (64 %) with diagnoses of sprain/strain (44 %). Baseline clinician classification accuracy was high (ROC = 0.86) for selecting programs that lead to successful return-to-work. Classification performance for machine learning techniques outperformed the clinician baseline classification (ROC = 0.94). The final classifiers were multifactorial and included the variables: injury duration, occupation, job attachment status, work status, modified work availability, pain intensity rating, self-rated occupational disability, and 9 items from the SF-36 Health Survey. Conclusions The use of machine learning classification techniques appears to have resulted in classification performance better than clinician decision-making. The final algorithm has been integrated into a computer-based clinical decision support tool that requires additional validation in a clinical sample. HubMed – rehab