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A cohort study investigating a simple, early assessment to predict upper extremity function after stroke - a part of the SALGOT study.

ABSTRACT: BACKGROUND:For early prediction of upper extremity function, there is a need for short clinical measurements suitable for acute settings. Previous studies demonstrate correct prediction of function, but have ether included a complex assessment procedure or have an outcome that does not automatically correspond to motor function required to be useful in daily activity. The purpose of this study was to investigate whether a sub-set of items from the Action Research Arm Test (ARAT) at 3 days and 1 month post-stroke could predict the level of upper extremity motor function required for a drinking task at three later stages during the first year post-stroke. METHODS:The level of motor function required for a drinking task was identified with the Fugl-Meyer Assessment for Upper Extremity (FMA-UE). A structured process was used to select ARAT items not requiring special equipment and to find a cut-off level of the items' sum score. The early prognostic values of the selected items, aimed to determine the level of motor function required for a drinking task at 10 days and 1 and 12 months, were investigated in a cohort of 112 patients. The patients had a first time stroke and impaired upper extremity function at day 3 after stroke onset, were ?18 years and received care in a stroke unit. RESULTS:Two items, "Pour water from glass to glass" and "Place hand on top of head", called ARAT-2, met the requirements to predict upper extremity motor function. ARAT-2 is a sum score (0-6) with a cut-off at 2 points, where >2 is considered an improvement. At the different time points, the sensitivity varied between 98% and 100%, specificity between 73% and 94%. Correctly classified patients varied between 81% and 96%. CONCLUSIONS:Using ARAT-2, 3 days post-stroke could predict the level of motor function (assessed with FMA-UE) required for a drinking task during the first year after a stroke. ARAT-2 demonstrates high predictive values, is easily performed and has the potential to be clinically feasible. TRAIL REGISTRATION:ClinicalTrials.gov: NCT01115348.

SUBMITTER: Persson HC

PROVIDER: S-EPMC4471915 | biostudies-literature | 2015 Jun

REPOSITORIES: biostudies-literature

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A cohort study investigating a simple, early assessment to predict upper extremity function after stroke - a part of the SALGOT study.

Persson Hanna C HC Alt Murphy Margit M Danielsson Anna A Lundgren-Nilsson Åsa Å Sunnerhagen Katharina S KS

BMC neurology 20150618

<h4>Background</h4>For early prediction of upper extremity function, there is a need for short clinical measurements suitable for acute settings. Previous studies demonstrate correct prediction of function, but have ether included a complex assessment procedure or have an outcome that does not automatically correspond to motor function required to be useful in daily activity. The purpose of this study was to investigate whether a sub-set of items from the Action Research Arm Test (ARAT) at 3 day ...[more]

PMID: 26084397

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Project description:Background and objectivePrediction of voluntary upper extremity (UE) movement recovery is largely unknown in patients with little voluntary UE movement at admission. The present study aimed to investigate (1) the extent and variation of voluntary UE movement recovery, and (2) the best predictive model of the recovery of voluntary UE movement by clinical variables in patients with severe UE paresis.DesignProspective cohort study.Methods140 (out of 590) stroke patients with severe UE paresis completed all assessments. Voluntary UE movement was assessed using the UE subscale of the Stroke Rehabilitation Assessment of Movement (STREAM-UE). Two outcome measures, STREAM-UE scores at discharge (DC(STREAM-UE)) and changes between admission and discharge (Δ(STREAM-UE)), were investigated to represent the final states and improvement of the recovery of voluntary UE movement. Stepwise regression analyses were used to investigate 19 clinical variables and to find the best predictive models of the two outcome measures.ResultsThe participants showed wide variation in both DC(STREAM-UE) and Δ(STREAM-UE). 3.6% of the participants almost fully recovered at discharge (DC(STREAM-UE) > 15). A large improvement (Δ(STREAM-UE) >= 10) occurred in 16.4% of the participants, while 32.9% of the participants did not have any improvement. The four predictors for the DC(STREAM-UE) (R(2) = 35.0%) were 'baseline STREAM-UE score', 'hemorrhagic stroke', 'baseline National Institutes of Health Stroke Scale (NIHSS) score', and 'cortical lesion excluding primary motor cortex'. The three predictors for the Δ(STREAM-UE) (R(2) = 22.0%) were 'hemorrhagic stroke', 'baseline NIHSS score', and 'cortical lesion excluding primary motor cortex'.ConclusionsRecovery of voluntary UE movement varied widely in patients with severe UE paresis after stroke. The predictive power of clinical variables was poor. Both results indicate the complex nature of voluntary UE movement recovery in patients with severe UE paresis after stroke.

Project description:BackgroundUpper extremity (UE) impairment affects up to 80% of stroke survivors and accounts for most of the rehabilitation after discharge from the hospital release. Compensation, commonly used by stroke survivors during UE rehabilitation, is applied to adapt to the loss of motor function and may impede the rehabilitation process in the long term and lead to new orthopedic problems. Intensive monitoring of compensatory movements is critical for improving the functional outcomes during rehabilitation.ObjectiveThis review analyzes how technology-based methods have been applied to assess and detect compensation during stroke UE rehabilitation.MethodsWe conducted a wide database search. All studies were independently screened by 2 reviewers (XW and YF), with a third reviewer (BY) involved in resolving discrepancies. The final included studies were rated according to their level of clinical evidence based on their correlation with clinical scales (with the same tasks or the same evaluation criteria). One reviewer (XW) extracted data on publication, demographic information, compensation types, sensors used for compensation assessment, compensation measurements, and statistical or artificial intelligence methods. Accuracy was checked by another reviewer (YF). Four research questions were presented. For each question, the data were synthesized and tabulated, and a descriptive summary of the findings was provided. The data were synthesized and tabulated based on each research question.ResultsA total of 72 studies were included in this review. In all, 2 types of compensation were identified: disuse of the affected upper limb and awkward use of the affected upper limb to adjust for limited strength, mobility, and motor control. Various models and quantitative measurements have been proposed to characterize compensation. Body-worn technology (25/72, 35% studies) was the most used sensor technology to assess compensation, followed by marker-based motion capture system (24/72, 33% studies) and marker-free vision sensor technology (16/72, 22% studies). Most studies (56/72, 78% studies) used statistical methods for compensation assessment, whereas heterogeneous machine learning algorithms (15/72, 21% studies) were also applied for automatic detection of compensatory movements and postures.ConclusionsThis systematic review provides insights for future research on technology-based compensation assessment and detection in stroke UE rehabilitation. Technology-based compensation assessment and detection have the capacity to augment rehabilitation independent of the constant care of therapists. The drawbacks of each sensor in compensation assessment and detection are discussed, and future research could focus on methods to overcome these disadvantages. It is advised that open data together with multilabel classification algorithms or deep learning algorithms could benefit from automatic real time compensation detection. It is also recommended that technology-based compensation predictions be explored.

Dataset Information

A cohort study investigating a simple, early assessment to predict upper extremity function after stroke - a part of the SALGOT study.

Publications

A cohort study investigating a simple, early assessment to predict upper extremity function after stroke - a part of the SALGOT study.

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