Development and Validation of a Predictive Model for Functional Outcome After Stroke Rehabilitation

Return to work within a year after first stroke: blue and white collar workers comparison, predictors and causal mediation assessed during inpatient rehabilitation

BACKGROUND

Most research focuses around impairments in body function and structure, with relatively only a small number exploring their social impact.

OBJECTIVES

1) compare characteristics for individuals who before stroke were blue collar vs. white collar workers 2) identify clinical, functional, and job-related factors associated with return to work within 1 year after discharge 3) identify specific ADL individual items (assessed at rehabilitation discharge) as return to work predictors and 4) identify return to work causal mediators.

METHODS

Retrospective observational cohort study, analyzing adult patients with stroke admitted to rehabilitation between 2007 and 2021, including baseline Barthel Index (BI) and return to work assessments between 2008 and 2022. Kaplan-Meier survival curves and Cox proportional hazards were applied. Causal mediation analyses using 1000-bootstrapped simulations were performed.

RESULTS

A total of 802 individuals were included (14.6% returned to work), 53.6% blue-collar and 46.4% white-collar. Blue-collar workers showed significantly higher proportion of ischemic stroke, diabetes, dyslipidemia, and hypertension.Individuals not returning to work presented a higher proportion of blue collar, dominant side affected, aphasia, lower BI scores, and larger length of stay (LOS). Multivariable Cox proportional hazards identified age at injury, aphasia, hypertension, and total discharge BI score (C-Index = 0.74). Univariable Cox models identified three independent BI items at all levels of independence: bathing (C-Index = 0.58), grooming (C-Index = 0.56) and feeding (C-Index = 0.59). BI efficiency (gain/LOS) was a causal mediator.

CONCLUSION

Blue collar workers showed higher proportion of risk factors and comorbidities. Novel factors, predictors, and a return to work mediator were identified.

Predicting the functional outcome of intensive inpatient rehabilitation after stroke: results from the RIPS Study

BACKGROUND

The complexity of stroke sequelae, the heterogeneity of outcome measures and rehabilitation pathways, and the lack of extensively validated prediction models represent a challenge in predicting stroke rehabilitation outcomes.

AIM

To prospectively investigate a multidimensional set of variables collected at admission to inpatient post-stroke rehabilitation as potential predictors of the functional level at discharge.

DESIGN

Multicentric prospective observational study.

SETTING

Patients were enrolled in four Intensive Rehabilitation Units (IRUs).

POPULATION

Patients were consecutively recruited in the period December 2019-December 2020 with the following inclusion criteria: aged 18+, with ischemic/haemorrhagic stroke, and undergoing inpatient rehabilitation within 30 days from stroke.

METHODS

This is a multicentric prospective observational study. The rehabilitation pathway was reproducible and evidence-based. The functional outcome was disability in activities of daily living, measured by the modified Barthel Index (mBI) at discharge. Potential multidimensional predictors, assessed at admission, included demographics, event description, clinical assessment, functional and cognitive profile, and psycho-social domains. The variables statistically associated with the outcome in the univariate analysis were fed into a multivariable model using multiple linear regression.

RESULTS

A total of 220 patients were included (median [IQR] age: 80 [15], 112 women, 175 ischemic). Median mBI was 26 (43) at admission and 62.5 (52) at discharge. In the multivariable analysis younger age, along with better functioning, fewer comorbidities, higher cognitive abilities, reduced stroke severity, and higher motor functions at admission, remained independently associated with higher discharge mBI. The final model allowed a reliable prediction of discharge functional outcome (adjusted R2=77.2%).

CONCLUSIONS

The model presented in this study, based on easily collectable, reliable admission variables, could help clinicians and researchers to predict the discharge scores of the global functional outcome for persons enrolled in an evidence-based inpatient stroke rehabilitation program.

CLINICAL REHABILITATION IMPACT

A reliable outcome prediction derived from standardized assessment measures and validated treatment protocols could guide clinicians in the management of patients in the subacute phase of stroke and help improve the planning of the rehabilitation individualized project.

Explaining predictors of discharge destination assessed along the patients' acute stroke journey

INTRODUCTION

Accurate prediction of outcome destination at an early stage would help manage patients presenting with stroke. This study assessed the predictive ability of three machine learning (ML) algorithms to predict outcomes at four different stages as well as compared the predictive power of stroke scores.

METHODS

Patients presenting with acute stroke to the Canberra Hospital between 2015 and 2019 were selected retrospectively. 16 potential predictors and one target variable (discharge destination) were obtained from the notes. k-Nearest Neighbour (kNN) and two ensemble-based classification algorithms (Adaptive Boosting and Bootstrap Aggregation) were employed to predict outcomes. Predictive accuracy was assessed at each of the four stages using both overall and per-class accuracy. The contribution of each variable to the prediction outcome was evaluated by the ensemble-based algorithm and using the Relief feature selection algorithm. Various combinations of stroke scores were tested using the aforementioned models.

RESULTS

Of the three ML models, Adaptive Boosting demonstrated the highest accuracy (90%) at Stage 4 in predicting death while the highest overall accuracy (81.7%) was achieved by kNN (k=2/City-block distance). Feature importance analysis has shown that the most important features are the 24-hour Scandinavian Stroke Scale (SSS) and 24-hour National Institutes of Health Stroke Scale (NIHSS) scores, dyslipidaemia, hypertension and premorbid mRS score. For the initial and 24-hour scores, there was a higher correlation (0.93) between SSS scores than for NIHSS scores (0.81). Reducing the overall four scores to InitSSS/24hrNIHSS increased accuracy to 95% in predicting death (Adaptive Boosting) and overall accuracy to 85.4% (kNN). Accuracies at Stage 2 (pre-treatment, 11 predictors) were not far behind those at Stage 4.

CONCLUSION

Our findings suggest that even in the early stages of management, a clinically useful prediction regarding discharge destination can be made. Adaptive Boosting might be the best ML model, especially when it comes to predicting death. The predictors' importance analysis also showed that dyslipidemia and hypertension contributed to the discharge outcome even more than expected. Further, surprisingly using mixed score systems might also lead to higher prediction accuracies.

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.

AI in Rehabilitation Medicine: Opportunities and Challenges

Artificial intelligence (AI) tools are increasingly able to learn from larger and more complex data, thus allowing clinicians and scientists to gain new insights from the information they collect about their patients every day. In rehabilitation medicine, AI can be used to find patterns in huge amounts of healthcare data. These patterns can then be leveraged at the individual level, to design personalized care strategies and interventions to optimize each patient's outcomes. However, building effective AI tools requires many careful considerations about how we collect and handle data, how we train the models, and how we interpret results. In this perspective, we discuss some of the current opportunities and challenges for AI in rehabilitation. We first review recent trends in AI for the screening, diagnosis, treatment, and continuous monitoring of disease or injury, with a special focus on the different types of healthcare data used for these applications. We then examine potential barriers to designing and integrating AI into the clinical workflow, and we propose an end-to-end framework to address these barriers and guide the development of effective AI for rehabilitation. Finally, we present ideas for future work to pave the way for AI implementation in real-world rehabilitation practices.

Predicting motor gains with home-based telerehabilitation after stroke

INTRODUCTION

Telerehabilitation (TR) may be useful for rehabilitation therapy after stroke. However, stroke is a heterogeneous condition, and not all patients can be expected to derive the same benefit from TR, underscoring the need to identify predictors of response to TR.

METHODS

A prior trial provided patients with 6 weeks of intensive rehabilitation therapy targeting arm movement, randomly assigned to be provided in the home via TR (current focus) or in clinic. Eligible patients had moderate arm motor deficits and were in the subacute-chronic stage post stroke. Behavioral gains were measured as change in the arm motor Fugl-Meyer score from baseline to 30 days post therapy. To delineate predictors of TR response, multivariable linear regression was performed, advancing the most significant predictor from each of eight categories: patient demographics, stroke characteristics, medical history, rehabilitation therapy outside of study procedures, motivation, sensorimotor impairment, cognitive/affective deficits, and functional status.

RESULTS

The primary focus was on patients starting TR >90 days post stroke onset (n = 44), among whom female sex, less spasticity, and less visual field defects predicted greater motor gains. This model explained 39.3% of the variance in treatment-related gains. In secondary analysis that also included TR patients enrolled ≤90 days post stroke (total n = 59), only female sex was a predictor of treatment gains. A separate secondary analysis examined patients >90 days post stroke (n = 34) randomized to in-clinic therapy, among whom starting therapy earlier post stroke and less ataxia predicted greater motor gains.

DISCUSSION

Response to TR varies across patients, emphasizing the need to identify characteristics that predict treatment-related behavioral gain. The current study highlights factors that might be important to patient selection for home-based TR after stroke.

Machine learning in predicting outcomes for stroke patients following rehabilitation treatment: A systematic review

OBJECTIVE

This review aimed to summarize the use of machine learning for predicting the potential benefits of stroke rehabilitation treatments, to evaluate the risk of bias of predictive models, and to provide recommendations for future models.

MATERIALS AND METHODS

This systematic review was conducted in accordance with the PRISMA statement and the CHARMS checklist. The PubMed, Embase, Cochrane Library, Scopus, and CNKI databases were searched up to April 08, 2023. The PROBAST tool was used to assess the risk of bias of the included models.

RESULTS

Ten studies within 32 models met our inclusion criteria. The optimal AUC value of the included models ranged from 0.63 to 0.91, and the optimal R2 value ranged from 0.64 to 0.91. All of the included models were rated as having a high or unclear risk of bias, and most of them were downgraded due to inappropriate data sources or analysis processes.

DISCUSSION AND CONCLUSION

There remains much room for improvement in future modeling studies, such as high-quality data sources and model analysis. Reliable predictive models should be developed to improve the efficacy of rehabilitation treatment by clinicians.

Clinical, Neuroimaging and Robotic Measures Predict Long-Term Proprioceptive Impairments following Stroke

Proprioceptive impairments occur in ~50% of stroke survivors, with 20-40% still impaired six months post-stroke. Early identification of those likely to have persistent impairments is key to personalizing rehabilitation strategies and reducing long-term proprioceptive impairments. In this study, clinical, neuroimaging and robotic measures were used to predict proprioceptive impairments at six months post-stroke on a robotic assessment of proprioception. Clinical assessments, neuroimaging, and a robotic arm position matching (APM) task were performed for 133 stroke participants two weeks post-stroke (12.4 ± 8.4 days). The APM task was also performed six months post-stroke (191.2 ± 18.0 days). Robotics allow more precise measurements of proprioception than clinical assessments. Consequently, an overall APM Task Score was used as ground truth to classify proprioceptive impairments at six months post-stroke. Other APM performance parameters from the two-week assessment were used as predictive features. Clinical assessments included the Thumb Localisation Test (TLT), Behavioural Inattention Test (BIT), Functional Independence Measure (FIM) and demographic information (age, sex and affected arm). Logistic regression classifiers were trained to predict proprioceptive impairments at six months post-stroke using data collected two weeks post-stroke. Models containing robotic features, either alone or in conjunction with clinical and neuroimaging features, had a greater area under the curve (AUC) and lower Akaike Information Criterion (AIC) than models which only contained clinical or neuroimaging features. All models performed similarly with regard to accuracy and F1-score (>70% accuracy). Robotic features were also among the most important when all features were combined into a single model. Predicting long-term proprioceptive impairments, using data collected as early as two weeks post-stroke, is feasible. Identifying those at risk of long-term impairments is an important step towards improving proprioceptive rehabilitation after a stroke.

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.

Regression-based prognostic models for functional independence after postacute brain injury rehabilitation are not transportable: a systematic review

BACKGROUND AND OBJECTIVES

To identify and summarize validated multivariable prognostic models for the Functional Independence Measure® (FIM®) at discharge from post-acute inpatient rehabilitation in adults with acquired brain injury (ABI).

METHODS

This review was conducted based on the recommendations of the Cochrane Prognosis Methods Group and adheres to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Three databases were systematically searched in May 2021 and updated in April 2022. Main inclusion criteria were: a) adult patients with ABI, b) validated multivariable prognostic model, c) time of prognostication within 1-week of admission to post-acute rehabilitation, and d) outcome was the FIM® at discharge from post-acute rehabilitation.

RESULTS

The search yielded 3,169 unique articles. Three articles fulfilled the inclusion criteria, accounting for n = 6 internally and n = 2 externally validated prognostic models. Discrimination was estimated as an area under the curve between 0.76 and 0.89. Calibration was deemed to be assessed insufficiently. The included models were judged to be of high risk of bias.

CONCLUSION

Current prognostic models for the FIM® in post-acute rehabilitation for patients with ABI lack the methodological rigor to support clinical use outside the development setting. Future studies addressing functional independence should ensure appropriate model validation and conform to uniform reporting standards for prognosis research.

Barriers to patient recruitment in a poststroke neurorehabilitation multicenter trial in Brazil

There is a high demand for stroke rehabilitation in the Brazilian public health system, but most studies that have addressed rehabilitation for unilateral spatial neglect (USN) after stroke have been performed in high-income countries. Therefore, the aim of this study was to analyze USN patient recruitment in a multicenter noninvasive brain stimulation clinical trial performed in Brazil and to provide study design recommendations for future studies. We evaluated the reasons for exclusion of patients from a multicenter, randomized, double-blinded clinical trial of rehabilitation of USN patients after stroke. Clinical and demographic variables were compared between the included and excluded patients. A descriptive statistical analysis was performed. Only 173 of the 1953 potential neglect patients (8.8%) passed the initial screening. After screening evaluation, 87/173 patients (50.3%) were excluded for clinical reasons. Cognitive impairment led to the exclusion of 21/87 patients (24.1%). Low socioeconomic status led to the exclusion of 37/173 patients (21.4%). Difficulty obtaining transportation to access treatment was the most common reason for their exclusion (16/37 patients, 43.3%). The analyzed Brazilian institutions have potential for conducting studies of USN. The recruitment of stroke survivors with USN was restricted by the study design and limited financial support. A history of cognitive impairment, intracranial stenting or craniectomy, and lack of transportation were the most common barriers to participating in a multicenter noninvasive brain stimulation trial among patients with USN after stroke.

Discharge Rehabilitation Measures Predict Activity Limitations in Patients With Stroke 6 Months After Inpatient Rehabilitation

OBJECTIVE

The aim of this study was to identify rehabilitation measures at discharge from acute inpatient stroke rehabilitation that predict activity limitations at 6 mos postdischarge.

DESIGN

This is a retrospective analysis of a prospective, longitudinal, observational cohort study. It was conducted in an acute inpatient rehabilitation unit at an urban, academic medical center. Activity limitations in patients ( N = 141) with stroke of mild-moderate severity were assessed with the activity measure for post-acute care at inpatient stroke rehabilitation discharge and 6-mo follow-up. Rehabilitation measures at discharge were investigated as predictors for activity limitations at 6 mos.

RESULTS

Measures of balance (Berg Balance Scale), functional limitations in motor-based activities (functional independence measure-motor subscore), and motor impairment (motricity index), in addition to discharge activities measure for post-acute care scores, strongly predicted activity limitations in basic mobility and daily activities at 6 mos (51% and 41% variance explained, respectively). Functional limitations in cognition (functional independence measure-cognitive subscore) and executive function impairment (Trail Making Test-part B), in addition to the discharge activities measure for post-acute care score, modestly predicted limitations in cognitively based daily activities at 6 mos (12% of variance).

CONCLUSIONS

Standardized rehabilitation measures at inpatient stroke rehabilitation discharge can predict future activity limitations, which may improve prediction of outcome post-stroke and aid in postdischarge treatment planning.

Long-term trajectories of motor functional independence after ischemic stroke in young adults: Identification and characterization using inpatient baseline assessments

BACKGROUND: Stroke is a major worldwide cause of serious long-term disability. Most previous studies addressing functional independence included only inpatients with limited follow-up. OBJECTIVE: To identify novel classes of patients having similar temporal patterns in motor functional independence and relate them to baseline clinical features. METHODS: Retrospective observational cohort study, data were obtained for n = 428 adult patients with ischemic stroke admitted to rehabilitation (March 2005–March 2020), including baseline clinical features and follow-ups of motor Functional Independence Measure (mFIM) categorized as poor, fair or good. Growth mixture models (GMMs) were fitted to identify classes of patients with similar mFIM trajectories. RESULTS: GMM identified three classes of trajectories (1,664 mFIM assessments): C1 (11.2 %), 97.9% having poor admission mFIM, at 4.93 years 61.1% still poor, with the largest percentage of hypertension, neglect, dysphagia, diabetes and dyslipidemia of all three classes. C2 (23.1%), 99% had poor admission mFIM, 25% poor discharge mFIM, the largest percentage of aphasia and greatest mFIM gain, at 4.93 years only 6.2% still poor. C3 (65.7%) the youngest, lowest NIHSS, 37.7% poor admission mFIM, 73% good discharge mFIM, only 4.6% poor discharge mFIM, 90% good at 4.93 years. CONCLUSIONS: GMM identified novel motor functional classes characterized by baseline features.

Machine learning methods for functional recovery prediction and prognosis in post-stroke rehabilitation: a systematic review

BACKGROUND

Rehabilitation medicine is facing a new development phase thanks to a recent wave of rigorous clinical trials aimed at improving the scientific evidence of protocols. This phenomenon, combined with new trends in personalised medical therapies, is expected to change clinical practice dramatically. The emerging field of Rehabilomics is only possible if methodologies are based on biomedical data collection and analysis. In this framework, the objective of this work is to develop a systematic review of machine learning algorithms as solutions to predict motor functional recovery of post-stroke patients after treatment.

METHODS

We conducted a comprehensive search of five electronic databases using the Patient, Intervention, Comparison and Outcome (PICO) format. We extracted health conditions, population characteristics, outcome assessed, the method for feature extraction and selection, the algorithm used, and the validation approach. The methodological quality of included studies was assessed using the prediction model risk of bias assessment tool (PROBAST). A qualitative description of the characteristics of the included studies as well as a narrative data synthesis was performed.

RESULTS

A total of 19 primary studies were included. The predictors most frequently used belonged to the areas of demographic characteristics and stroke assessment through clinical examination. Regarding the methods, linear and logistic regressions were the most frequently used and cross-validation was the preferred validation approach.

CONCLUSIONS

We identified several methodological limitations: small sample sizes, a limited number of external validation approaches, and high heterogeneity among input and output variables. Although these elements prevented a quantitative comparison across models, we defined the most frequently used models given a specific outcome, providing useful indications for the application of more complex machine learning algorithms in rehabilitation medicine.

The TWIST Tool Predicts When Patients Will Recover Independent Walking After Stroke: An Observational Study

BACKGROUND

The likelihood of regaining independent walking after stroke influences rehabilitation and hospital discharge planning.

OBJECTIVE

This study aimed to develop and internally validate a tool to predict whether and when a patient will walk independently in the first 6 months post-stroke.

METHODS

Adults with stroke were recruited if they had new lower limb weakness and were unable to walk independently. Clinical assessments were completed one week post-stroke. The primary outcome was time post-stroke by which independent walking (Functional Ambulation Category score ≥ 4) was achieved. Cox hazard regression identified predictors for achieving independent walking by 4, 6, 9, 16, or 26 weeks post-stroke. The cut-off and weighting for each predictor was determined using β-coefficients. Predictors were assigned a score and summed for a final TWIST score. The probability of achieving independent walking at each time point for each TWIST score was calculated.

RESULTS

We included 93 participants (36 women, median age 71 years). Age < 80 years, knee extension strength Medical Research Council grade ≥ 3/5, and Berg Balance Test < 6, 6 to 15, or ≥ 16/56, predicted independent walking and were combined to form the TWIST prediction tool. The TWIST prediction tool was at least 83% accurate for all time points.

CONCLUSIONS

The TWIST tool combines routine bedside tests at one week post-stroke to accurately predict the probability of an individual patient achieving independent walking by 4, 6, 9, 16, or 26 weeks post-stroke. If externally validated, the TWIST prediction tool may benefit patients and clinicians by informing rehabilitation decisions and discharge planning.

Ludic Table: a comparative study between playful rehabilitation and kinesiotherapy in restricting upper limb movements in individuals with stroke

Stroke is a neurological syndrome resulting from the sudden interruption of blood flow. Among the symptoms/consequences of the stroke are muscle weakness in the lower and/or upper limbs, decreased sensitivity, altered fine motor skills, proprioception, and reflections. The treatment for the motor consequences is orthopedic management, in which the physiotherapist assists the individual in repetitive range of motion exercises, which can be demotivating during the treatment. The Ludic Table (LT), on the other hand, incorporates playfulness into therapy, making it a motivating tool. This research describes the comparative study between kinesiotherapy techniques and exercises using the LT, applied to the development of upper limb movements. For this, fourteen volunteers were divided into groups, submitted to interventions according to the techniques, and evaluated using systems such as goniometry, HAQ-DI, GMFM-88, and neurofunctional assessment. In general, it can be stated that regardless of the intervention, the individuals obtained gain in movements (minimum average of 7 degrees) and that the use of the LT allows the development of the angular amplitude and the reduction of the effects of spasticity. The individuals submitted to the intervention through the LT obtained the development of a greater number of articular movements of the shoulder and elbow.

Validity of the Controlling Nutritional Status score as a Nutritional Assessment Tool early after stroke

Malnutrition is relatively common in stroke survivors and it also affects weight loss and muscle strength. Various nutritional assessment tools have been used to monitor changes in nutritional status. Among such tools, the Controlling Nutritional Status (CONUT) score is a convenient and cost-effective index calculated from serum albumin level, total peripheral lymphocyte count, and total cholesterol level. This study investigated the prognostic role of malnutrition, as assessed by the CONUT scoring system. We hypothesized that malnutrition negatively affects outcomes as expressed by Functional Independence Measure (FIM) motor or Berg Balance Scale (BBS) change in stroke patients. This was a retrospective cohort study involving 117 individuals including first-time subacute stroke inpatients from March 2017 to February 2020. All participants were evaluated with BBS and FIM. We used multiple linear regression analysis with backward stepwise selection to examine the association between CONUT and changes during rehabilitation. After adjusting for independent predictors, we found the CONUT score to be associated with FIM motor (B = -1.848 ± 5.811, P < 0.001) and BBS change (B = -2.035 ± 0.424, P < 0.001). The present study showed that the malnutritional status calculated by the CONUT score at admission might help to predict the functional outcomes of stroke patients. The CONUT score is a comprehensive and feasible marker that could provide information for the nutritional management of stroke patients to significantly improve their clinical outcomes.

The impact of COVID-19 on home, social, and productivity integration of people with chronic traumatic brain injury or stroke living in the community

Compare community integration of people with stroke or traumatic brain injury (TBI) living in the community before and during the coronavirus severe acute respiratory syndrome coronavirus 2 disease (COVID-19) when stratifying by injury: participants with stroke (G1) and with TBI (G2); by functional independence in activities of daily living: independent (G3) and dependent (G4); by age: participants younger than 54 (G5) and older than 54 (G6); and by gender: female (G7) and male (G8) participants.Prospective observational cohort studyIn-person follow-up visits (before COVID-19 outbreak) to a rehabilitation hospital in Spain and on-line during COVID-19.Community dwelling adults (≥18 years) with chronic stroke or TBI.Community integration questionnaire (CIQ) the total-CIQ as well as the subscale domains (ie, home-CIQ, social-CIQ, productivity CIQ) were compared before and during COVID-19 using the Wilcoxon ranked test or paired t test when appropriate reporting Cohen effect sizes (d). The functional independence measure was used to assess functional independence in activities of daily living.Two hundred four participants, 51.4% with stroke and 48.6% with TBI assessed on-line between June 2020 and April 2021 were compared to their own in-person assessments performed before COVID-19.When analyzing total-CIQ, G1 (d = -0.231), G2 (d = -0.240), G3 (d = -0.285), G5 (d = -0.276), G6 (d = -0.199), G7 (d = -0.245), and G8 (d = -0.210) significantly decreased their scores during COVID-19, meanwhile G4 was the only group with no significant differences before and during COVID-19.In productivity-CIQ, G1 (d = -0.197), G4 (d = -0.215), G6 (d = -0.300), and G8 (d = -0.210) significantly increased their scores, meanwhile no significant differences were observed in G2, G3, G5, and G7.In social-CIQ, all groups significantly decreased their scores: G1 (d = -0.348), G2 (d = -0.372), G3 (d = -0.437), G4 (d = -0.253), G5 (d = -0.394), G6 (d = -0.319), G7 (d = -0.355), and G8 (d = -0.365).In home-CIQ only G6 (d = -0.229) significantly decreased, no significant differences were observed in any of the other groups.The largest effect sizes were observed in total-CIQ for G3, in productivity-CIQ for G6, in social-CIQ for G3 and in home-CIQ for G6 (medium effect sizes).Stratifying participants by injury, functionality, age or gender allowed identifying specific CIQ subtotals where remote support may be provided addressing them.

Prediction of robotic neurorehabilitation functional ambulatory outcome in patients with neurological disorders

Introduction

Conflicting results persist regarding the effectiveness of robotic-assisted gait training (RAGT) for functional gait recovery in post-stroke survivors. We used several machine learning algorithms to construct prediction models for the functional outcomes of robotic neurorehabilitation in adult patients.

Methods and materials

Data of 139 patients who underwent Lokomat training at Taipei Medical University Hospital were retrospectively collected. After screening for data completeness, records of 91 adult patients with acute or chronic neurological disorders were included in this study. Patient characteristics and quantitative data from Lokomat were incorporated as features to construct prediction models to explore early responses and factors associated with patient recovery.

Results

Experimental results using the random forest algorithm achieved the best area under the receiver operating characteristic curve of 0.9813 with data extracted from all sessions. Body weight (BW) support played a key role in influencing the progress of functional ambulation categories. The analysis identified negative correlations of BW support, guidance force, and days required to complete 12 Lokomat sessions with the occurrence of progress, while a positive correlation was observed with regard to speed.

Conclusions

We developed a predictive model for ambulatory outcomes based on patient characteristics and quantitative data on impairment reduction with early-stage robotic neurorehabilitation. RAGT is a customized approach for patients with different conditions to regain walking ability. To obtain a more-precise and clearer predictive model, collecting more RAGT training parameters and analyzing them for each individual disorder is a possible approach to help clinicians achieve a better understanding of the most efficient RAGT parameters for different patients.

Trial registration: Retrospectively registered.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12984-021-00965-6.

Precision medicine in stroke: towards personalized outcome predictions using artificial intelligence

Stroke ranks among the leading causes for morbidity and mortality worldwide. New and continuously improving treatment options such as thrombolysis and thrombectomy have revolutionized acute stroke treatment in recent years. Following modern rhythms, the next revolution might well be the strategic use of the steadily increasing amounts of patient-related data for generating models enabling individualized outcome predictions. Milestones have already been achieved in several health care domains, as big data and artificial intelligence have entered everyday life.

The aim of this review is to synoptically illustrate and discuss how artificial intelligence approaches may help to compute single-patient predictions in stroke outcome research in the acute, subacute and chronic stage. We will present approaches considering demographic, clinical and electrophysiological data, as well as data originating from various imaging modalities and combinations thereof. We will outline their advantages, disadvantages, their potential pitfalls and the promises they hold with a special focus on a clinical audience. Throughout the review we will highlight methodological aspects of novel machine-learning approaches as they are particularly crucial to realize precision medicine. We will finally provide an outlook on how artificial intelligence approaches might contribute to enhancing favourable outcomes after stroke.

Young individuals with stroke in rehabilitation: a cohort study

Stroke in younger populations is a public health crisis and the prevalence is rising. Little is known about the progress of younger individuals with stroke in rehabilitation. Characterization of the course and speed of recovery is needed so that rehabilitation professionals can set goals and make decisions. This was a cohort study with data extracted from electronic medical records. Participants were 408 individuals diagnosed with stroke who participated in inpatient rehabilitation in an urban, academic medical center in the USA. The main predictor was age which was categorized as (18-44, 45-64, 65-74 and 75+). Outcomes included baseline-adjusted discharge functional independence measure (FIM) scores and FIM efficiency. In linear regression models for FIM scores, the reference category was the youngest age group. The oldest group was discharged with significantly lower FIM total (B = -8.84), mobility (B = -4.13), self-care (B = -4.07) and cognitive (B = -1.57) scores than the youngest group after controlling for covariates. The 45-64 group also finished with significantly lower FIM total (B = -6.17), mobility (B = -2.61) and self-care (B = -3.01) scores than youngest group. FIM efficiencies were similar for all ages in each of the FIM scales. Younger individuals with stroke make slightly greater functional gains compared to older individuals with stroke, but other factors, such as admission scores, are more important and the rates of recovery may be similar.

Predictive models for independence after stroke rehabilitation: Maugeri external validation and development of a new model

BACKGROUND

Many efforts have been devoted to identify predictors of functional outcomes after stroke rehabilitation. Though extensively recommended, there are very few external validation studies.

OBJECTIVE

To externally validate two predictive models (Maugeri model 1 and model 2) and to develop a new model (model 3) that estimate the probability of achieving improvement in physical functioning (primary outcome) and a level of independence requiring no more than supervision (secondary outcome) after stroke rehabilitation.

METHODS

We used multivariable logistic regression analysis for validation and development. Main outcome measures were: Functional Independence Measure (FIM) (primary outcome), Functional Independence Staging (FIS) (secondary outcome) and Minimal Clinically Important Difference (MCID).

RESULTS

Patients with stroke admitted to a rehabilitation center from 2006 to 2019 were retrospectively studied (N = 710). Validation of Maugeri models confirmed very good discrimination: for model 1 AUC = 0.873 (0.833-0.915) and model 2 AUC = 0.803 (0.749-0.857). The Hosmer-Lemeshow χ2 was 6.07(p = 0.63) and 8.91(p = 0.34) respectively. Model 3 yielded an AUC = 0.894 (0.857-0.929) (primary outcome) and an AUC = 0.769 (0.714-0.825) (MCID).

CONCLUSIONS

Discriminative power of both Maugeri models was externally confirmed (in a 20 years younger population) and a new model (incorporating aphasia) was developed outperforming Maugeri models in primary outcome and MCID.

Sex differences in independence in activities of daily living early in stroke rehabilitation

BACKGROUND

Current stroke research suggests that there are differences between females and males regarding incidence, stroke risk factors, stroke severity, outcome, and mortality. The few studies that have investigated sex differences in rehabilitation 8-12 months poststroke found that males are more independent, compared to females.

OBJECTIVES

To investigate if there is a difference in the improvement of independence in activities of daily living (ADL) between females and males in the acute phase (first 2 weeks) of stroke rehabilitation in a Danish population.

METHODS

A prospective cohort study enrolling patients admitted to the hospital's rehabilitation ward with a stroke diagnosis from January 1, 2016, to March 17, 2017. Baseline and follow-up data regarding the primary outcome, Barthel-100 index, were analyzed using an adjusted linear mixed model.

RESULTS

The study included 206 patients (83 females). Females were older at admission and more males lived with a partner. No differences in stroke severity or any of the risk factors were found. There were no differences between female and male scores at baseline. In the adjusted linear mixed model, quantifying the difference between follow-up and baseline Barthel-100 score, females increased their Barthel-100 score by 20.8 points (95% confidence interval (CI) 15.4-26.3) and males with 29.0 points (95% CI 24.6-33.4).

CONCLUSION

In a homogeneous sample of stroke survivors undergoing specialized 24-h stroke rehabilitation for 11-14 days, females were more dependent in ADL than males.

Relationship Between Motor Function, DTI, and Neurophysiological Parameters in Patients with Stroke in the Recovery Rehabilitation unit

OBJECTIVES

We investigated the relationship between pyramidal tract evaluation indexes (i.e., diffusion tensor imaging, transcranial magnetic stimulation (TMS)-induced motor-evoked potential (MEP), and central motor conduction time (CMCT) on admission to the recovery rehabilitation unit) and motor functions at discharge in patients with ischemic or hemorrhagic stroke.

MATERIALS AND METHODS

Seventeen patients were recruited (12 men; 57.9 ± 10.3 years). The mean fractional anisotropy (FA) values of the right and left posterior limbs of the internal capsule were estimated using a computer-automated method. We determined the ratios of FA values in the affected and unaffected hemispheres (rFA), TMS-induced MEP, and the ratios of CMCT in the affected and unaffected hemispheres (rCMCT) and examined their association with motor functions (Fugl-Meyer Assessment (FMA) and Action Research Arm Test (ARAT)) at discharge.

RESULTS

Higher rFA values of the posterior limb of the internal capsule on admission to the recovery rehabilitation unit led to a better recovery of upper limb function (FMA: r = 0.78, p < 0.001; ARAT: r = 0.74, p = 0.001). Patients without MEP had poorer recovery of upper limb function than those with MEP (FMA: p < 0.001; ARAT: p = 0.001). The higher the rCMCT, the poorer the recovery of upper limb function (ARAT: r = -0.93, p < 0.001). However, no association was observed between the pyramidal tract evaluation indexes and recovery of lower limb motor function.

CONCLUSIONS

Evaluating the pyramidal tract is useful for predicting upper limb function prognosis, but not for lower limb function prognosis.

Predictors of Function, Activity, and Participation of Stroke Patients Undergoing Intensive Rehabilitation: A Multicenter Prospective Observational Study Protocol

Background: The complex nature of stroke sequelae, the heterogeneity in rehabilitation pathways, and the lack of validated prediction models of rehabilitation outcomes challenge stroke rehabilitation quality assessment and clinical research. An integrated care pathway (ICP), defining a reproducible rehabilitation assessment and process, may provide a structured frame within investigated outcomes and individual predictors of response to treatment, including neurophysiological and neurogenetic biomarkers. Predictors may differ for different interventions, suggesting clues to personalize and optimize rehabilitation. To date, a large representative Italian cohort study focusing on individual variability of response to an evidence-based ICP is lacking, and predictors of individual response to rehabilitation are largely unexplored. This paper describes a multicenter study protocol to prospectively investigate outcomes and predictors of response to an evidence-based ICP in a large Italian cohort of stroke survivors undergoing post-acute inpatient rehabilitation.

Methods: All patients with diagnosis of ischemic or hemorrhagic stroke confirmed both by clinical and brain imaging evaluation, admitted to four intensive rehabilitation units (adopting the same stroke rehabilitation ICP) within 30 days from the acute event, aged 18+, and providing informed consent will be enrolled (expected sample: 270 patients). Measures will be taken at admission (T0), at discharge (T1), and at follow-up 6 months after a stroke (T2), including clinical data, nutritional, functional, neurological, and neuropsychological measures, electroencephalography and motor evoked potentials, and analysis of neurogenetic biomarkers.

Statistics: In addition to classical multivariate logistic regression analysis, advanced machine learning algorithms will be cross-validated to achieve data-driven prognosis prediction models.

Discussion: By identifying data-driven prognosis prediction models in stroke rehabilitation, this study might contribute to the development of patient-oriented therapy and to optimize rehabilitation outcomes.

Clinical Trial Registration:ClinicalTrials.gov, NCT03968627. https://www.clinicaltrials.gov/ct2/show/NCT03968627?term=Cecchi&cond=Stroke&draw=2&rank=2.

Novel lateral transfer assist robot decreases the difficulty of transfer in post-stroke hemiparesis patients: a pilot study

PURPOSE

The purpose of this study was to clarify whether the novel lateral transfer assist robot facilitates easier transfers compared with a wheelchair in post-stroke hemiparesis patients.

METHODS

This cross-sectional study enrolled 20 post-stroke hemiparesis patients, and the task difficulty of transfers was compared between a wheelchair and lateral transfer assist robot. All participants were asked to transfer from either wheelchair or lateral transfer assist robot to a platform table and back. The primary outcome was the transfer score of the Functional Independence Measure. The secondary outcome was the time required for transfer.

RESULTS

The transfer score of the Functional Independence Measure was significantly higher with lateral transfer assist robot than with wheelchair (p < .001). The transfer times from these devices to a platform table and back showed no significant differences (to device from platform table: 7.8 s, lateral transfer assist robot vs 7.6 s, wheelchair, p > .05: device to platform table: 7.1 s, lateral transfer assist robot vs 8.0 s, wheelchair, p > .05).

CONCLUSIONS

Transfer with a lateral transfer assist robot is easier than with wheelchair and facilitates independence in post-stroke hemiparesis patients.IMPLICATIONS FOR REHABILITATIONTransfer skill influences the functional independence and quality of life of a wheelchair userA novel structural mobility device-the lateral transfer assist robot (LTAR)-can facilitate transfersThe LTAR could improve the degree of independence for transfers than the wheelchair, without any time loss, in post-stroke hemiparesis patientsThe LTAR could potentially reduce the risk for falls in various medical and care facilities.

Inpatient stroke rehabilitation: prediction of clinical outcomes using a machine-learning approach

BACKGROUND

In clinical practice, therapists often rely on clinical outcome measures to quantify a patient's impairment and function. Predicting a patient's discharge outcome using baseline clinical information may help clinicians design more targeted treatment strategies and better anticipate the patient's assistive needs and discharge care plan. The objective of this study was to develop predictive models for four standardized clinical outcome measures (Functional Independence Measure, Ten-Meter Walk Test, Six-Minute Walk Test, Berg Balance Scale) during inpatient rehabilitation.

METHODS

Fifty stroke survivors admitted to a United States inpatient rehabilitation hospital participated in this study. Predictors chosen for the clinical discharge scores included demographics, stroke characteristics, and scores of clinical tests at admission. We used the Pearson product-moment and Spearman's rank correlation coefficients to calculate correlations among clinical outcome measures and predictors, a cross-validated Lasso regression to develop predictive equations for discharge scores of each clinical outcome measure, and a Random Forest based permutation analysis to compare the relative importance of the predictors.

RESULTS

The predictive equations explained 70-77% of the variance in discharge scores and resulted in a normalized error of 13-15% for predicting the outcomes of new patients. The most important predictors were clinical test scores at admission. Additional variables that affected the discharge score of at least one clinical outcome were time from stroke onset to rehabilitation admission, age, sex, body mass index, race, and diagnosis of dysphasia or speech impairment.

CONCLUSIONS

The models presented in this study could help clinicians and researchers to predict the discharge scores of clinical outcomes for individuals enrolled in an inpatient stroke rehabilitation program that adheres to U.S. Medicare standards.

Outcome in Stroke Patients Is Associated with Age and Fractional Anisotropy in the Cerebral Peduncles: A Multivariate Regression Study

Objectives:

Diffusion tensor fractional anisotropy (FA) in the corticospinal tracts has been used to assess the long-term outcome in stroke patients. Patient age and the type of stroke may also affect outcomes. In this study, we investigated the associations of age, type of stroke, and FA in the ipsilesional and contralesional cerebral peduncles with stroke outcomes.

Methods:

This study involved 80 patients with stroke (40 hemorrhagic, 40 ischemic) that we had investigated previously. Diffusion tensor FA images were obtained between 14 and 21 days post-stroke. FA values in the ipsilesional and contralesional cerebral peduncles were extracted and their ratio (rFA) was calculated. Outcome was assessed using the Brunnstrom stage, the motor component of the Functional Independence Measure (FIM-motor) at discharge, and the length of stay until discharge from rehabilitation. Using forward stepwise multivariate regression, we assessed the associations of rFA, contralesional FA, age, and type of stroke with outcome measures.

Results:

rFA and contralesional FA were included in the final model for the Brunnstrom stage in the upper limbs. There was a strong association between hemorrhagic stroke and poorer lower extremity function. rFA, contralesional FA, and age were included in the final model for FIM-motor and length of stay. The effect of rFA on all outcome measures was stronger than that of contralesional FA. The effect of age on FIM-motor was as strong as that of rFA.

Conclusions:

Neural damage in the corticospinal tracts (indicated by rFA) had the strongest effect on outcome measures, whereas the level of disability (measured by FIM-motor) was associated with a broader range of factors, including age.

BDNF Met allele Is Associated With Lower Cognitive Function in Poststroke Rehabilitation

Background and purpose. The identification of a genetic role for cognitive outcome could influence the design of individualized treatment in poststroke rehabilitation. The aim of this study is to determine whether brain-derived neurotrophic factor ( BDNF) Val66Met polymorphism is independently associated with poststroke functional outcome. Methods. A total of 775 stroke patients with genomic data were identified from the Partners HealthCare Biobank, which contains a large number of genotypes from Biobank’s consented patients. Of 775 stroke patients who met the inclusion/exclusion criteria, 86 were enrolled. Functional outcomes were assessed using the Functional Independence Measure scores at the time of admission and discharge. Logistic and linear regression models adjusted for covariate variables, including age, sex, and medical conditions, were used to evaluate the association between BDNF Val66Met and functional outcome. Results. We detected a significant correlation between Met alleles and lower cognitive function at discharge in both ischemic and hemorrhagic stroke patients. Genotyping findings confirmed that BDNF Met allele frequency was higher in contrast to Val/Val allele frequency in lower cognitive functional recovery. Furthermore, after adjusting for covariate variables, BDNF Met alleles were found to be associated with lower cognitive outcome [ P = .003; odds ratio (OR) = 5.95 (1.81-19.52)] and recovery [ P = .006; OR = 3.16 (1.4-7.15)], especially with lower problem solving, expression, and social recovery in all stroke patients. Conclusions. Met allele carriers exhibited impaired poststroke cognitive function. The BDNF genotype may be a useful predictor of cognitive function in inpatient poststroke rehabilitation.

Cognition and Daily Functioning: Results from the Hispanic Community Health Study/Study of Latinos (SOL) and Study of Latinos-Investigation of Neurocognitive Aging (SOL-INCA)

BACKGROUND

Among older adults, poorer cognitive functioning has been associated with impairments in instrumental activities of daily living (IADLs). However, IADL impairments among older Hispanics/Latinos is poorly understood.

OBJECTIVE

To characterize the relationships between cognition and risk for IADL impairment among diverse Hispanics/Latinos.

METHODS

Participants included 6,292 community-dwelling adults from the Study of Latinos - Investigation of Neurocognitive Aging, an ancillary study of 45+ year-olds in the Hispanic Community Health Study/Study of Latinos. Cognitive data (learning, memory, executive functioning, processing speed, and a Global cognitive composite) were collected at Visit 1. IADL functioning was self-reported 7 years later, and treated as a categorical (i.e., risk) and continuous (i.e., degree) measures of impairment. Survey two-part models (mixture of logit and generalized linear model with Gaussian distribution) and ordered logistic regression tested the associations of cognitive performance (individual tests and composite z-score) with IADL impairment. Additionally, we investigated the moderating role of age, sex, and Hispanic/Latino background on the association between cognition and IADL impairment.

RESULTS

Across all cognitive measures, poorer performance was associated with higher odds of IADL impairment 7 years later. Associations were generally stronger for the oldest group (70+ years) relative to the youngest group (50-59 years). Sex and Hispanic/Latino background did not modify the associations. Across the full sample, lower scores on learning, memory, and the Global cognitive composite were also associated with higher degree of IADL impairment.

CONCLUSION

Across diverse Hispanics/Latinos, cognitive health is an important predictor of everyday functioning 7 years later, especially in older adulthood.

Patients with neuropsychological disorders short after stroke have worse functional outcome: a systematic review and meta-analysis

PURPOSE

To investigate if patients with neuropsychological disorders (neglect, aphasia, or cognitive dysfunction measured with the Mini-mental state examination) short after stroke have different functional outcome at follow-up compared to patients without these disorders.

METHODS

Embase, Medline-Ovid, PsycINFO, Cochrane CENTRAL, Web of Science and Google Scholar were systematically searched for cohort studies up to 3 March 2019. PRISMA guidelines were followed. Functional outcome had to be measured with the Barthel Index or the Functional Independence Measure. If at least three studies studying the same neuropsychological disorder reported functional outcome at comparable follow-up, meta-analysis were performed and the quality of evidence was assessed using GRADE.

RESULTS

The search resulted in 5398 unique articles and finally 27 articles were included. Pooled results show a standardized mean difference of -0.93 (95% confidence interval [-1.27 to -0.59]), indicating that the group with neglect short after stroke has significant lower functional outcome at follow-up. Regarding aphasia, the standardized mean difference was -0.50 (95% confidence interval [-0.72 to -0.28]). It appears in the limited articles available that patients with cognitive dysfunction have lower scores for functional outcome.

CONCLUSIONS

Patients with neglect or aphasia, especially aphasia with comprehension deficits, short after stroke have significant worse functional outcome.Implications for rehabilitationClinicians should perform an extensive screening for neglect, aphasia, and cognitive disorders to make sure to diagnose the different neuropsychological disorders correctly.When patients with neuropsychological disorders are referred for rehabilitation, it can be expected that they need a longer rehabilitation period or may never reach the same level of functioning.Clinicians should pay attention to instructions of training moments outside therapy and involve caregivers and patients family making these training moments more effective.

Functional limitations and cognitive impairment predict the outcome of dysphagia in older patients after an acute neurologic event

BACKGROUND

Dysphagia prevalence increases with age and a significant contribution is given by stroke survivors; its treatment is mainly based on rehabilitation, but outcome cannot be easily predicted.

OBJECTIVE

The aim of this study is to detect possible predictors of the outcome of dysphagia in patients beginning rehabilitation after a major Central Nervous System injury.

METHODS

Dysphagia severity was measured in 95 consecutive patients (71 with ischemic or hemorrhagic stroke) upon admission to our neurorehabilitation unit and at discharge, during the year 2017. The initial evaluation included also demographic data, functional and geriatric multidimensional assessment, laboratory test results and comorbidities. Their possible predictive value on the degree of recovery of the swallowing process at discharge has been analyzed.

RESULTS

Poor functional conditions and the presence of cognitive impairment on admission appear to be associated with a worse outcome of dysphagia at discharge. A significant correlation exists between scores at functional scales at the beginning of rehabilitation and dysphagia score at discharge. Patients with cognitive impairment at the beginning (n = 60) showed a significantly lower degree of recovery of dysphagia at discharge.

CONCLUSION

Other factors, beside the degree of dysphagia itself, are important to predict its outcome. Their knowledge not only allows an initial prognostic assessment; it can also be useful to decide which aspects should receive greater attention when treating patients with dysphagia.

The new frontiers of rehabilitation medicine in people with chronic disabling illnesses

Because of the demographic shift and the increased proportion of patients surviving acute critical illnesses, the number of people living with severely disabling chronic diseases and, consequently, the demand for rehabilitation are expected to increase sharply over time. As underscored by the World Health Organization, there is substantial evidence that the provision of inpatient rehabilitation in specialized rehabilitation units to people with complex needs is effective in fostering functional recovery, improving health-related quality of life, increasing independence, reducing institutionalization rate, and improving prognosis. Recent studies in the real world setting reinforce the evidence that patients with ischemic heart disease or stroke benefit from rehabilitation in terms of improved prognosis. In addition, there is evidence of the effectiveness of rehabilitation for the prevention of functional deterioration in patients with complex and/or severe chronic diseases. Given this evidence of effectiveness, rehabilitation should be regarded as an essential part of the continuum of care. Nonetheless, rehabilitation still is underdeveloped and underused. Efforts should be devoted to foster healthcare professional awareness of the benefits of rehabilitation and to increase referral and participation.

IL-6 Plasma Levels Correlate With Cerebral Perfusion Deficits and Infarct Sizes in Stroke Patients Without Associated Infections

Introduction: We aimed to investigate several blood-based biomarkers related to inflammation, immunity, and stress response in a cohort of patients without stroke-associated infections regarding their predictive abilities for functional outcome and explore whether they correlate with MRI markers, such as infarct size or location. Methods: We combined the clinical and radiological data of patients participating in two observational acute stroke cohorts: the PREDICT and 1000Plus studies. The following blood-based biomarkers were measured in these patients: monocytic HLA-DR, IL-6, IL-8, IL-10, LBP, MRproANP, MRproADM, CTproET, Copeptin, and PCT. Multiparametric stroke MRI was performed including T2^*, DWI, FLAIR, TOF-MRA, and perfusion imaging. Standard descriptive sum statistics were used to describe the sample. Associations were analyzed using Fischer's exact test, independent samples t-test and Spearmans correlation, where appropriate. Results: Demographics and stroke characteristics were as follows: 94 patients without infections, mean age 68 years (SD 10.5), 32.2% of subjects were female, median NIHSS score at admission 3 (IQR 2-5), median mRS 3 months after stroke 1 (IQR 0-2), mean volume of DWI lesion at admission 5.7 ml (SD 12.8), mean FLAIR final infarct volume 10 ml (SD 14.9), cortical affection in 61% of infarctions. Acute DWI lesion volume on admission MRI was moderately correlated to admission/maximum IL-6 as well as maximum LBP. Extent of perfusion deficit and mismatch were moderately correlated to admission/maximum IL-6 levels. Final lesion volume on FLAIR was moderately correlated to admission IL-6 levels. Conclusion: We found IL-6 to be associated with several parameters from acute stroke MRI (acute DWI lesion, perfusion deficit, final infarct size, and affection of cortex) in a cohort of patients not influenced by infections. Clinical Trial Registration: www.ClinicalTrials.gov, identifiers NCT01079728 and NCT00715533.

Rehabilitation Outcomes of Patients With Severe Disability Poststroke

OBJECTIVE

To characterize rehabilitation outcomes of patients with severe poststroke motor impairment (MI) and develop a predictive model for treatment failure.

DESIGN

Retrospective cohort study. Correlates of treatment failure, defined as the persistence of severe MI after rehabilitation, were identified using logistic regression analysis. Then, an integer-based scoring rule was developed from the logistic model.

SETTING

Three specialized inpatient rehabilitation facilities.

PARTICIPANTS

Patients (N=1265) classified as case-mix groups (CMGs) 0108, 0109, and 0110 of the Medicare classification system.

INTERVENTIONS

Not applicable.

MAIN OUTCOME MEASURE

Change in the severity of MI, as assessed by the FIM, from admission to discharge.

RESULTS

Median FIM-motor (FIM-M) score increased from 17 (interquartile range [IQR] 14-23) to 38 (IQR, 25-55) points. Median proportional recovery, as expressed by FIM-M effectiveness, was 26% (IQR, 12-47). Median FIM-M change was 18 (IQR, 9-34) points. About 38.5% patients achieved the minimal clinically important difference. Eighteen point six percent and 32.0% of the patients recovered to a stage of either mild (FIM-M ≥62) or moderate (FIM-M 38-61) MI, respectively. All between-CMG differences were statistically significant. Outcomes have also been analyzed according to classification systems used in Australia and Canada. The scoring rule had an area under the curve of 0.833 (95% confidence interval, 0.808-0.858). Decision curve analysis displayed large net benefit of using the risk score compared with the treat all strategy.

CONCLUSIONS

This study provides a snapshot of rehabilitation outcomes in a large cohort of patients with severe poststroke MI, thus filling a gap in knowledge. The scoring rule accurately identified the patients at risk for treatment failure.