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Background: Tailored rehabilitation programs for stroke patients cannot be made without knowledge of their recovery potential. The aim of this study is to characterize the functional recovery patterns of ischemic stroke (IS) and intracerebral hemorrhage (ICH) patients under post-acute care stroke rehabilitation. Methods: This retrospective study analyzed the data of patients enrolled in the Post-Acute Care-Cerebrovascular Disease (PAC-CVD) rehabilitation program, which provides an individualized 1- to 3-hour intensive physical, occupational, and speech and language therapy for post-acute stroke patients in Taoyuan Chang Gung Memorial hospital in Taiwan. Our primary endpoint measure was Barthel Index (BI), and secondary endpoint measures included other 12 functional measures. Results: A total of 489 patients were included for analysis. Patients with stroke history had less BI improvement than those who suffered their first-ever stroke. In first-ever stroke patients who had completed 6 to 12 weeks of PAC-CVD program, subcortical ICH patients had greater BI, quality of life, sensation, and balance improvements, and had greater late-phase recovery than their IS counterparts. In IS patients, those with age > 75 had less BI improvement; those with National Institute of Health Stroke Scale (NIHSS) score 1– 5 had greater Motor Activity Log quality of use (MAL-quality) improvement than those with NIHSS score > 5; those with Mini-Mental State Examination (MMSE) score ≥ 24 had greater BI and instrumental activities of daily living (IADL) improvement. Using the general linear model, previous stroke (ß: − 6.148, p=0.01) and subcortical ICH (ß: 5.04, p=0.03) were factors associated with BI improvement. Conclusion: Subcortical ICH patients have greater functional improvement and greater late-phase recovery than their IS counterparts following PAC rehabilitation. More studies are needed to validate our findings and unravel the underlying mechanisms of stroke recovery to optimize the treatment strategy following a stroke. [ABSTRACT FROM AUTHOR]

Objective To identify the baseline motor characteristics of the patients who responded to 3 prominent intervention programs. Design Observational cohort study. Setting Outpatient rehabilitation clinics. Participants Individuals with chronic stroke (N=174). Interventions Participants received 30 hours of constraint-induced movement therapy (CIMT), robot-assisted therapy, or mirror therapy (MT). Main Outcome Measures The primary outcome measure was the change score of the Upper Extremity Fugl-Meyer Assessment (UE-FMA). The potential predicting variables were baseline proximal, distal, and total UE-FMA and Action Research Arm Test scores. We combined polynomial regression analyses and the minimal clinically important difference to stratify the patients as responders and nonresponders for each intervention approach. Results Baseline proximal UE-FMA scores significantly predicted clinically important improvement on the primary outcome measure after all 3 interventions. Participants with baseline proximal UE-FMA scores of approximately <30 benefited significantly from CIMT and robot-assisted therapy, whereas participants with scores between 21 and 35 demonstrated significant improvement after MT. Baseline distal and total UE-FMA and Action Research Arm Test scores could also predict upper limb improvement after CIMT and MT, but not after robot-assisted therapy. Conclusions This study could inform clinicians about the selection of suitable rehabilitation approaches to help patients achieve clinically meaningful improvement in upper extremity function. [ABSTRACT FROM AUTHOR]

Background: Robot-assisted therapy (RT) is a widely used intervention approach to enhance motor recovery in patients after stroke, but its effects on functional improvement remained uncertain. Neuromuscular electrical stimulation (NMES) is one potential adjuvant intervention approach to RT that could directly activate the stimulated muscles and improve functional use of the paretic hand. Methods: This was a randomized, double-blind, sham-controlled study. Thirty-nine individuals with chronic stroke were randomly assigned to the RT combined with NMES (RT + ES) or to RT with sham stimulation (RT + Sham) groups. The participants completed the intervention 90 to 100 minutes/day, 5 days/week for 4 weeks. The outcome measures included the upper extremity Fugl-Meyer Assessment (UE-FMA), modified Ashworth scale (MAS), Wolf Motor Function Test (WMFT), Motor Activity Log (MAL), and Stroke Impact Scale 3.0 (SIS). All outcome measures were assessed before and after intervention, and the UE-FMA, MAL, and SIS were reassessed at 3 months of follow-up. Results: Compared with the RT + Sham group, the RT + ES group demonstrated greater improvements in wrist flexor MAS score, WMFT quality of movement, and the hand function domain of the SIS. For other outcome measures, both groups improved significantly after the interventions, but no group differences were found. Conclusion: RT + ES induced significant benefits in reducing wrist flexor spasticity and in hand movement quality in patients with chronic stroke. Trial Registration: ClinicalTrials.gov. NCT01655446. [ABSTRACT FROM AUTHOR]

Objective To examine the concurrent and predictive validity of measurements of kinematic variables during reaching tasks with and without a trunk constraint in individuals with stroke. Design Randomized controlled trials. Settings Hospitals and a laboratory. Participants Individuals with stroke (N=95) enrolled in previous and ongoing clinical trials. Interventions Upper limb training protocols were 90 to 120 minutes of intervention every weekday for 3 to 4 weeks. Main Outcome Measures Functional capacity was assessed using the Action Research Arm Test and motor impairment using the Fugl-Meyer Assessment for the Upper Extremity. Movement kinematics were measured during a reaching task with and without a trunk constraint. We derived 5 endpoint control variables and 3 joint recruitment variables for estimating concurrent and predictive validity. Results The adjusted R 2 values for the constraint tasks ranged from .24 to .38 and for the unconstraint tasks from .29 to .40. Movement time was the most prominent kinematic variable for the Fugl-Meyer Assessment for the Upper Extremity before and after the intervention ( P <.05). For the Action Research Arm Test, movement time and endpoint displacement were the most significant variables before and after the intervention, respectively ( P <.05). Conclusions Measuring kinematic performance during an unconstrained task is appropriate and possibly sufficient to represent motor impairment and functional capacity of individuals with stroke. Movement time is the dominant variable associated with motor impairment and functional capacity, and endpoint displacement is unique in reflecting functional capacity of individuals with stroke. [ABSTRACT FROM AUTHOR]

Prediction of post-stroke functional outcomes is crucial for allocating medical resources. In this study, a total of 577 patients were enrolled in the Post-Acute Care-Cerebrovascular Disease (PAC-CVD) program, and 77 predictors were collected at admission. The outcome was whether a patient could achieve a Barthel Index (BI) score of >60 upon discharge. Eight machine-learning (ML) methods were applied, and their results were integrated by stacking method. The area under the curve (AUC) of the eight ML models ranged from 0.83 to 0.887, with random forest, stacking, logistic regression, and support vector machine demonstrating superior performance. The feature importance analysis indicated that the initial Berg Balance Test (BBS-I), initial BI (BI-I), and initial Concise Chinese Aphasia Test (CCAT-I) were the top three predictors of BI scores at discharge. The partial dependence plot (PDP) and individual conditional expectation (ICE) plot indicated that the predictors' ability to predict outcomes was the most pronounced within a specific value range (e.g., BBS-I < 40 and BI-I < 60). BI at discharge could be predicted by information collected at admission with the aid of various ML models, and the PDP and ICE plots indicated that the predictors could predict outcomes at a certain value range. [ABSTRACT FROM AUTHOR]

Objectives: Prediction of activities of daily living (ADL) is crucial for optimized care of post-stroke patients. However, no suitably-validated and practical models are currently available in clinical practice.Methods: Participants of a Post-acute Care-Cerebrovascular Diseases (PAC-CVD) program from a reference hospital in Taiwan between 2014 and 2016 were enrolled in this study. Based on 15 rehabilitation assessments, machine learning (ML) methods, namely logistic regression (LR), support vector machine (SVM), and random forest (RF), were used to predict the Barthel index (BI) status at discharge. Furthermore, SVM and linear regression were used to predict the actual BI scores at discharge.Results: A total of 313 individuals (men: 208; women: 105) were enrolled in the study. All the classification models outperformed single assessments in predicting the BI statuses of the patients at discharge. The performance of the LR and RF algorithms was higher (area under ROC curve (AUC): 0.79) than that of SVM algorithm (AUC: 0.77). In addition, the mean absolute errors of both SVM and linear regression models in predicting the actual BI score at discharge were 9.86 and 9.95, respectively.Conclusions: The proposed ML-based method provides a promising and practical computer-assisted decision making tool for predicting ADL in clinical practice. [ABSTRACT FROM AUTHOR]