Prediction of Walking and Arm Recovery after Stroke: A Critical Review

Neuroimaging biomarkers for predicting stroke outcomes: A systematic review

Background and Aims

Stroke is a prominent cause of long-term adult impairment globally and a significant global health issue. Only 14% of stroke survivors achieve full recovery, while 25% to 50% require varying degrees of support, and over half become dependent. The aftermath of a stroke brings profound changes to an individual's life, with early choices significantly impacting their quality of life. This review aims to establish the efficacy of neuroimaging data in predicting long-term outcomes and recovery rates following a stroke.

Methods

A scientific literature search was conducted using the Centre of Reviews and Dissemination (CRD) criteria and PRISMA guidelines for a combined meta-narrative and systematic quantitative review. The methodology involved a structured search in databases like PubMed and The Cochrane Library, following inclusion and exclusion criteria to identify relevant studies on neuroimaging biomarkers for stroke outcome prediction. Data collection utilized the Microsoft Edge Zotero plugin, with quality appraisal conducted via the CASP checklist. Studies published from 2010 to 2024, including observational, randomized control trials, case reports, and clinical trials. Non-English and incomplete studies were excluded, resulting in the identification of 11 pertinent articles. Data extraction emphasized study methodologies, stroke conditions, clinical parameters, and biomarkers, aiming to provide a thorough literature overview and evaluate the significance of neuroimaging biomarkers in predicting stroke recovery outcomes.

Results

The results of this systematic review indicate that integrating advanced neuroimaging methods with highly successful reperfusion therapies following a stroke facilitates the diagnosis of the condition and assists in improving neurological impairments resulting from stroke. These measures reduce the possibility of death and improve the treatment provided to stroke patients.

Conclusion

These findings highlight the crucial role of neuroimaging in advancing our understanding of post-stroke outcomes and improving patient care.

R3-Walk and R6-Walk, Simple Clinical Equations to Accurately Predict Independent Walking at 3 and 6 Months After Stroke: A Prospective, Cohort Study

OBJECTIVE

To investigate if independent walking at 3 and 6 months poststroke can be accurately predicted within the first 72 hours, based on simple clinical bedside tests.

DESIGN

Prospective observational cohort study with 3-time measurements: immediately after stroke, and 3 and 6 months poststroke.

SETTING

Public hospital.

PARTICIPANTS

Adults with first-ever stroke evaluated at 3 (N=263) and 6 (N=212) months poststroke.

INTERVENTION

Not applicable.

MAIN OUTCOME MEASURES

The outcome of interest was independent walking at 3 and 6 months after stroke. Predictors were age, walking ability, lower limb strength, motor recovery, spatial neglect, continence, and independence in activities of daily living.

RESULTS

The equation for predicting walking 3 months poststroke was 3.040 + (0.283 × FAC baseline) + (0.021 × Modified Barthel Index), and for predicting walking 6 months poststroke was 3.644 + (-0.014 × age) + (0.014 × Modified Barthel Index). For walking ability 3 months after stroke, sensitivity was classified as high (91%; 95% CI: 81-96), specificity was moderate (57%; 95% CI: 45-69), positive predictive value was high (76%; 95% CI: 64-86), and negative predictive value was high (80%; 95% CI: 60-93). For walking ability 6 months after stroke, sensitivity was classified as moderate (54%; 95% CI: 47-61), specificity was high (81%; 95% CI: 61-92), positive predictive value was high (87%; 95% CI: 70-96), and negative predictive value was low (42%; 95% CI: 50-73).

CONCLUSIONS

This study provided 2 simple equations that predict walking ability 3 and 6 months after stroke. This represents an important step to accurately identify individuals, who are at high risk of walking dependence early after stroke.

Factors influencing physiotherapy decisions between restorative and compensatory gait rehabilitation: an Italian multicenter study

Background and purpose

This study aimed to investigate the factors that influence physiotherapists' decision in choosing restorative or compensatory rehabilitation during gait training in people with neurological disorders (PwNDs) and the different treatments used in the approaches.

Methods

This cross-sectional analysis used the baseline data from an observational cohort study. We analyzed data from 83 PwNDs (65 people after stroke, 5 with multiple sclerosis, and 13 with Parkinson's disease) who underwent at least 10 sessions of physiotherapy (PT) focusing on gait function. Performance was quantified using the modified Dynamic Gait Index (MDGI), three impairment domains of Fugl-Meyer Assessment for lower extremity (mFM-LL), Activities-specific Balance Confidence (ABC), modified Barthel Index (mBI), Mini-Mental State Examination (MMSE), and Motivational Index (MI). Forty-three physiotherapists completed a treatment report form categorizing the rehabilitation approach and specifying treatments used (e.g., resistance training and proprioceptive exercises).

Results

Fifty-six subjects underwent restorative rehabilitation approach. The univariate predictors of restorative approach were being in the subacute phase with a disease onset of less than 180 days, (odds ratio [95%CI]; 3.27[1.19-9.24]), mFM-LL (1.25[1.11-1.44]), MMSE (0.85[0.67-1.00]), and number of sessions (1.03[1-1.01]). The backward stepwise analysis revealed an association between restorative and subacute phase (36.32[4.11-545.50]), mFM-LL (3.11[1.55-9.73]), mBI (1.79[1.08-3.77]), MMSE (0.46[0.25-0.71]), and the interaction between mFM-LL and mBI (0.99[0.98-1.00]). No statistically significant association between treatments used and approach was found (p = 0.46).

Discussion and conclusion

The restorative approach was more commonly used to improve gait. The main variables associated with this approach were: being in the subacute phase of the disease, a low level of impairment, and a high level of functional independence at baseline. However, few differences were found between the treatments used for the restorative or compensatory approaches, as similar PT treatments were used for both.

Early Prediction of Poststroke Rehabilitation Outcomes Using Wearable Sensors

OBJECTIVE

Inpatient rehabilitation represents a critical setting for stroke treatment, providing intensive, targeted therapy and task-specific practice to minimize a patient's functional deficits and facilitate their reintegration into the community. However, impairment and recovery vary greatly after stroke, making it difficult to predict a patient's future outcomes or response to treatment. In this study, the authors examined the value of early-stage wearable sensor data to predict 3 functional outcomes (ambulation, independence, and risk of falling) at rehabilitation discharge.

METHODS

Fifty-five individuals undergoing inpatient stroke rehabilitation participated in this study. Supervised machine learning classifiers were retrospectively trained to predict discharge outcomes using data collected at hospital admission, including patient information, functional assessment scores, and inertial sensor data from the lower limbs during gait and/or balance tasks. Model performance was compared across different data combinations and was benchmarked against a traditional model trained without sensor data.

RESULTS

For patients who were ambulatory at admission, sensor data improved the predictions of ambulation and risk of falling (with weighted F1 scores increasing by 19.6% and 23.4%, respectively) and maintained similar performance for predictions of independence, compared to a benchmark model without sensor data. The best-performing sensor-based models predicted discharge ambulation (community vs household), independence (high vs low), and risk of falling (normal vs high) with accuracies of 84.4%, 68.8%, and 65.9%, respectively. Most misclassifications occurred with admission or discharge scores near the classification boundary. For patients who were nonambulatory at admission, sensor data recorded during simple balance tasks did not offer predictive value over the benchmark models.

CONCLUSION

These findings support the continued investigation of wearable sensors as an accessible, easy-to-use tool to predict the functional recovery after stroke.

IMPACT

Accurate, early prediction of poststroke rehabilitation outcomes from wearable sensors would improve our ability to deliver personalized, effective care and discharge planning in the inpatient setting and beyond.

Predicting self-perceived manual ability at three and six months after stroke: A prospective longitudinal study

BACKGROUND

Recovery of manual ability is a critical issue in rehabilitation. Currently, little is known regarding the baseline predictors of self-perceived manual ability, which could capture information on individual's perceived functional ability, especially in carrying-out routine tasks outside clinical settings.

OBJECTIVE

To identify baseline predictors, which can be easily obtained within clinical settings, of self-perceived manual ability at three and six months after discharge from a stroke unit.

METHODS

A 6-month longitudinal study was carried-out. Participants were recruited from a stroke unit of a public hospital. The dependent outcome was self-perceived manual ability, and the following predictors were investigated: age, stroke severity, upper-limb motor impairments, cognitive function, muscle strength, and functional capacity. Linear regression analyses were employed to identify multivariate predictors of manual ability at three and six months after discharge (α=5%).

RESULTS

Participated 131 individuals, 69 women (mean age of 60 years). Regression analyses revealed that stroke severity and age accounted for 31% and 47% of the variance in manual ability at three and six months after stroke, respectively. Stroke severity was the best predictor of manual ability at three (R2=29%; F=44.7; p<0.0001) and six months (R2=45%; F=88.2; p<0.0001) after stroke, respectively.

CONCLUSION

Stroke severity showed to be the best predictor of manual ability at both three and six months after stroke. Although significant, age added little to the explained variance.

Trajectories of arm recovery early after stroke: an exploratory study using latent class growth analysis

AIM

To investigate trajectories of recovery of motor arm function after stroke during inpatient rehabilitation.

MATERIALS AND METHODS

Data were available from 74 consecutively-admitted stroke survivors receiving inpatient rehabilitation from an inception cohort study. Heterogeneity of arm recovery in the first 4-weeks was investigated using latent class analysis and weekly Box and Block Test (BBT) scores. Optimal number of clusters were determined; characterised and cluster associated factors explored.

RESULTS

A 4-cluster model was identified, including 19 participants with low baseline arm function and minimal recovery ('LOWstart/LOWprogress', 26%), 15 with moderate function and low recovery ('MODstart/LOWprogress', 20%), 15 with low function and high recovery ('LOWstart/HIGHprogress', 20%), and 25 with moderate function and recovery ('MODstart/MODprogress', 34%). Compared to LOWstart/LOWprogress: LOWstart/HIGHprogress presented earlier post-stroke (β, 95%CI) (-4.81 days, -8.94 to -0.69); MODstart/MODprogress had lower modified Rankin Scale scores (-0.74, -1.15 to -0.32); and MODstart/LOWprogress, LOWstart/HIGHprogress and MODstart/MODprogress had higher admission BBT (23.58, 18.82 to 28.34; 4.85, 0.85 to 9.61; 28.02, 23.82 to 32.21), Upper Limb-Motor Assessment Scale (9.60, 7.24 to 11.97; 3.34, 0.97 to 5.70; 10.86, 8.77 to 12.94), Action Research Arm Test (31.09, 22.86 to 39.33; 12.69, 4.46 to 20.93; 38.01, 30.76 to 45.27), and Manual Muscle Test scores (10.64, 7.07 to 14.21; 6.24, 2.67 to 9.81; 11.87, 8.72 to 15.01).

CONCLUSIONS

We found unique patterns of arm recovery with distinct characteristics for each cluster. Better understanding of patterns of arm recovery can guide future models and intervention development.KEY MESSAGESArm recovery early after stroke follows four distinct trajectories that relate to time post stroke, initial stroke severity and baseline level of motor arm function.Identification of recovery patterns gives insight into the uniqueness of individual's recovery.This study offers a novel approach on which to build and develop future models of arm recovery.

Predictors of manual dexterity at 3 and 6 months after stroke: integration of clinical, neurophysiological, and neuroimaging factors

This retrospective study aimed to predict dexterity at 3 and 6 months post-stroke by integrating clinical, neurophysiological, and neuroimaging factors. We included 126 patients with first-ever, unilateral, and supratentorial stroke. Demographic, stroke characteristics, and initial clinical assessment variables [Mini-mental state examination and Fugl-Meyer Assessment Upper Extremity (FMA-UE)] were evaluated 2 weeks after stroke. Dexterity, measured using the Manual Function Test (MFT) hand subscore, was the primary outcome. The neurophysiological variables, upper limb somatosensory evoked potential (SEP) and motor evoked potential (MEP), were assessed 2 weeks post-stroke. The neuroimaging variable, fractional anisotropy (FA) of the corticospinal tract (CST), was assessed 3 weeks post-stroke. Multiple regression analysis revealed significant predictors for improved dexterity at 3 and 6 months post-stroke, including younger age, higher FMA-UE score, presence of waveforms in the SEP and MEP, and higher FA values in the CST (adjusted R 2  = 0.776, P  < 0.001 at 3 months; adjusted R 2  = 0.668, P  < 0.001 at 6 months; where MEP, SEP, and FA accounted together for an additional 0.079 and 0.166 of variance beyond age and FMA-UE, respectively). Subgroup analysis was conducted by categorizing the participants based on their initial hand function: those with no hand function (MFT hand subscore = 0) (N = 60) and those with a score >0 (N = 51). Initial FMA-UE was a primary predictive factor regardless of the time point or initial severity, whereas the presence of MEP was a significant predictor only in the group with no initial hand dexterity.

Changes in the clinico-functional characteristics of stroke patients in the acute phase during the COVID-19 pandemic

OBJECTIVE

To compare the sociodemographic and clinico-functional characteristics of patients admitted to a stroke unit immediately before and during two different COVID-19 pandemic phases.

METHODS

This exploratory study was conducted in the stroke unit of a public hospital in Brazil. Patients consecutively admitted to a stroke unit for 18 months with primary stroke aged ≥20 years were included and divided into three groups: G1: Pre-pandemic; G2: Early pandemic; and G3: Late pandemic. The sociodemographic and clinico-functional characteristics of the groups were compared (α=0.05).

RESULTS

The study included 383 individuals (G1=124; G2=151; G3=108). The number of risk factors (higher in G2; p≤0.001), smoking (more common in G2; p≤0.01), type of stroke (ischemic more common in G3; p=0.002), stroke severity (more severe in G2; p=0.02), and level of disability (more severe in G2: p≤0.01) were significantly different among the groups.

CONCLUSION

A greater number of serious events and risk factors including smoking and higher level of disability was observed in patients in the beginning of the pandemic than in the late phases. Only the occurrence of ischemic stroke increased in the late phase. Therefore, these individuals may have an increased need for rehabilitation services monitoring and care during their lifespan. Additionally, these results indicate that health promotion and prevention services should be strengthened for future health emergencies.

Predicting models for arm impairment: External validation of the Scandinavian models and identification of new predictors in post-acute stroke settings

BACKGROUND

Post-stroke arm impairment at rehabilitation admission as predictor of discharge arm impairment was consistently reported as extremely useful. Several models for acute prediction exist (e.g. the Scandinavian), though lacking external validation and larger time-window admission assessments.

OBJECTIVES

(1) use the 33 Fugl-Meyer Assessment-Upper Extremity (FMA-UE) individual items to predict total FMA-UE score at discharge of patients with ischemic stroke admitted to rehabilitation within 90 days post-injury, (2) use eight individual items (seven from the Scandinavian study plus the top predictor item from objective 1) to predict mild impairment (FMA-UE≥48) at discharge and (3) adjust the top three models from objective 2 with known confounders.

METHODS

This was an observational study including 287 patients (from eight settings) admitted to rehabilitation (2009-2020). We applied regression models to candidate predictors, reporting adjusted R2, odds ratios and ROC-AUC using 10-fold cross-validation.

RESULTS

We achieved good predictive power for the eight item-level models (AUC: 0.70-0.82) and for the three adjusted models (AUC: 0.85-0.88). We identified finger mass flexion as new item-level top predictor (AUC:0.88) and time to admission (OR = 0.9(0.9;1.0)) as only common significant confounder.

CONCLUSION

Scandinavian item-level predictors are valid in a different context, finger mass flexion outperformed known predictors, days-to-admission predict discharge mild arm impairment.

Prognosis of Individual-Level Mobility and Daily Activities Recovery From Acute Care to Community, Part 2: A Proof-of-Concept Single Group Prospective Cohort Study

OBJECTIVE

To demonstrate a proof-of-concept for prognostic models of post-stroke recovery on activity level outcomes.

DESIGN

Longitudinal cohort with repeated measures from acute care, inpatient rehabilitation, and post-discharge follow-up to 6 months post-stroke.

SETTING

Enrollment from a single Midwest USA inpatient rehabilitation facility with community follow-up.

PARTICIPANTS

One-hundred fifteen persons recovering from stroke admitted to an acute rehabilitation facility (N=115).

INTERVENTIONS

Not applicable.

MAIN OUTCOME MEASURE(S)

Activity Measure for Post-Acute Care Basic Mobility and Daily Activities domains administered as 6 Clicks and patient-reported short forms.

RESULTS

The final Basic Mobility model defined a group-averaged trajectory rising from a baseline (pseudo-intercept) T score of 35.5 (P<.001) to a plateau (asymptote) T score of 56.4 points (P<.001) at a negative exponential rate of -1.49 (P<.001). Individual baseline scores varied by age, acute care tissue plasminogen activator, and acute care length of stay. Individual plateau scores varied by walking speed, acute care tissue plasminogen activator, and lower extremity Motricity Index scores. The final Daily Activities model defined a group-averaged trajectory rising from a baseline T score of 24.5 (P<.001) to a plateau T score of 41.3 points (P<.001) at a negative exponential rate of -1.75 (P<.001). Individual baseline scores varied by acute care length of stay, and plateau scores varied by self-care, upper extremity Motricity Index, and Berg Balance Scale scores.

CONCLUSIONS

As a proof-of-concept, individual activity-level recovery can be predicted as patient-level trajectories generated from electronic medical record data, but models require attention to completeness and accuracy of data elements collected on a fully representative patient sample.

Prognostic Value of Combined Radiomic Features from Follow-Up DWI and T2-FLAIR in Acute Ischemic Stroke

The biological pathways involved in lesion formation after an acute ischemic stroke (AIS) are poorly understood. Despite successful reperfusion treatment, up to two thirds of patients with large vessel occlusion remain functionally dependent. Imaging characteristics extracted from DWI and T2-FLAIR follow-up MR sequences could aid in providing a better understanding of the lesion constituents. We built a fully automated pipeline based on a tree ensemble machine learning model to predict poor long-term functional outcome in patients from the MR CLEAN-NO IV trial. Several feature sets were compared, considering only imaging, only clinical, or both types of features. Nested cross-validation with grid search and a feature selection procedure based on SHapley Additive exPlanations (SHAP) was used to train and validate the models. Considering features from both imaging modalities in combination with clinical characteristics led to the best prognostic model (AUC = 0.85, 95%CI [0.81, 0.89]). Moreover, SHAP values showed that imaging features from both sequences have a relevant impact on the final classification, with texture heterogeneity being the most predictive imaging biomarker. This study suggests the prognostic value of both DWI and T2-FLAIR follow-up sequences for AIS patients. If combined with clinical characteristics, they could lead to better understanding of lesion pathophysiology and improved long-term functional outcome prediction.

External validation and extension of the Early Prediction of Functional Outcome after Stroke (EPOS) prediction model for upper limb outcome 3 months after stroke

Objective

The ‘Early Prediction of Functional Outcome after Stroke’ (EPOS) model was developed to predict the presence of at least some upper limb capacity (Action Research Am Test [ARAT] ≥10/57) at 6 months based on assessments on days 2, 5 and 9 after stroke. External validation of the model is the next step towards clinical implementation. The objective here is to externally validate the EPOS model for upper limb outcome 3 months poststroke in Switzerland and extend the model using an ARAT cut-off at 32 points.

Methods

Data from two prospective longitudinal cohort studies including first-ever stroke patients admitted to a Swiss stroke center were analyzed. The presence of finger extension and shoulder abduction was measured on days 1 and 8 poststroke in Cohort 1, and on days 3 and 9 in Cohort 2. Upper limb capacity was measured 3 months poststroke. Discrimination (area under the curve; AUC) and calibration obtained with the model were determined.

Results

In Cohort 1 (N = 39, median age 74 years), the AUC on day 1 was 0.78 (95%CI 0.61, 0.95) and 0.96 (95%CI 0.90, 1.00) on day 8, using the model of day 5. In Cohort 2 (N = 85, median age 69 years), the AUC was 0.96 (95%CI 0.93, 0.99) on day 3 and 0.89 (95% CI 0.80, 0.98) on day 9. Applying a 32-point ARAT cut-off resulted in an AUC ranging from 0.82 (95%CI 0.68, 0.95; Cohort 1, day 1) to 0.95 (95%CI 0.87, 1.00; Cohort 1, day 8).

Conclusions

The EPOS model was successfully validated in first-ever stroke patients with mild-to-moderate neurological impairments, who were independent before their stroke. Now, its impact on clinical practice should be investigated in this population. Testing the model’s performance in severe (recurrent) strokes and stratification of patients using the ARAT 32-point cut-off is required to enhance the model’s generalizability and potential clinical impact.

Beyond Diffusion Tensor MRI Methods for Improved Characterization of the Brain after Ischemic Stroke: A Review

Ischemic stroke is a worldwide problem, with 15 million people experiencing a stroke annually. MR imaging is a valuable tool for understanding and assessing brain changes after stroke and predicting recovery. Of particular interest is the use of diffusion MR imaging in the nonacute stage 1-30 days poststroke. Thousands of articles have been published on the use of diffusion MR imaging in stroke, including several recent articles reviewing the use of DTI for stroke. The goal of this work was to survey and put into context the recent use of diffusion MR imaging methods beyond DTI, including diffusional kurtosis, generalized fractional anisotropy, spherical harmonics methods, and neurite orientation and dispersion models, in patients poststroke. Early studies report that these types of beyond-DTI methods outperform DTI metrics either in being more sensitive to poststroke changes or by better predicting outcome motor scores. More and larger studies are needed to confirm the improved prediction of stroke recovery with the beyond-DTI methods.

Quantitative electrophysiological assessments as predictive markers of lower limb motor recovery after spinal cord injury: a pilot study with an adaptive trial design

Study design

Observational, cohort study.

Objectives

(1) Determine the feasibility and relevance of assessing corticospinal, sensory, and spinal pathways early after traumatic spinal cord injury (SCI) in a rehabilitation setting. (2) Validate whether electrophysiological and magnetic resonance imaging (MRI) measures taken early after SCI could identify preserved neural pathways, which could then guide therapy.

Setting

Intensive functional rehabilitation hospital (IFR).

Methods

Five individuals with traumatic SCI and eight controls were recruited. The lower extremity motor score (LEMS), electrical perceptual threshold (EPT) at the S2 dermatome, soleus (SOL) H-reflex, and motor evoked potentials (MEPs) in the tibialis anterior (TA) muscle were assessed during the stay in IFR and in the chronic stage (>6 months post-SCI). Control participants were only assessed once. Feasibility criteria included the absence of adverse events, adequate experimental session duration, and complete dataset gathering. The relationship between electrophysiological data collected in IFR and LEMS in the chronic phase was studied. The admission MRI was used to calculate the maximal spinal cord compression (MSCC).

Results

No adverse events occurred, but a complete dataset could not be collected for all subjects due to set-up configuration limitations and time constraints. EPT measured at IFR correlated with LEMS in the chronic phases (r = −0.67), whereas SOL H/M ratio, H latency, MEPs and MSCC did not.

Conclusions

Adjustments are necessary to implement electrophysiological assessments in an IFR setting. Combining MRI and electrophysiological measures may lead to better assessment of neuronal deficits early after SCI.

Prediction of Upper Limb use Three Months after Stroke: A Prospective Longitudinal Study

BACKGROUND

A major goal of upper limb (UL) rehabilitation after stroke is to facilitate the use of the paretic arm in daily life activities.

PURPOSE

To examine if UL impairment two weeks after stroke can predict real-life UL use at three months. Furthermore, to identify additional factors which contribute to future UL use, and characteristics of patients who do not achieve normal UL use.

METHODS

This study included patients with stroke ≥ 18 years. UL impairment was assessed by Fugl-Meyer upper extremity motor assessment (FM). Use ratio between affected and unaffected UL was assessed with accelerometers at three months after stroke. The association between FM score and UL use ratio was investigated with linear regression models and adjusted for secondary variables. Non-normal use was examined by a logistic regression.

RESULTS

Eighty-seven patients were included. FM score two weeks after stroke predicted 38% of the variance in UL use ratio three months after stroke. A multivariate regression model predicted 55%, and the significant predictors were FM, motor-evoked potential (MEP) status, and neglect. Non-normal use could be predicted with a high accuracy based on MEP and/or neglect. In a logistic regression sensitivity for prediction of non-normal use was 0.93 and specificity was 0.75.

CONCLUSION

Better baseline capacity of the paretic UL predicted increased use of the arm and hand in daily life. Non-normal UL use could be predicted reliably based on the absence of MEPs and/or presence of neglect.

Accuracy of an Algorithm in Predicting Upper Limb Functional Capacity in a United States Population

OBJECTIVE

To determine the accuracy of an algorithm, using clinical measures only, on a sample of persons with first-ever stroke in the United States (US). It was hypothesized that algorithm accuracy would fall in a range of 70%-80%.

DESIGN

Secondary analysis of prospective, observational, longitudinal cohort; 2 assessments were done: (1) within 48 hours to 1 week poststroke and (2) at 12 weeks poststroke.

SETTING

Recruited from a large acute care hospital and followed over the first 6 months after stroke.

PARTICIPANTS

Adults with first-ever stroke (N=49) with paresis of the upper limb (UL) at ≤48 hours who could follow 2-step commands and were expected to return to independent living at 6 months.

INTERVENTION

Not applicable.

MAIN OUTCOME MEASURES

The overall accuracy of the algorithm with clinical measures was quantified by comparing predicted (expected) and actual (observed) categories using a correct classification rate.

RESULTS

The overall accuracy (61%) and weighted κ (62%) were significant. Sensitivity was high for the Excellent (95%) and Poor (81%) algorithm categories. Specificity was high for the Good (82%), Limited (98%), and Poor (95%) categories. Positive predictive value (PPV) was high for Poor (82%) and negative predictive value (NPV) was high for all categories. No differences in participant characteristics were found between those with accurate or inaccurate predictions.

CONCLUSIONS

The results of the present study found that use of an algorithm with clinical measures only is better than chance alone (chance=25% for each of the 4 categories) at predicting a category of UL capacity at 3 months post troke. The moderate to high values of sensitivity, specificity, PPV, and NPV demonstrates some clinical utility of the algorithm within health care settings in the US.

Why Are Stroke Rehabilitation Trial Recruitment Rates in Single Digits?

Background: Recruitment of patients in early subacute rehabilitation trials (<30 days post-stroke) presents unique challenges compared to conventional stroke trials recruiting individuals >6 months post-stroke. Preclinical studies suggest treatments be initiated sooner after stroke, thus requiring stroke rehabilitation trials be conducted within days post-stroke. How do specific inclusion and exclusion criteria affect trial recruitment rates for early stroke rehabilitation trials?

Objectives: Provide estimates of trial recruitment based on screening and enrollment data from a phase II early stroke rehabilitation trial.

Methods: CPASS, a phase II intervention trial screened ischemic stroke patients in acute care (18-months, N = 395) and inpatient rehabilitation (22-months, N = 673). Patients were stratified by upper extremity (UE) impairment into mild (NIHSS motor arm = 0, 1); moderate (NIHSS = 2, 3); severe (NIHSS = 4) and numbers of patients disqualified due to CPASS exclusion criteria determined. We also examined if a motor-specific evaluation (Action Research Arm Test, ARAT) increases the pool of eligible patients disqualified by the NIHSS motor arm item.

Results: CPASS recruitment in acute care (5.3%) and inpatient rehabilitation (5%) was comparable to prior trials. In acute care, a short stay (7–17-days), prior stroke (13.5% in moderately; 13.2% in severely impaired) disqualified the majority. In inpatient rehabilitation, the majority (40.8%) were excluded for “too mild” impairment. The next majority were disqualified for reaching inpatient rehabilitation “too late” to participate in an early stroke trial (15% in moderately; 24% in severely impaired). Mean ARAT in the “too mild” showed significant impairment and potential to benefit from participation in select UE rehabilitation trials.

Conclusions: Screening of ischemic stroke patients while they are still in acute care is crucial to successful recruitment for early stroke rehabilitation trials. A significant proportion of eligible patients are lost to “short length of stay” in acute care, and arrive to inpatient rehabilitation “too late” for an early rehabilitation trial. Additional screening of mildly impaired patients using a motor function specific scale will benefit the trial recruitment and generalizability.

Trial Registration Number:http://www.clinicaltrials.gov Identifier: NCT02235974.

Exploring physiotherapists' and occupational therapists' perceptions of the upper limb prediction algorithm PREP2 after stroke in a rehabilitation setting: a qualitative study

OBJECTIVE

To explore how physiotherapists (PTs) and occupational therapists (OTs) perceive upper limb (UL) prediction algorithms in a stroke rehabilitation setting and identify potential barriers to and facilitators of their implementation.

DESIGN

This was a qualitative study.

SETTING

The study took place at a neurorehabilitation centre.

PARTICIPANTS

Three to six PTs and OTs.

METHODS

We conducted four focus group interviews in order to explore therapists' perceptions of UL prediction algorithms, in particular the Predict Recovery Potential algorithm (PREP2). The Consolidated Framework for advancing Implementation Research was used to develop the interview guide. Data were analysed using a thematic content analysis. Meaning units were identified and subthemes formed. Information gained from all interviews was synthesised, and four main themes emerged.

RESULTS

The four main themes were current practice, perceived benefits, barriers and preconditions for implementation. The participants knew of UL prediction algorithms. However, only a few had a profound knowledge and few were using the Shoulder Abduction Finger Extension test, a core component of the PREP2 algorithm, in their current practice. PREP2 was considered a potentially helpful tool when planning treatment and setting goals. A main barrier was concern about the accuracy of the algorithm. Furthermore, participants dreaded potential dilemmas arising from having to confront the patients with their prognosis. Preconditions for implementation included tailoring the implementation to a specific unit, sufficient time for acquiring new skills and an organisation supporting implementation.

CONCLUSION

In the present study, experienced neurological therapists were sceptical towards prediction algorithms due to the lack of precision of the algorithms and concerns about ethical dilemmas. However, the PREP2 algorithm was regarded as potentially useful.

Externally validated model predicting gait independence after stroke showed fair performance and improved after updating

OBJECTIVE

To externally validate recent prognostic models that predict independent gait following stroke.

STUDY DESIGN AND SETTING

A systematic search identified recent models (<10 years) that predicted independent gait in adult stroke patients, using easily obtainable predictors. Predictors from the original models were assigned proxies when required, and model performance was evaluated in the validation cohort (n = 957). Models were updated to determine if performance could be improved.

RESULTS

Three prognostic models met our criteria, all with high Risk of Bias. Validation data was only available for the Australian model. This model used National Institute of Health Stroke Scale (NIHSS) and age to predict independent gait, using Motor Assessment Scale (MAS) walking item. For validation, Scandinavian Stroke Scale (SSS) was a proxy for NIHSS, and Functional Independence Measure (FIM) locomotion item was a proxy for MAS. The Area Under the Curve was 0.77 (0.74-0.80) and had good calibration in the validation dataset. Adjustment of the intercept and regression coefficients slightly improved discrimination. By adding paretic leg strength, the model further improved (AUC 0.82).

CONCLUSION

External validation of the Australian model with proxies showed fair discrimination and good calibration. Updating the model by adding paretic leg strength further improved model performance.

Improving motor function after chronic stroke by interactive gaming with a redesigned MR-compatible hand training device

New rehabilitation strategies enabled by technological developments are challenging the prevailing concept of there being a limited window for functional recovery after stroke. In this study, we examined the utility of a robot-assisted therapy used in combination with a serious game as a rehabilitation and motor assessment tool in patients with chronic stroke. We evaluated 928 game rounds from 386 training sessions of 8 patients who had suffered an ischemic stroke affecting middle cerebral artery territory that incurred at least 6 months prior. Motor function was assessed with clinical motor scales, including the Fugl-Meyer upper extremity (FM UE) scale, Action Research Arm Test, Modified Ashworth scale and the Box and Blocks test. Robotic device output measures (mean force, force-position correlation) and serious game score elements (collisions, rewards and total score) were calculated. A total of 2 patients exhibited a marginal improvement after a 10-week training protocol according to the FM UE scale and an additional patient exhibited a significant improvement according to Box and Blocks test. Motor scales showed strong associations of robotic device parameters and game metrics with clinical motor scale scores, with the strongest correlations observed for the mean force (0.677<Ρ<0.869), followed by the number of collisions (-0.670<Ρ<-0.585). Linear regression analysis showed that these indices were independent predictors of motor scale scores. In conclusion, a robotic device linked to a serious game can be used by patients with chronic stroke and induce at least some clinical improvements in motor performance. Robotic device output parameters and game score elements associate strongly with clinical motor scales and have the potential to be used as predictors in models of rehabilitation progress.

Predicting specific abilities after disabling stroke: Development and validation of prognostic models

Background

Predicting specific abilities (e.g. walk and talk) to provide a functional profile six months after disabling stroke could help patients/families prepare for the consequences of stroke and facilitate involvement in treatment decision-making.

Aim

To develop new statistical models to predict specific abilities six months after stroke and test their performance in an independent cohort of patients with disabling stroke.

Methods

We developed models to predict six specific abilities (to be independent, walk, talk, eat normally, live without major anxiety/depression, and to live at home) using data from seven large multicenter stroke trials with multivariable logistic regression. We included 13,117 participants recruited within three days of hospital admission. We assessed model discrimination and derived optimal cut-off values using four statistical methods. We validated the models in an independent single-center cohort of patients (n = 403) with disabling stroke. We assessed model discrimination and calibration and reported the performance of our models at the statistically derived cut-off values.

Results

All six models had good discrimination in external validation (AUC 0.78–0.84). Four models (predicting to walk, eat normally, live without major anxiety/depression, live at home) calibrated well. Models had sensitivities between 45.0 and 97.9% and specificities between 21.6 and 96.5%.

Conclusions

We have developed statistical models to predict specific abilities and demonstrated that these models perform reasonably well in an independent cohort of disabling stroke patients. To aid decision-making regarding treatments, further evaluation of our models is required.

Relationship between lower limb function and functional connectivity assessed by EEG among motor-related areas after stroke

Background: Neural connectivity in brain has been known as indicators for neural function and recovery of brain. Although previous studies reported that neural connectivity predicted the recovery of upper limb function after stroke, the relationship between neural connectivity and lower limb function has not been clear.Objectives: To clarify whether functional connectivity (FC) assessed by electroencephalographiy (EEG) with five electrodes placed on motor-related areas could be related to the functional motor recovery of the lower limbs in patients after stroke.Methods: Twenty-four patients with stroke during the recovery phase were recruited. Motor function of the lower limbs was assessed using Fugl-Meyer Assessment lower limb section (FMAL). EEG signals were recorded by five electrodes (C3, C4, FC3, FC4, and FCz) at rest and during ankle movement. Amplitude envelope correlations, as values for FC, were calculated in α (8-12 Hz), β (13-30 Hz), low-β (13-19 Hz), and high-β (20-30 Hz) frequency bands. The predictive regression equation of the FMAL score in the eighth week after stroke (8 W) was created by FCs in the fourth week (4 W).Results: The higher intra-hemispheric FC in both hemispheres in the resting state and during the ankle movement at 4 W was related to a higher lower limb function at 8 W. Additionally, the higher inter-hemispheric FC between M1 on both sides during the ankle movement was related to a higher function recovery.Conclusions: The intra- and inter-hemispheric FC among motor-related areas at 4 W after stroke might be related to the functional recovery of the lower limbs at 8 W.

Accuracy of the Upper Limb Prediction Algorithm PREP2 Applied 2 Weeks Poststroke: A Prospective Longitudinal Study

BACKGROUND

The Predict Recovery Potential algorithm (PREP2) was developed to predict upper limb (UL) function early after stroke. However, assessment in the acute phase is not always possible.

OBJECTIVE

To assess the prognostic accuracy of the PREP2 when applied in a subacute neurorehabilitation setting.

METHODS

This prospective longitudinal study included patients ≥18 years old with UL impairment following stroke. Patients were assessed in accordance with the PREP2 approach. However, 2 main components, the shoulder abduction finger extension (SAFE) score and motor-evoked potentials (MEPs) were obtained 2 weeks poststroke. UL function at 3 months was predicted in 1 of 4 categories and compared with the actual outcome at 3 months as assessed by the Action Research Arm Test. The prediction accuracy of the PREP2 was quantified using the correct classification rate (CCR).

RESULTS

Ninety-one patients were included. Overall CCR of the PREP2 was 60% (95% CI 50%-71%). Within the 4 categories, CCR ranged from the lowest value at 33% (95% CI 4%-85%) for the category Limited to the highest value at 78% (95% CI 43%-95%) for the category Poor. In the present study, the overall CCR was significantly lower (P < .001) than the 75% reported by the PREP2 developers.

CONCLUSIONS

The low overall CCR makes PREP2 obtained 2 weeks poststroke unsuited for clinical implementation. However, PREP2 may be used to predict either excellent UL function in already well-recovered patients or poor UL function in patients with persistent severe UL paresis.

Action observation training and brain-computer interface controlled functional electrical stimulation enhance upper extremity performance and cortical activation in patients with stroke: a randomized controlled trial

PURPOSE

Brain-computer interface (BCI)-functional electronic stimulation (FES) systems are increasingly being explored as potential neuro-rehabilitation tools. Here, we investigate the effect of action observation training (AOT) plus electroencephalogram (EEG)-based BCI-controlled FES system on motor recovery of upper extremity and cortical activation in patients with stroke.

METHOD

There were a total of 26 patients: an AOT plus BCI-FES group (n = 13) and a control group (n = 13). The control group performed FES treatment and the conventional physical therapy, while the AOT plus BCI-FES group performed AOT plus BCI-FES and the conventional physical therapy. Upper extremity performance was measured using the Fugl-Meyer Assessment of the Upper Extremity (FMA-UE), Wolf Motor Function Test (WMFT), Motor Activity Log (MAL) and Modified Barthel Index (MBI). Cortical activation was measured using electro-encephalographic recordings from alpha and beta power, concentration, and activation.

RESULTS

After intervention, there were significant differences between two groups in FMA-UE, WMFT, MAL and MBI and the results of EEG including alpha power, beta power, concentration and activation.

CONCLUSIONS

This study demonstrated that AOT plus BCI-FES can enhance motor function of upper extremity and cortical activation in patients with stroke. This training method may be feasible and suitable for individuals with stroke.

Knowledge and application of upper limb prediction models and attitude toward prognosis among physiotherapists and occupational therapists in the clinical stroke setting

BACKGROUND

A substantial body of research on prediction models for upper limb (UL) function after stroke has emerged during recent years. Despite considerable evidence supporting the use of prediction models, their implementation into clinical practice has not been examined.

OBJECTIVES

To investigate whether physiotherapists (PTs) and occupational therapists (OTs) who evaluate and rehabilitate stroke patients know about and apply prediction models for the recovery of UL function. Furthermore, to examine their attitudes toward prognosis for UL function in clinical practice.

METHODS

The authors developed an online survey using REDCap®, specifically aimed to investigate this study's objectives. Physiotherapists and occupational therapists from Danish hospitals with acute stroke or rehabilitation wards were invited to participate. Data were analyzed using STATA 15.1.

RESULTS

Of the 380 therapists invited, 58% responded to the survey. Among those, 35% reported that they knew of prediction models for UL function after stroke. More physiotherapists than occupational therapists were familiar with prediction models (p = .03). Of all respondents, 9% confirmed the use of prediction models for UL function in clinical practice. Most therapists (89%) stated that it was important to know how UL function will develop after stroke.

CONCLUSIONS

Results from this study indicate that prediction models for UL function after stroke are not yet a part of daily practice in Danish stroke rehabilitation. At the same time, knowledge of prognosis seems to be relevant for most therapists in their clinical work.

The addition of the MEP amplitude of finger extension muscles to clinical predictors of hand function after stroke: A prospective cohort study

BACKGROUND

Within the first 72 hours after stroke, active finger extension is a strong predictor of long-term dexterity. Transcranial magnetic stimulation may add prognostic value to clinical assessment, which is especially relevant for patients unable to follow instructions.

OBJECTIVE

The current prospective cohort study aims at determining whether amplitude of motor evoked potentials of the extensor digitorum communis (EDC) can improve clinical prediction after stroke when added to clinical tests.

METHODS

the amplitude of motor evoked potentials of the affected EDC muscle at rest was measured in 18 participants within 4 weeks after stroke, as were the ability to perform finger extension and the Fugl-Meyer Motor Assessment of the upper extremity (FMA_UE). These three determinants were related to the FMA_UE at 26 weeks after stroke (FMA_UE26), both directly, and via the proportional recovery prediction model. The relation between amplitude of the motor evoked potentials and FMA_UE26 was evaluated for EDC. For comparison, also the MEP amplitudes of biceps brachii and adductor digiti minimi muscles were recorded.

RESULTS

Patients' ability to voluntarily extend the fingers was strongly related to FMA_UE26, in our cohort there were no false negative results for this predictor. Our data revealed that the relation between amplitude of motor evoked potential of EDC and FMA_UE26 was significant, but moderate (rs = 0.58) without added clinical value. The other tested muscles did not correlate significantly to FMA_UE26.

CONCLUSIONS

Our study demonstrates no additional value of motor evoked potential amplitude of the affected EDC muscle to the clinical test of finger extension, the latter being more strongly related to FMA_UE26.

Easily Conducted Tests During the First Week Post-stroke Can Aid the Prediction of Arm Functioning at 6 Months

Background: Prognostic models can estimate the recovery of arm functioning after stroke, guide the selection of individual training strategies, and inform patient selection in clinical trials. Several models for early prediction of arm recovery have been proposed, but their implementation has been hindered by insufficient external validation, limited evidence of their impact on patient outcomes, and reliance on predictors that are not feasible in regular clinical practice.

Objectives: To determine the predictive value of new and previously reported tests that can be easily conducted in regular clinical settings for early prognosis of two levels of favorable arm recovery at 6 months post-stroke.

Methods: We performed a secondary analysis of merged data (n = 223) from two Scandinavian prospective longitudinal cohorts. The candidate predictors were seven individual tests of motor function and the sensory function measured by the Fugl-Meyer Assessment of Upper Extremity within 7 days post-stroke, and the whole motor section of this assessment. For each candidate predictor, we calculated the adjusted odds ratio (OR) of two levels of residual motor impairment in the affected arm at 6 months post-stroke: moderate-to-mild (≥32 points on the motor section of the Fugl-Meyer Assessment of Upper Extremity, FMA-UE) and mild (FMA-UE ≥ 58 points).

Results: Patients with partial shoulder abduction (OR 14.6), elbow extension (OR 15.9), and finger extension (OR 9.5) were more likely to reach FMA-UE ≥ 32. Patients with full function on all individual motor tests (OR 5.5–35.3) or partial elbow extension, pronation/supination, wrist dorsiflexion and grasping ability (OR 2.1–18.3) were more likely to achieve FMA-UE ≥ 58 compared with those with absent function. Intact sensory function (OR 2.0–2.2) and moderate motor impairment on the FMA-UE (OR 7.5) were also associated with favorable outcome.

Conclusions: Easily conducted motor tests can be useful for early prediction of arm recovery. The added value of this study is the prediction of two levels of a favorable functional outcome from simple motor tests. This knowledge can be used in the development of prognostic models feasible in regular clinical settings, inform patient selection and stratification in future trials, and guide clinicians in the selection of individualized training strategies for improving arm functioning after stroke.

Clinical Trial Registration:ClinicalTrials.gov: NCT02250365, NCT01115348.

Dosage and predictors of arm practice during inpatient stroke rehabilitation: an inception cohort study

PURPOSE

To describe the amount/type of arm practice completed by stroke survivors during inpatient rehabilitation; and establish predictors of arm practice dose achieved.

MATERIALS AND METHODS

Inception cohort study including 99 consecutively admitted stroke survivors. Amount (repetitions) and type of arm practice completed during inpatient rehabilitation and possible predictors of dose were recorded.

RESULTS

Average length-of-stay was 36.9 days (standard deviation (SD) = 30.0, median = 28.0, interquartile range = 39.5) and days of therapy provided was 11.1 days (SD = 13.3, median = 6.0, IQR = 12.0). Mean number of arm practice sessions completed overall was 12.8 (SD = 15.3, median = 7.0, interquartile range = 15.0), or 2.0 sessions per week (SD = 1.5, median = 1.5, interquartile range = 1.7). Mean repetitions of practice completed per therapy day was 86.1 (SD = 76.5, median = 68.5, interquartile range = 88.2). Variation in practice dose was best explained by age (-1.3 repetitions per year of age, p = 0.04) and cognitive impairment (-34.9 repetitions, p = 0.03). In participants without cognitive impairment (n = 73) variation in dose was best explained by stroke severity (modified Rankin Sale = 5, -48.4 repetitions, p = 0.01), and the inability to grasp/release (Box and Block Test = 0, +48.3 repetitions, p = 0.03).

CONCLUSIONS

The amount of arm practice completed was low. Daily sessions were often not provided as recommended in clinical guidelines. Clinicians should focus on strategies to increase intensity and opportunities for arm practice.Implications for RehabilitationDose (repetitions) of arm practice varied greatly during inpatient rehabilitation.Number of arm rehabilitation sessions provided was lower than levels recommended in clinical guidelines.Therapists and researchers should focus on strategies to increase amount of therapy and opportunities for arm practice.

Measuring Community Mobility in Survivors of Stroke Using Global Positioning System Technology: A Prospective Observational Study

BACKGROUND AND PURPOSE

Returning to community mobility is important for people recovering from a stroke, yet few studies have directly measured this construct following inpatient rehabilitation. Using global positioning system (GPS) technology, we examined community mobility of survivors of stroke (SS) over the first year after discharge and compared them to an age-matched comparison group without neurological impairment.

METHODS

We conducted a prospective observational study that included SS (n = 14) and age- and location-matched comparison subjects (CS; n = 6). All participants identified target locations important to their community mobility goals and wore a GPS unit during the first, fifth and ninth weeks after discharge, or from baseline for CS, and at 26 and 52 weeks' follow up. The 6-minute walk test (SMWT), Berg balance test (BBT), Reintegration to Normal Living (RNLI), and Short Form-36 Quality of Life Survey Physical Functioning domain (SF-36-PF) were collected. Number of trips and percentage of targets visited were extracted from GPS data.

RESULTS

Twelve of 14 SS completed 9 weeks, 7 completed the full year, and no CS withdrew. The SS took fewer trips and attained fewer targets compared with CS at weeks 1 and 9, but not at weeks 5, 26, and 52. All 4 clinical outcome measures were significantly correlated to trips (Spearman r for SMWT = 0.5067, BBT = 0.3841, RNLI = 0.4119, and SF-36-PF = 0.4192).

DISCUSSION AND CONCLUSIONS

Directly measured community mobility in SS was decreased through 9 weeks following discharge from inpatient rehabilitation. The limited strength of bivariate correlations between clinical measures and number of trips supported the uniqueness of the community mobility construct.Video Abstract available for more insights from the authors (see Supplemental Digital Content 1, available at: http://links.lww.com/JNPT/A277).

Thumb and finger movement is reduced after stroke: An observational study

Hand motor impairment is common after stroke but there are few comprehensive data on amount of hand movement. This study aimed to compare the amount of thumb and finger movement over an extended period of time in people with stroke and able-bodied people. Fifteen stroke subjects and 15 able-bodied control subjects participated. Stroke subjects had impaired hand function. Movement of the thumb and index finger was recorded using stretch sensors worn on the affected hand (stroke subjects) or the left or right hand (control subjects) for ∼4 hours during the day. A digit movement was defined as a monotonic increase or decrease in consecutive sensor values. Instantaneous digit position was expressed as a percentage of maximal digit flexion. Mixed linear models were used to compare the following outcomes between groups: (1) average amplitude of digit movement, (2) digit cadence and average digit velocity, (3) percentage of digit idle time and longest idle time. Amplitude of digit movement was not different between groups. Cadence at the thumb (between-group mean difference, 95% CI, p value: -0.6 movements/sec, -1.0 to -0.2 movements/sec, p = 0.003) and finger (-0.5 movements/sec, -0.7 to -0.3 movements/sec, p<0.001) was lower in stroke than control subjects. Digit velocity was not different between groups. Thumb idle time was not different between groups, but finger idle time was greater in stroke than control subjects (percentage of idle time: 6%, 1 to 11%, p = 0.02; longest idle time: 375 sec, 29 to 721 sec, p = 0.04). Rehabilitation after stroke should encourage the performance of functional tasks that involve movements at faster cadences, and encourage more frequent movement of the digits with shorter periods of inactivity.

Evaluation of a functional hand orthosis combined with electrical stimulation adjunct to arm-hand rehabilitation in subacute stroke patients with a severely to moderately affected hand function

PURPOSE

To investigate the usability and effectiveness of a functional hand orthosis, combined with electrical stimulation adjunct to therapy-as-usual, on functional use of the moderately/severely impaired hand in sub-acute stroke patients.

MATERIALS AND METHODS

Single case experiment (A-B-A'-design) involving eight sub-acute stroke patients. The functional hand orthosis and electrical stimulation were used for six weeks, four days/week, 45'/day.

OUTCOME MEASURES

Action_Research_Arm_Test, Intrinsic_Motivation_Inventory.

RESULTS

At group level, patients improved 19.2 points (median value) (interquartile range: [8.8, 29.5] points) on the Action_Research_Arm_Test (p = 0.001). After correcting for spontaneous recovery and/or therapy-as-usual effects Action_Research_Arm_Test scores still improved significantly (median: 17.2 points; interquartile range: [5.1, 29.2] points) (p = 0.002). At individual level, six patients had improved as to arm-hand skill performance at follow-up (p < = 0.010). In one patient, arm-hand skill performance improvement did not attain statistical significance. In another patient, no arm-hand skill performance improvement was observed. Average Intrinsic_Motivation_Inventory sub-scores were between 4.6 and 6.3 (maximum: 7), except for 'perceived pressure/tension' (3.3).

CONCLUSION

Sub-acute stroke patients who display only little/modest improvement on their capacity to perform daily activities, seem to benefit from training with a dynamic arm orthosis in combination with electrical stimulation. Patients' perceived intrinsic motivation and sense of self-regulation was high. Implications for rehabilitation Arm-hand training featuring the dynamic hand orthosis in combination with electrical stimulation shows a shift from no dexterity to dexterity. As to the users' experience regarding the dynamic hand orthosis, patients perceive a high-intrinsic motivation and sense of self-regulation. Combining the orthosis with electrical stimulation creates opportunities for a nonfunctional hand towards task-oriented training.

Contribution of Neuro-Imaging for Prediction of Functional Recovery after Ischemic Stroke

Prediction measures of recovery and outcome after stroke perform with only modest levels of accuracy if based only on clinical data. Prediction scores can be improved by including morphologic imaging data, where size, location, and development of the ischemic lesion is best documented by magnetic resonance imaging. In addition to the primary lesion, the involvement of fiber tracts contributes to prognosis, and consequently the use of diffusion tensor imaging (DTI) to assess primary and secondary pathways improves the prediction of outcome and of therapeutic effects. The recovery of ischemic tissue and the progression of damage are dependent on the quality of blood supply. Therefore, the status of the supplying arteries and of the collateral flow is not only crucial for determining eligibility for acute interventions, but also has an impact on the potential to integrate areas surrounding the lesion that are not typically part of a functional network into the recovery process. The changes in these functional networks after a localized lesion are assessed by functional imaging methods, which additionally show altered pathways and activated secondary centers related to residual functions and demonstrate changes in activation patterns within these networks with improved performance. These strategies in some instances record activation in secondary centers of a network, for example, also in homolog contralateral areas, which might be inhibitory to the recovery of primary centers. Such findings might have therapeutic consequences, for example, image-guided inhibitory stimulation of these areas. In the future, a combination of morphological imaging including DTI of fiber tracts and activation studies during specific tasks might yield the best information on residual function, reserve capacity, and prospects for recovery after ischemic stroke.

Changes in arm-hand function and arm-hand skill performance in patients after stroke during and after rehabilitation

BACKGROUND

Arm-hand rehabilitation programs applied in stroke rehabilitation frequently target specific populations and thus are less applicable in heterogeneous patient populations. Besides, changes in arm-hand function (AHF) and arm-hand skill performance (AHSP) during and after a specific and well-described rehabilitation treatment are often not well evaluated.

METHOD

This single-armed prospective cohort study featured three subgroups of stroke patients with either a severely, moderately or mildly impaired AHF. Rehabilitation treatment consisted of a Concise_Arm_and_hand_ Rehabilitation_Approach_in_Stroke (CARAS). Measurements at function and activity level were performed at admission, clinical discharge, 3, 6, 9 and 12 months after clinical discharge.

RESULTS

Eighty-nine stroke patients (M/F:63/23; mean age:57.6yr (+/-10.6); post-stroke time:29.8 days (+/-20.1)) participated. All patients improved on AHF and arm-hand capacity during and after rehabilitation, except on grip strength in the severely affected subgroup. Largest gains occurred in patients with a moderately affected AHF. As to self-perceived AHSP, on average, all subgroups improved over time. A small percentage of patients declined regarding self-perceived AHSP post-rehabilitation.

CONCLUSIONS

A majority of stroke patients across the whole arm-hand impairment severity spectrum significantly improved on AHF, arm-hand capacity and self-perceived AHSP. These were maintained up to one year post-rehabilitation. Results may serve as a control condition in future studies.