Identifying the characteristics of patients with stroke who have difficulty benefiting from gait training with the hybrid assistive limb: a retrospective cohort study.

Robot-assisted gait training is effective for walking independence in stroke rehabilitation, the hybrid assistive limb (HAL) is an example. However, gait training with HAL may not be effective for everyone, and it is not clear who is not expected to benefit. Therefore, we aimed to identify the characteristics of stroke patients who have difficulty gaining benefits from gait training with HAL. We conducted a single-institutional retrospective cohort study. The participants were 82 stroke patients who had received gait training with HAL during hospitalization. The dependent variable was the functional ambulation category (FAC) that a measure of gait independence in stroke patients, and five independent [age, National Institutes of Health Stroke Scale, Brunnstrom recovery stage (BRS), days from stroke onset, and functional independence measure total score (cognitive items)] variables were selected from previous studies and analyzed by logistic regression analysis. We evaluated the validity of logistic regression analysis by using several indicators, such as the area under the curve (AUC), and a confusion matrix. Age, days from stroke onset to HAL initiation, and BRS were identified as factors that significantly influenced walking independence through gait training with HAL. The AUC was 0.86. Furthermore, after building a confusion matrix, the calculated binary accuracy, sensitivity (recall), and specificity were 0.80, 0.80, and 0.81, respectively, indicated high accuracy. Our findings confirmed that older age, greater degree of paralysis, and delayed initiation of HAL-assisted training after stroke onset were associated with increased likelihood of walking dependence upon hospital discharge.

Does frequent use of an exoskeletal upper limb robot improve motor function in stroke patients?

PURPOSE: To determine how differences in frequency of the single-joint hybrid assistive limb (HAL-SJ) use affect the improvement of upper limb motor function and activities of daily living (ADL) in stroke patients. MATERIALS AND METHODS: Subacute stroke patients were divided into the high or low frequency of HAL-SJ use groups. The two groups were matched by propensity score, and the degree of changes 30 days after initiating HAL-SJ use was compared. A logistic regression analysis was performed to examine whether frequent use would increase the number of subjects experiencing the efficacy of more than the minimal clinically important difference (MCID) of Fugl-Meyer assessment (FMA). RESULTS: Twenty-five stroke patients were matched by propensity score, and nine pairs were matched. The high-frequency group showed a significantly superior increase to total FMA shoulder, elbow, forearm, and Barthel index compared with the low-frequency group. Logistic regression analysis revealed no significant associations between frequent use and MCID. CONCLUSIONS: The frequency of HAL-SJ use may affect the improvement of motor function and ADL ability of the upper limb with exception of the fingers and wrist. However, the frequency of intervention was not effective enough to further increase the number of subjects with clinically meaningful changes in upper limb motor function.IMPLICATIONS FOR REHABILITATIONThe current study aimed to clarify how differences in the frequency of single-joint hybrid assistive limb (HAL-SJ) use can affect the improvement of upper-limb motor functions and ADL in subacute stroke patients.Our results implied that the frequency of HAL-SJ use may influence the recovery of upper limb function.However, even if HAL-SJ is used frequently, it does not mean that more patients will achieve clinically meaningful recovery.

Early Initiation of Exoskeletal Robotic Gait Training Improves Functional Outcomes in the Patients with Stroke: A Retrospective Observational Study.

INTRODUCTION: The effect of early initiation of gait training using hybrid assistive limb (HAL) remains unclear. This observational study aimed to investigate whether early initiation of gait training using HAL improves functional outcomes in patients with stroke. METHODS: We retrospectively analyzed patients with acute stroke admitted to our facility. HAL was used for exoskeletal robotic gait training. Study participants were median split into an early group and a late group based on the days from stroke onset to initiation of gait training using HAL. The functional outcomes, defined by the Brunnstrom recovery stage (BRS), modified Rankin Scale (mRS), and Functional Independence Measure (FIM) at discharge, were compared using propensity score-matched analysis. RESULTS: We performed a propensity score-matched analysis in 63 patients with stroke (31 from the early group and 32 from the late group), and 17 pairs were matched. There were no significant differences in discharge in the BRS of the upper limb and finger in the post-matched cohort. On the other hand, the BRS of the lower limb in the early group was significantly higher than that in the late group. In addition, the mRS, but not FIM scores, was significantly better in the early group than that in the late group. CONCLUSIONS: In conclusion, early initiation of gait training using HAL might improve the motor function of the paralyzed lower limb and disability in patients with stroke.

Clinical Prediction Rule for Identifying the Stroke Patients who will Obtain Clinically Important Improvement of Upper Limb Motor Function by Robot-Assisted Upper Limb.

BACKGROUND: The number of studies on the characteristics of patients with stroke who would benefit from robot-assisted upper limb rehabilitation is limited, and there are no clear criteria for determining which individuals should receive such treatment. The current study aimed to develop a clinical prediction rule using machine learning to identify the characteristics of patients with stroke who can the achieve minimal clinically important difference of the Fugl-Meyer Upper Extremity Evaluation (FMA-UE) after single-joint hybrid assistive limb (HAL-SJ) rehabilitation. METHODS: This study included 71 patients with subacute stroke who received HAL-SJ rehabilitation. The chi-square automatic interaction detector (CHAID) model was applied to predict improvement in upper limb motor function. Based the analysis using CHAID, age, sex, days from stroke onset to the initiation of HAL-SJ rehabilitation, and upper limb motor and cognitive functions were used as independent variables. Improvement in upper limb motor function was determined based on the minimal clinically important difference of the FMA-UE, which was used as a dependent variable. RESULTS: According to the CHAID model, the FMA-UE score during the initiation of HAL-SJ rehabilitation was the most significant predictive factor for patients who are likely to respond to the intervention. Interestingly, this therapy was more effective in patients with moderate upper limb motor dysfunction and early initiation of HAL-SJ rehabilitation. The accuracy of the CHAID model was 0.89 (95% confidence interval: 0.81-0.96). CONCLUSION: We developed a clinical prediction rule for identifying the characteristics of patients with stroke whose upper limb motor function can improve with HAL-SJ rehabilitation.

Involvement of the Rostromedial Prefrontal Cortex in Human-Robot Interaction: fNIRS Evidence From a Robot-Assisted Motor Task.

Assistive exoskeleton robots are being widely applied in neurorehabilitation to improve upper-limb motor and somatosensory functions. During robot-assisted exercises, the central nervous system appears to highly attend to external information-processing (IP) to efficiently interact with robotic assistance. However, the neural mechanisms underlying this process remain unclear. The rostromedial prefrontal cortex (rmPFC) may be the core of the executive resource allocation that generates biases in the allocation of processing resources toward an external IP according to current behavioral demands. Here, we used functional near-infrared spectroscopy to investigate the cortical activation associated with executive resource allocation during a robot-assisted motor task. During data acquisition, participants performed a right-arm motor task using elbow flexion-extension movements in three different loading conditions: robotic assistive loading (ROB), resistive loading (RES), and non-loading (NON). Participants were asked to strive for kinematic consistency in their movements. A one-way repeated measures analysis of variance and general linear model-based methods were employed to examine task-related activity. We demonstrated that hemodynamic responses in the ventral and dorsal rmPFC were higher during ROB than during NON. Moreover, greater hemodynamic responses in the ventral rmPFC were observed during ROB than during RES. Increased activation in ventral and dorsal rmPFC subregions may be involved in the executive resource allocation that prioritizes external IP during human-robot interactions. In conclusion, these findings provide novel insights regarding the involvement of executive control during a robot-assisted motor task.

Comparison of Supervised Machine Learning Algorithms for Classifying of Home Discharge Possibility in Convalescent Stroke Patients: A Secondary Analysis.

OBJECTIVES: Classifying the possibility of home discharge is important during stroke rehabilitation to support decision-making. There have been several studies on supervised machine learning algorithms, but only a few have compared the performance of different algorithms based on the same dataset for the classification of home discharge possibility. Therefore, we aimed to evaluate five supervised machine learning algorithms for the classification of home discharge possibility in stroke patients. MATERIALS AND METHODS: This was a secondary analysis based on the data of 481 stroke patients from the database of our institution. Five models developed by supervised machine learning algorithms, including decision tree (DT), linear discriminant analysis (LDA), k-nearest neighbors (k-NN), support vector machine (SVM), and random forest (RF) were compared by constructing a classification system based on the same dataset. Several parameters including classification accuracy, area under the curve (AUC), and F1 score (a weighted average of precision and recall) were used for model evaluation. RESULTS: The k-NN model had the best classification accuracy (84.0%) with a moderate AUC (0.88) and F1 score (87.8). The SVM model also showed high classification accuracy (82.6%) along with the highest AUC (0.91), sensitivity (94.4), negative predictive value (87.5), and negative likelihood ratio (0.088). The DT, LDA, and RF models had high classification accuracies (≥ 79.9%) with moderate AUCs (≥ 0.84) and F1 scores (≥ 83.8). CONCLUSIONS: Regarding model performance, the k-NN and SVM seemed the best candidate algorithms for classifying the possibility of home discharge in stroke patients.

Machine Learning Algorithm Identifies the Importance of Environmental Factors for Hospital Discharge to Home of Stroke Patients using Wheelchair after Discharge.

BACKGROUND AND PURPOSE: Physical environmental factors are generally likely to become barriers for discharge to home of wheelchair users, compared with non-wheelchair users. However, the importance of environmental factors has not been investigated adequately. Application of machine learning technology might efficiently identify the most influential factors, although it is not easy to interpret and integrate various information including individual and environmental factors in clinical stroke rehabilitation. This study aimed to identify the influential factors affecting home discharge in the stroke patients who use a wheelchair after discharge by using machine learning technology. METHODS: This study used the rehabilitation database of our facility, which includes all stroke patients admitted into the convalescence rehabilitation ward. The chi-squared automatic interaction detection (CHAID) algorithm was used to develop a model to classify wheelchair-using stroke patients discharged to home or not-to-home. RESULTS: Among the variables, including basic information, motor functional factor, activities of daily living ability factor, and environmental factors, the CHAID model identified house renovation and the existence of sloping roads around the house as the first and second discriminators for home discharge. CONCLUSIONS: Our present results could scientifically clarify that the clinician need to focus on the physical environmental factors for achieving home discharge in the patients who use a wheelchair after discharge.

Anti-pruritic effect of nemolizumab in hemodialysis patients with uremic pruritus: a phase II, randomized, double-blind, placebo-controlled clinical study.

BACKGROUND: The pathophysiology of uremic pruritus (UP), which is characterized by systemic and intractable itching, remains unclear. As interleukin (IL)-31 may be involved, we conducted a phase II, randomized, controlled study to evaluate nemolizumab (anti-IL-31 receptor A antibody) in Japanese hemodialysis patients with UP. METHODS: Patients were randomly assigned (1:1:1:1:1) to one of four double-blind groups (receiving a single subcutaneous injection of nemolizumab 0.125, 0.5, or 2.0 mg/kg, or placebo on Day 1) or an open-label reference group (receiving oral nalfurafine hydrochloride 2.5-5 µg once daily for 12 weeks). The primary endpoint was the difference in the absolute change in pruritus visual analog scale (VAS) at Week 4 between placebo and each nemolizumab group. RESULTS: The primary efficacy endpoint was not met. The mean change from baseline with all three nemolizumab doses at Week 1, and with 0.5 mg/kg at Week 4, was greater than with placebo. Least square mean differences (95% confidence intervals) in the absolute changes between the placebo arm and each nemolizumab arm were - 2.4 (- 19.7, 14.9) for 0.125 mg/kg, - 8.7 (- 26.6, 9.2) for 0.5 mg/kg, and 0.4 (- 17.0, 17.8) for 2.0 mg/kg. Secondary efficacy parameters including the Shiratori severity score and 5-D itch score failed to show between-group differences. Patients with higher serum IL-31 levels at screening tended to have greater pruritus VAS reductions following nemolizumab treatment. CONCLUSIONS: In this phase II study in patients with UP, the primary efficacy parameter was not met. Nemolizumab was generally well tolerated with no clinically significant safety concerns. CLINICAL TRIAL REGISTRATION: JAPIC: JapicCTI-152961, https://www.clinicaltrials.jp/cti-user/trial/ShowDirect.jsp?japicId=JapicCTI-152961 .

Decision Tree Algorithm Identifies Stroke Patients Likely Discharge Home After Rehabilitation Using Functional and Environmental Predictors.

BACKGROUND AND PURPOSE: The importance of environmental factors for stroke patients to achieve home discharge was not scientifically proven. There are limited studies on the application of the decision tree algorithm with various functional and environmental variables to identify stroke patients with a high possibility of home discharge. The present study aimed to identify the factors, including functional and environmental factors, affecting home discharge after stroke inpatient rehabilitation using the machine learning method. METHOD: This was a cohort study on data from the maintained database of all patients with stroke who were admitted to the convalescence rehabilitation ward of our facility. In total, 1125 stroke patients were investigated. We developed three classification and regression tree (CART) models to identify the possibility of home discharge after inpatient rehabilitation. RESULTS: Among three models, CART model incorporating basic information, functional factor, and environmental factor variables achieved the highest accuracy for identification of home discharge. This model identified FIM dressing of the upper body (score of ≤2 or >2) as the first single discriminator for home discharge. Performing house renovation was associated with a high possibility of home discharge even in patients with stroke who had a poor FIM score in the ability to dress the upper body (≤2) at admission into the convalescence rehabilitation ward. Interestingly, many patients who performed house renovation have achieved home discharge regardless of the degree of lower limb paralysis. CONCLUSION: We identified the influential factors for realizing home discharge using the decision tree algorithm, including environmental factors, in patients with convalescent stroke.

Development and Validation of Machine Learning-Based Prediction for Dependence in the Activities of Daily Living after Stroke Inpatient Rehabilitation: A Decision-Tree Analysis.

BACKGROUND AND PURPOSE: Accurate prediction using simple and changeable variables is clinically meaningful because some known-predictors, such as stroke severity and patients age cannot be modified with rehabilitative treatment. There are limited clinical prediction rules (CPRs) that have been established using only changeable variables to predict the activities of daily living (ADL) dependence of stroke patients. This study aimed to develop and assess the CPRs using machine learning-based methods to identify ADL dependence in stroke patients. METHODS: In total, 1125 stroke patients were investigated. We used a maintained database of all stroke patients who were admitted to the convalescence rehabilitation ward of our facility. The classification and regression tree (CART) methodology with only the FIM subscores was used to predict the ADL dependence. RESULTS: The CART method identified FIM transfer (bed, chair, and wheelchair) (score ≤ 4.0 or > 4.0) as the best single discriminator for ADL dependence. Among those with FIM transfer (bed, chair, and wheelchair) score > 4.0, the next best predictor was FIM bathing (score ≤ 2.0 or > 2.0). Among those with FIM transfer (bed, chair, and wheelchair) score ≤ 4.0, the next predictor was FIM transfer toilet (score ≤ 3 or > 3). The accuracy of the CART model was 0.830 (95% confidence interval, 0.804-0.856). CONCLUSION: Machine learning-based CPRs with moderate predictive ability for the identification of ADL dependence in the stroke patients were developed.