Fast Outcome Categorization of the Upper Limb After Stroke

Development and External Validation of a Motor Intention-Integrated Prediction Model for Upper Extremity Motor Recovery After Intention-Driven Robotic Hand Training for Chronic Stroke

OBJECTIVE

To derive and validate a prediction model for minimal clinically important differences (MCIDs) in upper extremity (UE) motor function after intention-driven robotic hand training using residual voluntary electromyography (EMG) signals from affected UE.

DESIGN

A prospective longitudinal multicenter cohort study. We collected preintervention candidate predictors: demographics, clinical characteristics, Fugl-Meyer assessment of UE (FMAUE), Action Research Arm Test scores, and motor intention of flexor digitorum and extensor digitorum (ED) measured by EMG during maximal voluntary contraction (MVC). For EMG measures, recognizing challenges for stroke survivors to move paralyzed hand, peak signals were extracted from 8 time windows during MVC-EMG (0.1-5s) to identify subjects' motor intention. Classification and regression tree algorithm was employed to predict survivors with MCID of FMAUE. Relationship between predictors and motor improvements was further investigated.

SETTING

Nine rehabilitation centers.

PARTICIPANTS

Chronic stroke survivors (N=131), including 87 for derivation sample, and 44 for validation sample.

INTERVENTIONS

All participants underwent 20-session robotic hand training (40min/session, 3-5sessions/wk).

MAIN OUTCOME MEASURES

Prediction efficacies of models were assessed by area under the receiver operating characteristic curve (AUC). The best effective model was final model and validated using AUC and overall accuracy.

RESULTS

The best model comprised FMAUE (cutoff score, 46) and peak activity of ED from 1-second MVC-EMG (MVC-EMG 4.604 times higher than resting EMG), which demonstrated significantly higher prediction accuracy (AUC, 0.807) than other time windows or solely using clinical scores (AUC, 0.595). In external validation, this model displayed robust prediction (AUC, 0.916). Significant quadratic relationship was observed between ED-EMG and FMAUE increases.

CONCLUSIONS

This study presents a prediction model for intention-driven robotic hand training in chronic stroke survivors. It highlights significance of capturing motor intention through 1-second EMG window as a predictor for MCID improvement in UE motor function after 20-session robotic training. Survivors in 2 conditions showed high percentage of clinical motor improvement: moderate-to-high motor intention and low-to-moderate function; as well as high intention and high function.

A Hierarchical Bayesian Model for Cyber-Human Assessment of Movement in Upper Extremity Stroke Rehabilitation

The evidence-based quantification of the relation between changes in movement quality and functionality can assist clinicians in achieving more effective structuring or adapting of therapy. In this paper, clinicians rated task, segment, and composite movement feature performance for 478 videos of stroke survivors executing upper extremity therapy tasks. We used the clinician ratings to develop a Hierarchical Bayesian Model (HBM) with task, segment, and composite layers for computing the statistical relation of movement quality changes to function. The model was enhanced through a detailed correlation graph ( ∆HBM ) that links computationally extracted kinematics with clinician-rated composite features for different task-segment combinations. Utilizing the weights and correlation graphs, we finally derive reverse cascading probabilities of the proposed HBM from kinematics to composite features, segments, and tasks. In a test involving 98 cases where clinician ratings differed, the HBM resolved 95% of these discrepancies. The model effectively aligned kinematic data with specific task-segment combinations in over 90% of cases. Once the HBM is expanded and refined through additional data it can be used for the automated calculation of statistical relations between changes in kinematics and performance of functional tasks and the generation of therapy assessment recommendations for clinicians. While our work primarily focuses on the upper extremities of stroke survivors, the HBM can be adapted to many other neurorehabilitation contexts.

Stem cell therapy: a new hope for stroke and traumatic brain injury recovery and the challenge for rural minorities in South Carolina

Stroke and traumatic brain injury (TBI) are a significant cause of death and disability nationwide. Both are considered public health concerns in rural communities in the state of South Carolina (SC), particularly affecting the African American population resulting in considerable morbidity, mortality, and economic burden. Stem cell therapy (SCT) has emerged as a potential intervention for both diseases with increasing research trials showing promising results. In this perspective article, the authors aim to discuss the current research in the field of SCT, the results of early phase trials, and the utilization of outcome measures and biomarkers of recovery. We searched PubMed from inception to December 2023 for articles on stem cell therapy in stroke and traumatic brain injury and its impact on rural communities, particularly in SC. Early phase trials of SCT in Stroke and Traumatic Brain injury yield promising safety profile and efficacy results, but the findings have not yet been consistently replicated. Early trials using mesenchymal stem cells for stroke survivors showed safety, feasibility, and improved functional outcomes using broad and domain-specific outcome measures. Neuroimaging markers of recovery such as Functional Magnetic Resonance Imaging (fMRI) and electroencephalography (EEG) combined with neuromodulation, although not widely used in SCT research, could represent a breakthrough when evaluating brain injury and its functional consequences. This article highlights the role of SCT as a promising intervention while addressing the underlying social determinants of health that affect therapeutic outcomes in relation to rural communities such as SC. It also addresses the challenges ethical concerns of stem cell sourcing, the high cost of autologous cell therapies, and the technical difficulties in ensuring transplanted cell survival and strategies to overcome barriers to clinical trial enrollment such as the ethical concerns of stem cell sourcing, the high cost of autologous cell therapies, and the technical difficulties in ensuring transplanted cell survival and equitable healthcare.

Accuracy and Reliability of Remote Categorization of Upper Limb Outcome After Stroke

BACKGROUND

There is an increasing need for motor assessments after stroke that can be performed quickly and remotely. The Fast Outcome Categorization of the Upper Limb after Stroke-4 (FOCUS-4) assessment remotely classifies upper limb outcome into 1 of 4 categories after stroke and was developed via retrospective analysis of Action Research Arm Test (ARAT) scores.

OBJECTIVE

The aim of this study was to prospectively evaluate the accuracy and reliability of FOCUS-4 assessments for categorizing upper limb outcome after stroke when administered remotely during a videocall compared to an in-person ARAT.

METHODS

Data were collected from 26 participants at 3 months post-stroke (3M), 27 participants at 6 months post-stroke (6M), and 56 participants at the chronic stage of stroke (>6M). Participants performed an in-person ARAT and a remote FOCUS-4 assessment administered during a videocall, and accuracy was evaluated by comparing the upper limb outcome categories. Participants at the chronic stage of stroke also performed a second remote FOCUS-4 assessment to assess between-day reliability.

RESULTS

Overall accuracy of the remote FOCUS-4 assessment was 88% at 3M and 96% at 6M. Overall accuracy of the first and second remote FOCUS-4 assessments at the chronic stage was 75% and 79%, respectively. Reliability of the FOCUS-4 assessment at the chronic stage was 82%. The remote FOCUS-4 assessment was most accurate and reliable for participants with mild or severe upper limb functional impairment.

CONCLUSIONS

The remote FOCUS-4 assessment has potential to classify upper limb functional capacity or to screen possible participants for stroke trials, but external validation is required.

Performing a shortened version of the Action Research Arm Test in immersive virtual reality to assess post-stroke upper limb activity

BACKGROUND

To plan treatment and measure post-stroke recovery, frequent and time-bounded functional assessments are recommended. With increasing needs for neurorehabilitation advances, new technology based methods, such as virtual reality (VR) have emerged. Here, we developed an immersive VR version of the Action Research Arm Test (ARAT-VR) to complement neurorehabilitation.

OBJECTIVE

This study aimed to assess the validity, usability and test-retest reliability of the ARAT-VR among individuals with stroke, healthcare professionals and healthy control subjects (HCS).

METHODS

Among the 19 items of the ARAT, 13 items were selected and developed in immersive VR. 11 healthcare professionals, 30 individuals with stroke, and 25 HCS were recruited. Content validity was assessed by asking healthcare professionals to rate the difficulty of performing each item of the ARAT-VR in comparison to the classical Action Research Arm Test (ARAT-19). Concurrent validity was first measured using correlation (Spearman tests) between the ARAT-VR and ARAT-19 scores for the individuals with stroke, and second through correlation and comparison between the scores of the ARAT-VR and the reduced version of the ARAT (ARAT-13) for both individuals with stroke and HCS (Wilcoxon signed rank tests and Bland-Altman plots). Usability was measured using the System Usability Scale. A part of individuals with stroke and HCS were re-tested following a convenient delay to measure test-retest reliability (Intra-class correlation and Wilcoxon tests).

RESULTS

Regarding the content validity, median difficulty of the 13 ARAT-VR items (0[0 to - 1] to 0[0-1]) evaluated by healthcare professionals was rated as equivalent to the classical ARAT for all tasks except those involving the marbles. For these, the difficulty was rated as superior to the real tasks (1[0-1] when pinching with the thumb-index and thumb-middle fingers, and 1[0-2] when pinching with thumb-ring finger). Regarding the concurrent validity, for paretic hand scores, there were strong correlations between the ARAT-VR and ARAT-13 (r = 0.84), and between the ARAT-VR and ARAT-19 (r = 0.83). Usability (SUS = 82.5[75-90]) and test-retest reliability (ICC = 0.99; p < 0.001) were excellent.

CONCLUSION

The ARAT-VR is a valid, usable and reliable tool that can be used to assess upper limb activity among individuals with stroke, providing potential to increase assessment frequency, remote evaluation, and improve neurorehabilitation. Trial registration https://clinicaltrials.gov/ct2/show/NCT04694833 ; Unique identifier: NCT04694833, Date of registration: 11/24/2020.

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.