Prediction Tools for Stroke Rehabilitation

What influences provision of information about recovery on stroke units? A focused ethnographic case study

OBJECTIVE

Patients and carers frequently report dissatisfaction with post-stroke information provision. This study aimed to develop an in-depth understanding of the factors influencing provision of information about recovery in stroke units.

METHODS

Focused ethnographic case-studies in two UK stroke units, including non-participant observations and semi-structured interviews with professionals, patients and carers, and documentary analysis. A Framework approach to analysis was undertaken.

RESULTS

Twenty patients, 17 carers and 47 professionals participated. The unpredictable recovery trajectory led professionals to present prognostic estimates as uncertain possibilities. The need to maintain patients' motivation limited sharing of negative predictions, and generic information over-emphasised the importance of therapy in recovery. A structured multidisciplinary team approach to delivering information improved consistency. Complex clinical reasoning was required to identify and meet patients' needs. Hospital environments and routines restricted opportunities for dialogue, particularly with carers.

CONCLUSIONS

The process of providing information about post-stroke recovery is complex, requiring enhanced clinical reasoning and communication. The challenges faced by professionals are numerous and if not addressed can result in suboptimal provision.

PRACTICE IMPLICATIONS

Professionals should develop a co-ordinated multidisciplinary approach to information provision; and engage in dialogue to ensure a tailored approach to identifying and meeting patients' and carers' information needs.

Determinants of access to rehabilitation professionals by individuals with stroke in the first six months after hospital discharge in Brazil: a study based on the Andersen model

BACKGROUND

determinants of access to rehabilitation professionals after stroke in middle-income countries, where the burden of this disease is higher, are little known.

OBJECTIVES

To identify the determinants of access to rehabilitation professionals by individuals with stroke at one, three, and six months after hospital discharge in Brazil and compare referral and access rates after discharge.

METHODS

Longitudinal and prospective study, with individuals with primary stroke, without previous disabilities. At hospital discharge, the number of rehabilitation professionals referred by the multidisciplinary team was recorded. The possible determinants of access, according to Andersen's model, were: a) predisposing factors: age, sex, education levels, and belief that they could improve with treatment; b) need factors: stroke severity, levels of disability; c) enabling factors: socioeconomic status, disposable income for health care, and quality of care provided by rehabilitation professionals. One, three, and six months after hospital discharge, individuals were contacted to identify which rehabilitation professionals were accessed. Multiple linear regression model and Wilcoxon tests were used (α=5%).

RESULTS

201 individuals were included. Disability levels and stroke severity explained 31%, 34%, and 39% (p<0.01) of access at one, three, and six months after hospital discharge, respectively. In all periods, there was less access than that recommended at the time of hospital discharge (p<0.01).

CONCLUSION

Need factors (disability levels and stroke severity) were determinants of access in all assessed periods. In addition, in all periods, the comprehensiveness of care for individuals with stroke was compromised.

Microstructural properties in subacute aphasia: concurrent and prospective relationships underpinning recovery

BACKGROUND

Few investigations examined the relationship between microstructural white matter integrity and subacute post-stroke linguistic performance or the relationship between microstructural integrity and the recovery of language function. We examined two key questions: (1) How does subacute language performance, measured in single words and discourse, relate to the microstructural integrity of key white matter regions of interest in the language network? and (2) Does the integrity of these regions before treatment predict the improvement or resolution of linguistic symptoms immediately and chronically following treatment?

METHODS

58 participants within the first three months of stroke were enrolled in a randomized, single-center, double-blind, sham-controlled, study of anodal transcranial direct current stimulation combined with a computer-delivered speech and language naming therapy for subacute aphasia and were asked to complete magnetic resonance imaging at enrollment. Microstructural integrity was evaluated using diffusion tensor imaging processed with atlas-based segmentation. Regression and correlation analyses were conducted.

RESULTS

A subset of 22 participants received diffusion tensor imaging. Picture naming accuracy significantly correlated with lower mean diffusivity (higher microstructural integrity) in the left posterior inferior temporal gyrus. Recovery of naming performance was predicted by days since stroke and baseline microstructural integrity of the left posterior middle temporal gyrus, arcuate fasciculus, and superior longitudinal fasciculus. Recovery of discourse efficiency was significantly predicted by the same model.

CONCLUSIONS

This study demonstrates an association between picture naming and discourse and microstructural integrity of the key regions in the language network for patients with subacute post-stroke aphasia. Baseline microstructural integrity significantly predicts language recovery.

Cutoff values of motor and cognitive measures for predicting and discriminating levels of activities of daily living after stroke: a scoping review

The various assessments performed by rehabilitation professionals not only indicate the patient's current functional status but can also help determine the future status (prediction) or the ability to perform untested tasks (discrimination). In particular, the cutoff values are the simplest predictive and discriminative tool that can be widely used in clinical practice. The purpose of this scoping review was to summarize the current literature on cutoff values of motor and cognitive function for predicting or discriminating levels of activities of daily living after stroke. A literature search was conducted using the PubMed, CINAHL, and Scopus databases. The creation of the search criteria, primary screening of titles and abstract, and secondary screening by full-text review were performed by two rehabilitation professionals. A total of 54 articles were included. The summary of the cutoff values for prediction based on longitudinal studies revealed that an NIHSS score ≤8 and mRMI score ≥19 at acute hospitalization can predict good functional independence and walking independence, respectively, indicating reliable cutoff values. Cutoff values for predicting specific ADLs, such as toilet use or dressing, were not reported, which was a potential research gap identified in this review. Alternatively, the summary of the cutoff values for discrimination based on cross-sectional studies revealed that 288-367.5 m on the 6-min walk test and 25.5-27.6 points on the FMA-LL can discriminate community and noncommunity walkers. Considering the difference between prediction and discrimination, the reliable predicted cutoff values revealed in this review are useful for planning an intervention based on early prediction. Conversely, cutoff values for discrimination can estimate different performances with simpler test, or use as target values during rehabilitation.

Stroke risk prediction models: A systematic review and meta-analysis

BACKGROUND

Prediction algorithms/models are viable methods for identifying individuals at high risk of stroke across diverse populations for timely intervention. However, evidence summarizing the performance of these models is limited. This study examined the performance and weaknesses of existing stroke risk-score-prediction models (SRSMs) and whether performance varied by population and region.

METHODS

PubMed, EMBASE, and Web of Science were searched for articles on SRSMs from the earliest records until February 2022. The Prediction Model Risk of Bias Assessment Tool was used to assess the quality of eligible articles. The performance of the SRSMs was assessed by meta-analyzing C-statistics (0 and 1) estimates from identified studies to determine the overall pooled C-statistics by fitting a linear restricted maximum likelihood in a random effect model.

RESULTS

Overall, 17 articles (cohort study = 15, nested case-control study = 2) comprising 739,134 stroke cases from 6,396,594 participants from diverse populations/regions (Asia; n = 8, United States; n = 3, and Europe and the United Kingdom; n = 6) were eligible for inclusion. The overall pooled c-statistics of SRSMs was 0.78 (95%CI: 0.75, 0.80; I2 = 99.9%), with most SRSMs developed using cohort studies; 0.78 (95%CI: 0.75, 0.80; I2 = 99.9%). The subgroup analyses by geographical region: Asia [0.81 (95%CI: 0.79, 0.83; I2 = 99.8%)], Europe and the United Kingdom [0.76 (95%CI: 0.69, 0.83; I2 = 99.9%)] and the United States only [0.75 (95%CI: 0.72, 0.78; I2 = 73.5%)] revealed relatively indifferent performances of SRSMs.

CONCLUSION

SRSM performance varied widely, and the pooled c-statistics of SRSMs suggested a fair predictive performance, with very few SRSMs validated in independent population group(s) from diverse world regions.

Biomarkers of Motor Outcomes After Stroke

Predicting motor outcomes after stroke based on clinical judgment alone is often inaccurate and can lead to inefficient and inequitable allocation of rehabilitation resources. Prediction tools are being developed so that clinicians can make evidence-based, accurate, and reproducible prognoses for individual patients. Biomarkers of corticospinal tract structure and function can improve prediction tool performance, particularly for patients with initially moderate to severe motor impairment. Being able to make accurate predictions for individual patients supports rehabilitation planning and communication with patients and families.

Deep learning on pre-procedural computed tomography and clinical data predicts outcome following stroke thrombectomy

BACKGROUND

Deep learning using clinical and imaging data may improve pre-treatment prognostication in ischemic stroke patients undergoing endovascular thrombectomy (EVT).

METHODS

Deep learning models were trained and tested on baseline clinical and imaging (CT head and CT angiography) data to predict 3-month functional outcomes in stroke patients who underwent EVT. Classical machine learning models (logistic regression and random forest classifiers) were constructed to compare their performance with the deep learning models. An external validation dataset was used to validate the models. The MR PREDICTS prognostic tool was tested on the external validation set, and its performance was compared with the deep learning and classical machine learning models.

RESULTS

A total of 975 patients (550 men; mean±SD age 67.5±15.1 years) were studied with 778 patients in the model development cohort and 197 in the external validation cohort. The deep learning model trained on baseline CT and clinical data, and the logistic regression model (clinical data alone) demonstrated the strongest discriminative abilities for 3-month functional outcome and were comparable (AUC 0.811 vs 0.817, Q=0.82). Both models exhibited superior prognostic performance than the other deep learning (CT head alone, CT head, and CT angiography) and MR PREDICTS models (all Q<0.05).

CONCLUSIONS

The discriminative performance of deep learning for predicting functional independence was comparable to logistic regression. Future studies should focus on whether incorporating procedural and post-procedural data significantly improves model performance.

Do lower limb motor-evoked potentials predict walking outcomes post-stroke?

BACKGROUND

This observational study examined whether lower limb (LL) motor-evoked potentials (MEPs) 1 week post-stroke predict recovery of independent walking, use of ankle-foot orthosis (AFO) or walking aid, at 3 and 6 months post-stroke.

METHODS

Non-ambulatory participants were recruited 5 days post-stroke. Transcranial magnetic stimulation was used to determine tibialis anterior MEP status and clinical assessments (age, National Institutes of Health Stroke Scale (NIHSS), ankle dorsiflexion strength, LL motricity index, Berg Balance Test) were completed 1 week post-stroke. Functional Ambulation Category (FAC), use of AFO and walking aid were assessed 3 months and 6 months post-stroke. MEP status, alone and combined with clinical measures, and walking outcomes at 3 and 6 months were analysed with Pearson χ2 and multivariate binary logistic regression.

RESULTS

Ninety participants were included (median age 72 years (38-97 years)). Most participants (81%) walked independently (FAC ≥ 4), 17% used an AFO, and 49% used a walking aid 3 months post-stroke with similar findings at 6 months. Independent walking was better predicted by age, LL strength and Berg Balance Test (accuracy 92%, 95% CI 85% to 97%) than MEP status (accuracy 73%, 95% CI 63% to 83%). AFO use was better predicted by NIHSS and MEP status (accuracy 88%, 95% CI 79% to 94%) than MEP status alone (accuracy 76%, 95% CI 65% to 84%). No variables predicted use of walking aids.

CONCLUSIONS

The presence of LL MEPs 1-week post-stroke predicts independent walking at 3 and 6 months post-stroke. However, the absence of MEPs does not preclude independent walking. Clinical factors, particularly age, balance and stroke severity, more strongly predict independent walking than MEP status. LL MEP status adds little value as a biomarker for walking outcomes.

NSF DARE-transforming modeling in neurorehabilitation: perspectives and opportunities from US funding agencies

In recognition of the importance and timeliness of computational models for accelerating progress in neurorehabilitation, the U.S. National Science Foundation (NSF) and the National Institutes of Health (NIH) sponsored a conference in March 2023 at the University of Southern California that drew global participation from engineers, scientists, clinicians, and trainees. This commentary highlights promising applications of computational models to understand neurorehabilitation ("Using computational models to understand complex mechanisms in neurorehabilitation" section), improve rehabilitation care in the context of digital twin frameworks ("Using computational models to improve delivery and implementation of rehabilitation care" section), and empower future interdisciplinary workforces to deliver higher-quality clinical care using computational models ("Using computational models in neurorehabilitation requires an interdisciplinary workforce" section). The authors describe near-term gaps and opportunities, all of which encourage interdisciplinary team science. Four major opportunities were identified including (1) deciphering the relationship between engineering figures of merit-a term commonly used by engineers to objectively quantify the performance of a device, system, method, or material relative to existing state of the art-and clinical outcome measures, (2) validating computational models from engineering and patient perspectives, (3) creating and curating datasets that are made publicly accessible, and (4) developing new transdisciplinary frameworks, theories, and models that incorporate the complexities of the nervous and musculoskeletal systems. This commentary summarizes U.S. funding opportunities by two Federal agencies that support computational research in neurorehabilitation. The NSF has funding programs that support high-risk/high-reward research proposals on computational methods in neurorehabilitation informed by theory- and data-driven approaches. The NIH supports the development of new interventions and therapies for a wide range of nervous system injuries and impairments informed by the field of computational modeling. The conference materials can be found at https://dare2023.usc.edu/ .

Association between galectin-3 and the prognosis of patients with stroke: A meta-analysis of observational studies

AIM

This meta-analysis aimed to investigate the prognostic value of galectin-3 among patients with stroke.

DATA SYNTHESIS

Electronic databases, such as PubMed, Web of Science, Embase, Cochrane, and Clinical Trials, were utilized for conducting searches from database inception to November 2022. Two reviewers independently screened the papers, extracted the data, and used the Newcastle-Ottawa Scale to determine the risk of bias. Stata 17 was employed to conduct the meta-analysis, while the funnel plot was utilized to identify potential publication bias. Subgroup and meta-regression analyses were employed to examine the sources of heterogeneity. Nine studies that satisfied the inclusion criteria were included. Galectin-3 levels were higher post-stroke in patients with poor outcomes and mortality compared to those in patients with good outcomes (standardized mean difference [SMD], 2.83; 95 % confidence interval [CI], 1.25-4.40; P<0.001) and stroke survivors (SMD, 1.18; 95 % CI, 0.96-1,39; P< 0.001), and elevated galectin-3 levels were associated with poor stroke outcomes (odds ratio [OR], 1.19; 95 % CI, 1.06-1.33; P<0.001). Further, higher post-stroke galectin-3 levels were associated with a higher risk of mortality (OR, 1.16; 95 % CI, 1.08-1.25; P<0.001), and this relationship was maintained after trim-and-fill analyses (OR, 1.14; 95 % CI, 1.06-1.22).

CONCLUSIONS

Galectin-3 levels post-stroke are increased in patients with a poor prognosis, and this elevation is associated with poor stroke outcomes and mortality. It is recommended that further high-caliber investigations with expanded sample sizes corroborate the outcomes of this study.

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.

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.

Outcome Prediction by Combining Corticospinal Tract Lesion Load with Diffusion-tensor Fractional Anisotropy in Patients after Hemorrhagic Stroke

Objectives

The objective of this study was to evaluate the predictive precision of combining the corticospinal tract lesion load (CST-LL) with the diffusion-tensor fractional anisotropy of the corticospinal tract (CST-FA) in the lesioned hemispheres regarding motor outcomes.

Methods

Patients with putaminal and/or thalamic hemorrhage who had undergone computed tomography (CT) soon after onset in our hospital were retrospectively enrolled. The CST-LL was calculated after registration of the CT images to a standard brain. Diffusion-tensor imaging was performed during the second week after onset. Standardized automated tractography was employed to calculate the CST-FA. Outcomes were assessed at discharge from our affiliated rehabilitation facility using total scores of the motor component of the Stroke Impairment Assessment Set (SIAS-motor total; null to full, 0 to 25). Multivariate regression analysis was performed with CST-LL and CST-FA as explanatory variables and SIAS-motor total as a target value.

Results

Twenty-five patients participated in this study. SIAS-motor total ranged from 0 to 25 (median, 17). CST-LL ranged from 0.298 to 7.595 (median, 2.522) mL, and the lesion-side CST-FA ranged from 0.211 to 0.530 (median, 0.409). Analysis revealed that both explanatory variables were detected as statistically significant contributory factors. The estimated t values indicated that the contributions of these two variables were almost equal. The obtained regression model accounted for 63.9% of the variability of the target value.

Conclusions

Incorporation of the CST-LL with the lesion-side CST-FA enhances the precision of the stroke outcome prediction model.

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.

Motor rehabilitation after stroke: European Stroke Organisation (ESO) consensus-based definition and guiding framework

PURPOSE

To propose a consensus-based definition and framework for motor rehabilitation after stroke.

METHODS

An expert European working group reviewed the literature, attaining internal consensus after external feedback.

FINDINGS

Motor rehabilitation is defined as a process that engages people with stroke to benefit their motor function, activity capacity and performance in daily life. It is necessary for people with residual motor disability whose goal is to enhance their functioning, independence and participation. Motor rehabilitation operates through learning- and use-dependent mechanisms. The trajectory of motor recovery varies across patients and stages of recovery. Early behavioral restitution of motor function depends on spontaneous biological mechanisms. Further improvements in activities of daily living are achieved by compensations. Motor rehabilitation is guided by regular assessment of motor function and activity using consensus-based measures, including patient-reported outcomes. Results are discussed with the patient and their carers to set personal goals. During motor rehabilitation patients learn to optimize and adapt their motor, sensory and cognitive functioning through appropriately dosed repetitive, goal-oriented, progressive, task- and context-specific training. Motor rehabilitation supports people with stroke to maximize health, well-being and quality of life. The framework describes the International Classification of Functioning, Disability and Health in the context of stroke, describes neurobiological mechanisms of behavioral restitution and compensation, and summarizes recommendations for clinical assessment, prediction tools, and motor interventions with strong recommendations from clinical practice guidelines (2016-2022).

CONCLUSIONS

This definition and framework may guide clinical educators, inform clinicians on current recommendations and guidelines, and identify gaps in the evidence base.

Usefulness of the GLIM criteria to predict recovery of activities of daily living in older adults with post-acute stroke

OBJECTIVES

The usefulness of malnutrition diagnosed using the Global Leadership Initiative on Malnutrition (GLIM) criteria as a predictor of recovery of activities of daily living is unclear. This study aimed to investigate whether baseline malnutrition diagnosed using the GLIM criteria was predictive of recovery of activities of daily living in older patients with post-acute stroke.

MATERIALS AND METHODS

A retrospective cohort study was conducted in patients aged ≥70 years with post-acute stroke. The outcome was activities of daily living measured using the motor domain of the Functional Independence Measure (FIM-motor) score at discharge. Participants were classified as malnourished or non-malnourished according to the GLIM criteria. Multivariate linear regression analyses were performed to determine whether baseline malnutrition diagnosed using the GLIM criteria was predictive of the FIM-motor score at discharge. The analysis was adjusted for clinically relevant covariates associated with rehabilitation outcomes after stroke.

RESULTS

A total of 236 patients (mean age, 80.0 years; female, 54.2%) were included in the analysis. On admission, 83 (35.2%) patients were diagnosed with malnutrition. Multivariate linear regression analyses showed that malnutrition diagnosed using the GLIM criteria was predictive of the FIM-motor score at discharge (β = -0.347, P < 0.001).

CONCLUSIONS

Identifying malnutrition using the GLIM criteria is useful for predicting recovery of activities of daily living in older patients with post-acute stroke.

Functional independence measured in the acute phase of stroke predicts both generic and specific health-related quality of life: a 3-month prospective study in a middle-income country

PURPOSE

To identify acute predictors of both generic and specific health-related quality of life (HRQoL) 3 months after stroke in individuals from a middle-income country.

MATERIALS AND METHODS

A 3-month prospective study with individuals who had suffered their first stroke, without previous disability, discharged from a stroke unit. The dependent outcomes, assessed 3 months after stroke, were generic and specific HRQoL (SF-36 and SSQOL total scores, respectively). The predictors assessed in the stroke unit were age, sex, education level, duration of hospital stay, current living arrangement, stroke severity (National Institutes of Health Stroke Scale-NIHSS), functional independence (Modified Barthel Index-MBI), motor impairment (Fugl-Meyer Assessment), and lower- and upper-limb residual muscle strength deficits. Linear multiple regression analyses were employed to identify predictors of both generic (model-1) and specific (model-2) HRQoL (α = 5%).

RESULTS

One hundred twenty-six individuals were assessed at 3-month post-stroke (61.3 ± 13.6 years). Regression analysis showed that functional independence was the best predictor of both generic (R2 = 21%; F = 34.82; p < 0.001) and specific (R2 = 29%; F = 51.71; p < 0.001) HRQoL at 3-month post-stroke.

CONCLUSION

Both generic and specific HRQoL at 3-month post-stroke can be predicted by functional independence assessed in the acute phase with the MBI.

Lifetime and Acute Stress Predict Functional Outcomes Following Stroke: Findings From the Longitudinal STRONG Study

BACKGROUND

Stroke is a sudden-onset, uncontrollable event; stroke-related stress may impede rehabilitation and recovery. Lifetime stress may sensitize patients to experiencing greater stroke-related stress and indirectly affect outcomes. We examine lifetime stress as predictor of poststroke acute stress and examine lifetime and acute stress as predictors of 3- and 12-month functional status. We also compare acute stress and baseline National Institutes of Health Stroke Scale as predictors of poststroke functional status.

METHODS

Between 2016 and 2020 the STRONG Study (Stroke, Stress, Rehabilitation, and Genetics) enrolled adults with new radiologically confirmed stroke 2 to 10 days poststroke onset at 28 acute care US hospitals. Participants were interviewed 3 times: acute admission (acute stress; Acute Stress Disorder Interview), 3 months (Fugl-Meyer Upper Extremity motor impairment [Fugl-Meyer Upper Arm Assessment; N=431], modified Rankin Scale [3 months; N=542], Stroke Impact Scale-Activities of Daily Living [3 months; N=511], Lifetime Stress Exposure Inventory), and 12 months (modified Rankin Scale, N=533; Stroke Impact Scale 3.0 Activities of Daily Living; N=485; Telephone Montreal Cognitive Assessment; N=484) poststroke. Structural equation models examined whether acute stress predicted 3- and 12-month functional outcomes, and mediated an association between lifetime stress and outcomes controlling for demographics and initial National Institutes of Health Stroke Scale. Standardized betas are reported.

RESULTS

Sample (N=763) was 19 to 95 years old (mean=63; SD=14.9); 448 (58.7%) were male. Acute stress scores ranged from 0 to 14 (mean, 3.52 [95% CI, 3.31-3.73]). Controlling for age, gender, baseline National Institutes of Health Stroke Scale, and race and ethnicity, higher lifetime stress predicted higher acute stress (β=0.18, P<0.001), which predicted lower 3-month Fugl-Meyer Upper Arm Assessment scores (β=-0.19, P<0.001), lower Stroke Impact Scale 3.0 Activities of Daily Living scores at 3 months (β=-0.21, P<0.001) and 12 months (β=-0.21, P<0.001), higher modified Rankin Scale scores at 3 months (β=0.23, P<0.001) and 12 months (β=0.22, P<0.001), and lower 12-month Telephone Montreal Cognitive Assessment scores (β=-0.20, P<0.001). Acute stress predicted 12-month tMoCA (χ2[1]=5.29, P=0.022) more strongly, 3-month and 12-month modified Rankin Scale and SIS scores as strongly (all Ps>0.18), but Fugl-Meyer scores (χ2[1]=7.01, P=0.008) less strongly than baseline National Institutes of Health Stroke Scale.

CONCLUSIONS

Lifetime stress/trauma is associated with more poststroke acute stress, which is associated with greater motor and cognitive impairment and disability 3 and 12 months poststroke. Poststroke interventions for acute stress may help mitigate stroke-related disability.

Development and Preliminary Validity Study of a Modified Version of the Upper Extremity Fugl-Meyer Assessment for Use in Telerehabilitation

BACKGROUND/PURPOSE

The Upper Extremity Fugl-Meyer Assessment (UEFMA, maximum 66) is widely used in clinics and research studies to examine poststroke upper extremity (UE) impairment. This study aimed to develop and provide pilot data to support the validity of a remote version of the UEFMA to examine UE impairment after stroke through telerehabilitation.

METHODS

Team members developed a remote version of the UEFMA for telerehabilitation (tUEFMA, maximum 44) using subscales II to IV and VII of the UEFMA. Twenty-two participants with moderate to severe arm impairment (UEFMA, median = 19) and chronic stroke (>1 year post) were evaluated using the UEFMA (face-to-face) and the tUEFMA (remotely). A prediction equation was used to identify the function to predict the UEFMA based on the tUEFMA. Intraclass correlation (ICC) was used to test the absolute agreement between the subscales included in the UEFMA and the tUEFMA, and between their 2 normalized total scores.

RESULTS

A strong and significant agreement was found between the total scores of the UEFMA and the projected value based on the tUEFMA (ICC = 0.79, P < 0.05). The ICC test also reported a good agreement in subscales II to IV and a poor agreement in subscale VII between the UEFMA and the tUEFMA using a real-time video link.

DISCUSSION AND CONCLUSIONS

The study findings suggest that the tUEFMA is a promising tool to remotely examine UE impairment in individuals with chronic stroke and moderate to severe arm impairment. Future research should evaluate additional psychometric properties and clinical utility of the tUEFMA across stroke participants with a broad range of arm impairments.Video Abstract available for more insights from the authors (see the Video, Supplemental Digital Content 1, available at: http://links.lww.com/JNPT/A441 ).

A Decision Support System to Provide an Ongoing Prediction of Robot-Assisted Rehabilitation Outcome in Stroke Survivors

Clinicians often deal with complex robotic platform and serious games in stroke patients rehabilitation contexts, and they face two main problems: 1) the interpretation of either the performance in game or measures of a robotic system from the motor recovery point of view, and 2) the duration and complexity of clinical scales administration that makes repetitive assessments during the therapy unpractical. In this paper, a Random Tree Forest based system was trained and tested to provide a prediction of different clinical outcomes (i.e. FMA, ARAT, and MI) along the whole therapy duration, having non-clinical measures only as inputs, acting as a simulated decision support system. The dataset includes 30 post-stroke patients, that underwent a 30-session robot-assisted rehabilitation treatment. Results have shown that the system is able to produce very accurate and reliable predictions about the motor recovery of the patient at the end of the therapy, already in the first phases of the rehabilitation (i40% of therapy execution), just using robotic platform measures. Such a tool would provide a great benefit in terms of rehabilitation objectives planning, as a decision support tool for highly personalized rehabilitation treatments.

Do kinematic measures, added to clinical measures, better predict upper extremity motor impairments at three months post-stroke?

OBJECTIVE

Predicting post-stroke recovery through prediction models is crucial for choosing appropriate treatment options. However, the existing models predominantly incorporate clinical measures although measurement of movement quality using kinematic measures is essential for distinguishing various types of recovery. Thus, this study aimed at determining if, by considering varied aspects of recovery, adding kinematic measurements over clinical measures would better predict upper extremity (UE) motor impairments at three months post-stroke.

MATERIALS AND METHODS

Eighty-nine stroke survivors (58.9 ± 11.8 years) were assessed for clinical predictors between 4 and 7 days, kinematic predictors within 1 month, and the impairment outcome of the Fugl Meyer Assessment of the UE (FM-UE) at three months post-stroke. Significant predictors (p<0.05) with a variation inflation factor (VIF) <10 were selected for model development. After performing further step-wise selection, three models incorporating clinical outcomes, kinematic measurements, and a combination of these two, respectively, were formulated.

RESULTS

The clinical model (R2 = 0.70) included shoulder abduction finger extension (SAFE) scores, the National Institutes of Health Stroke Scale (NIHSS), and the Montreal Cognitive Assessment (MoCA). The kinematic model (R2 = 0.34) included total displacement, total time, and reaction time. The combined model (R2 = 0.72) comprised of SAFE score and shoulder flexion. All the models had a minimal mean squared error on cross validation, which indicated a good validity.

CONCLUSION

The performance of clinical and combined prediction models for predicting three-month post-stroke UE motor recovery was nearly similar. However, in order to detect minimal changes over time and to understand all aspects of motor recovery, there is a need to add instrument-based kinematic measures.

Structural Integrity of the Cerebellar Outflow Tract Predicts Long-Term Motor Function After Middle Cerebral Artery Ischemic Stroke

BACKGROUND

The cerebellum plays a crucial role in functional movement by influencing sensorimotor coordination and learning. However, the effects of cortico-cerebellar connectivity on the recovery of upper extremity motor function after stroke have not been investigated. We hypothesized that the integrity of the cortico-cerebellar connections would be reduced in patients with a subacute middle cerebral artery (MCA) stroke, and that this reduction may help to predict chronic upper extremity motor function.

METHODS

We retrospectively analyzed the diffusion-tensor imaging of 25 patients with a subacute MCA stroke (mean age: 62.2 ± 2.7 years; 14 females) and 25 age- and sex-matched healthy controls. We evaluated the microstructural integrity of the corticospinal tract (CST), dentatothalamocortical tract (DTCT), and corticopontocerebellar tract (CPCT). Furthermore, we created linear regression models to predict chronic upper extremity motor function based on the structural integrity of each tract.

RESULTS

In stroke patients, the affected DTCT and CST showed significantly impaired structural integrity compared to unaffected tracts and the tracts in controls. When all models were compared, the model that used the fractional anisotropy (FA) asymmetry indices of CST and DTCT as independent variables best predicted chronic upper extremity motor function (R2 = .506, P = .001). The extent of structural integrity of the CPCT did not significantly differ between hemispheres or groups and was not predictive of motor function.

CONCLUSIONS

We found evidence that microstructural integrity of the DTCT in the subacute phase of an MCA stroke helped to predict chronic upper extremity motor function, independent of CST status.

Connectomic insight into unique stroke patient recovery after rTMS treatment

An improved understanding of the neuroplastic potential of the brain has allowed advancements in neuromodulatory treatments for acute stroke patients. However, there remains a poor understanding of individual differences in treatment-induced recovery. Individualized information on connectivity disturbances may help predict differences in treatment response and recovery phenotypes. We studied the medical data of 22 ischemic stroke patients who received MRI scans and started repetitive transcranial magnetic stimulation (rTMS) treatment on the same day. The functional and motor outcomes were assessed at admission day, 1 day after treatment, 30 days after treatment, and 90 days after treatment using four validated standardized stroke outcome scales. Each patient underwent detailed baseline connectivity analyses to identify structural and functional connectivity disturbances. An unsupervised machine learning (ML) agglomerative hierarchical clustering method was utilized to group patients according to outcomes at four-time points to identify individual phenotypes in recovery trajectory. Differences in connectivity features were examined between individual clusters. Patients were a median age of 64, 50% female, and had a median hospital length of stay of 9.5 days. A significant improvement between all time points was demonstrated post treatment in three of four validated stroke scales utilized. ML-based analyses identified distinct clusters representing unique patient trajectories for each scale. Quantitative differences were found to exist in structural and functional connectivity analyses of the motor network and subcortical structures between individual clusters which could explain these unique trajectories on the Barthel Index (BI) scale but not on other stroke scales. This study demonstrates for the first time the feasibility of using individualized connectivity analyses in differentiating unique phenotypes in rTMS treatment responses and recovery. This personalized connectomic approach may be utilized in the future to better understand patient recovery trajectories with neuromodulatory treatment.

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.

Patient's assessment and prediction of recovery after stroke: a roadmap for clinicians

BACKGROUND AND PURPOSE

In neurorehabilitation clinical practice, assessment is usually more oriented to evaluate patient's present status, than to plan interventions according to predicted outcomes. Therefore, we conducted an extensive review of current prognostic models available in the literature for recovery prediction of many functions and constructs, after stroke. We reported results in the form of a practical guide for clinicians, with the aim of promoting the culture of early clinical assessment for patient stratification, according to expected outcome. To define a roadmap for clinicians, a stepwise sequence of five actions has been developed, from collecting information of past medical history to the adoption of validated prediction tools. Furthermore, a clinically-oriented organization of available prediction tools for recovery after stroke have been proposed for motor, language, physiological and independency functions. Finally, biomarkers and online resources with prognostic value have been reviewed, to give the most updated state of the art on prediction tools after stroke.

RECOMMENDATIONS FOR CLINICAL PRACTICE

Clinical assessment should be directed both towards the objective evaluation of the present health status, and to the prediction of expected recovery. The use of specific outcome measures with predictive value is recommended to help clinicians with the definition of sound therapeutic goals.

Establishment of a dynamic nomogram including thyroid function for predicting the prognosis of acute ischemic stroke with standardized treatment

Purpose

Many patients with acute ischemic stroke (AIS) cannot undergo thrombolysis or thrombectomy because they have missed the time window or do not meet the treatment criteria. In addition, there is a lack of an available tool to predict the prognosis of patients with standardized treatment. This study aimed to develop a dynamic nomogram to predict the 3-month poor outcomes in patients with AIS.

Methods

This was a retrospective multicenter study. We collected the clinical data of patients with AIS who underwent standardized treatment at the First People's Hospital of Lianyungang from 1 October 2019 to 31 December 2021 and at the Second People's Hospital of Lianyungang from 1 January 2022 to 17 July 2022. Baseline demographic, clinical, and laboratory information of patients were recorded. The outcome was the 3-month modified Rankin Scale (mRS) score. The least absolute shrinkage and selection operator regression were used to select the optimal predictive factors. Multiple logistic regression was performed to establish the nomogram. A decision curve analysis (DCA) was applied to assess the clinical benefit of the nomogram. The calibration and discrimination properties of the nomogram were validated by calibration plots and the concordance index.

Results

A total of 823 eligible patients were enrolled. The final model included gender (male; OR 0.555; 95% CI, 0.378-0.813), systolic blood pressure (SBP; OR 1.006; 95% CI, 0.996-1.016), free triiodothyronine (FT3; OR 0.841; 95% CI, 0.629-1.124), National Institutes of Health stroke scale (NIHSS; OR 18.074; 95% CI, 12.264-27.054), Trial of Org 10172 in Acute Stroke Treatment (TOAST; cardioembolic (OR 0.736; 95% CI, 0.396-1.36); and other subtypes (OR 0.398; 95% CI, 0.257-0.609). The nomogram showed good calibration and discrimination (C-index, 0.858; 95% CI, 0.830-0.886). DCA confirmed the clinical usefulness of the model. The dynamic nomogram can be obtained at the website: predict model (90-day prognosis of AIS patients).

Conclusion

We established a dynamic nomogram based on gender, SBP, FT3, NIHSS, and TOAST, which calculated the probability of 90-day poor prognosis in AIS patients with standardized treatment.

Association of inpatient rehabilitation with functional outcome in patients with stroke receiving mechanical thrombectomy

INTRODUCTION

Patients with ischemic strokes that have undergone mechanical thrombectomy (MT) can still have poor functional outcomes. Limited research exists on factors that impact functional outcomes in these patients who are discharged to inpatient rehabilitation. We examined patient characteristics correlated with 90-day outcomes in ischemic stroke patients having undergone MT and undergo acute rehabilitation.

METHODS

This is a retrospective study of patients (≥18 years) who were consecutively admitted to inpatient rehabilitation after undergoing MT following an ischemic stroke event from 2015 through 2018. Functional outcomes at 90-days were determined by the modified Rankin Score (mRS). Binary logistic regression models were used to measure the association of meaningful Functional Independence Measure (FIM) change with poor-outcome (mRS >2).

RESULTS

Out of 56 stroke patients undergoing MT, 36 had poor outcome (mRS >2) at 90-days. One unit increase in discharge FIM significantly decreased the likelihood of poor outcome by 4% [aOR = 0.96; 95% CI = 95% CI = 0.92, 0.99]. Admission FIM [aOR = 0.95; 95% CI = 0.90, 1.00] and change in FIM [aOR = 0.97; 95% CI = 0.93, 1.02] were not significantly associated poor outcome in the adjusted models.

CONCLUSION

Patients with ischemic stroke who received MT who have a worse disability, particularly at discharge, while undergoing inpatient rehabilitation have higher odds of having poor future functional outcomes. This was regardless of age, sex, or race. Future research is needed to understand the mechanisms that can improve functional performance in our patient population at the IRF.

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.

Predicting Motor Outcomes Using Atlas-Based Voxel Features of Post-Stroke Neuroimaging: A Scoping Review

BACKGROUND

Atlas-based voxel features have the potential to aid motor outcome prognostication after stroke, but are seldom used in clinically feasible prediction models. This could be because neuroimaging feature development is a non-standardized, complex, multistep process. This is a barrier to entry for researchers and poses issues for reproducibility and validation in a field of research where sample sizes are typically small.

OBJECTIVES

The primary aim of this review is to describe the methodologies currently used in motor outcome prediction studies using atlas-based voxel neuroimaging features. Another aim is to identify neuroanatomical regions commonly used for motor outcome prediction.

METHODS

A Preferred Reporting Items for Systematic Reviews and Meta-Analyses protocol was constructed and OVID Medline and Scopus databases were searched for relevant studies. The studies were then screened and details about imaging modality, image acquisition, image normalization, lesion segmentation, region of interest determination, and imaging measures were extracted.

RESULTS

Seventeen studies were included and examined. Common limitations were a lack of detailed reporting on image acquisition and the specific brain templates used for normalization and a lack of clear reasoning behind the atlas or imaging measure selection. A wide variety of sensorimotor regions relate to motor outcomes and there is no consensus use of one single sensorimotor atlas for motor outcome prediction.

CONCLUSION

There is an ongoing need to validate imaging predictors and further improve methodological techniques and reporting standards in neuroimaging feature development for motor outcome prediction post-stroke.

Medicare Advantage Patterns of Poststroke Discharge to an Inpatient Rehabilitation or Skilled Nursing Facility: A Consideration of Demographic, Functional, and Payer Factors

OBJECTIVE

The purpose of this study was to determine the factors influencing the discharge to an inpatient rehabilitation facility (IRF) or a skilled nursing facility (SNF) of people poststroke with Medicare Advantage plans.

METHODS

A retrospective cohort study was conducted with data from naviHealth, a company that manages postacute care discharge placement on behalf of Medicare Advantage organizations. The dependent variable was discharge destination (IRF or SNF). Variables included age, sex, prior living setting, functional status (Activity Measure for Post-Acute Care [AM-PAC]), acute hospital length of stay, comorbidities, and payers (health plans). Analysis estimated relative risk (RR) of discharge to SNF, while controlling for regional variation.

RESULTS

Individuals discharged to an SNF were older (RR = 1.17), women (RR = 1.05), lived at home alone or in assisted living (RR = 1.13 and 1.39, respectively), had comorbidities impacting their function "some" or "severely" (RR = 1.43 and 1.81, respectively), and had a length of stay greater than 5 days (RR = 1.16). Individuals with better AM-PAC Basic Mobility (RR = 0.95) went to an IRF, and individuals with better Daily Activity (RR = 1.01) scores went to an SNF. There was a substantial, significant variation in discharge of individuals to SNF by payer group (RR range = 1.12-1.92).

CONCLUSIONS

The results of this study show that individuals poststroke are more likely to be discharged to an SNF than to an IRF. This study did not find a different discharge decision-making picture for those with Medicare Advantage plans than previously described for other insurance programs.

IMPACT

Medicare Advantage payers have varied patterns in discharge placement to an IRF or SNF for patients poststroke.

Racial and Ethnic Disparities in Stroke Reperfusion Therapy in the USA

Racial and ethnic inequities in stroke care are ubiquitous. Acute reperfusion therapies, i.e., IV thrombolysis (IVT) and mechanical thrombectomy (MT), are central to acute stroke care and are highly efficacious at preventing death and disability after stroke. Disparities in the use of IVT and MT in the USA are pervasive and contribute to worse outcomes among racial and ethnic minority individuals with ischemic stroke. A meticulous understanding of disparities and underlying root causes is necessary in order to develop targeted mitigation strategies with lasting effects. This review details racial and ethnic disparities in the use of IVT and MT after stroke and highlights inequities in the underlying process measures as well as the contributing root causes. Furthermore, this review spotlights the systemic and structural inequities that contribute to race-based differences in the use of IVT and MT, including geographic and regional differences and differences based on neighborhood, zip code, and hospital type. In addition, recent promising trends suggesting improvements in racial and ethnic IVT and MT disparities and potential approaches for future solutions to achieve equity in stroke care are briefly discussed.

Association of clinical factors to functional outcomes in patients with stroke with large-vessel occlusion after endovascular thrombectomy

BACKGROUND/PURPOSE

Multiple clinical factors have been reported to be associated with functional outcomes in patients with stroke. However, little is known about prognostic predictors of functional independence in patients with stroke undergoing endovascular thrombectomy (EVT). Our study aimed to investigate the correlation between multiple prognostic variables (including EVT and rehabilitation-related parameters) and functional outcomes in patients post-EVT.

METHODS

This retrospective cohort study recruited patients hospitalized between December 2018 and March 2022. Patients with stroke with large-vessel occlusion who underwent EVT were eligible for inclusion in the study. Prognostic factors, including premorbid characteristics, laboratory data, EVT- and rehabilitation-related parameters, functional activity level, balance ability, swallowing, and sphincter function, were collected. Logistic regression and generalized linear models were used to analyze their correlations with functional outcomes.

RESULTS

A total of 148 patients were included. In the univariate logistic regression analysis, younger age, premorbid functional independence, higher hemoglobin (Hb) level, lower National Institute of Health Stroke Scale (NIHSS) score, absence of hemorrhagic transformation in 14 days, no nasogastric (NG) tube placement, earlier rehabilitation, frequent daily rehabilitation sessions, more out-of-bed rehabilitation, better ability of sitting up, better initial sitting balance, higher Barthel index (BI), absence of immobility, and neurological complications were associated with favorable outcomes at 3 months. In the stepwise regression model, the predictors of favorable function at 3 months included age, ability to sit up, and frequency of daily rehabilitation sessions; favorable outcomes at 6 months were associated with age, ability to sit up, and swallowing function.

CONCLUSION

In patients with stroke post-EVT, better functional outcomes were associated with prognostic variables, including younger age, better ability to sit up, normal swallowing function, and frequent daily rehabilitation sessions.

Stroke Rehabilitation and Motor Recovery

OBJECTIVE

Up to 50% of the nearly 800,000 patients who experience a new or recurrent stroke each year in the United States fail to achieve full independence afterward. More effective approaches to enhance motor recovery following stroke are needed. This article reviews the rehabilitative principles and strategies that can be used to maximize post-stroke recovery.

LATEST DEVELOPMENTS

Evidence dictates that mobilization should not begin prior to 24 hours following stroke, but detailed guidelines beyond this are lacking. Specific classes of potentially detrimental medications should be avoided in the early days poststroke. Patients with stroke who are unable to return home should be referred for evaluation to an inpatient rehabilitation facility. Research suggests that a substantial increase in both the dose and intensity of upper and lower extremity exercise is beneficial. A clinical trial supports vagus nerve stimulation as an adjunct to occupational therapy for motor recovery in the upper extremity. The data remain somewhat mixed as to whether robotics, transcranial magnetic stimulation, functional electrical stimulation, and transcranial direct current stimulation are better than dose-matched traditional exercise. No current drug therapy has been proven to augment exercise poststroke to enhance motor recovery.

ESSENTIAL POINTS

Neurologists will collaborate with rehabilitation professionals for several months following a patient's stroke. Many questions still remain about the ideal exercise regimen to maximize motor recovery in patients poststroke. The next several years will likely bring a host of new research studies exploring the latest strategies to enhance motor recovery using poststroke exercise.

Plasma bioactive adrenomedullin predicts outcome after acute stroke in early rehabilitation

An early and reliable prediction of outcomes after stroke is important for early effective stroke management and the adequate optimal planning of post-stroke rehabilitation and long-term care. Bioactive adrenomedullin (bio-ADM) is a 52-amino acid peptide that is an important peptide hormone in nervous system diseases. The aim of this study was to investigate the prognostic value of bio-ADM on outcomes after rehabilitation in patients with stroke. A total of 557 consecutive patients with a primary diagnosis of ischemic or hemorrhagic stroke (age 69.6-12.9 years, male 51.3%, ischemic stroke 72.5%), who were admitted to an in-patient early rehabilitation center directly after discharge from acute stroke hospital care, were enrolled in this prospective observational study. Plasma concentrations of bio-ADM were determined by using a chemiluminescence immunoassay (functional assay sensitivity 8 pg/ml). The early rehabilitation barthel index (ERBI) was used for the neurological assessment of the patients. The plasma bio-ADM level was analyzed in association with 6-month all-cause mortality as well as a composite outcome of all-cause mortality, unscheduled re-hospitalization, or transfer to a long-term care facility in a vegetative or minimally conscious state. Bio-ADM levels significantly increased in patients with ischemic stroke who died compared to surviving patients (40.4 pg/ml vs. 23.8 pg/ml, p < 0.001) or in those with composite outcomes compared to those with no events (36.9 pg/ml vs. 23.5 pg/ml, p < 0.001). Six-month all-cause mortality was higher in all patients with bio-ADM levels > 70 pg/ml (HR 4.83 [CI 2.28-10.2]). Patients with bio-ADM levels > 70 pg/ml also had higher rates of 6-month composite outcomes (HR 3.82 [CI 2.08-7.01]). Bio-ADM was an independent predictor of all-cause mortality and 6-month composite outcomes after adjusting for age, gender, and ERBI (adjusted OR 1.5; 95% CI 1.0-2.1; p = 0.047 and adjusted OR 1.48; 95% CI 1.1-2.0; p = 0.01, respectively). Bio-ADM may be a suitable novel biomarker to assess the outcomes of patients in rehabilitation after acute stroke. Elevated bio-ADM concentrations may have prognostic value for fatal and nonfatal events in patients with ischemic stroke during early rehabilitation.

Connectome-based predictive modeling for functional recovery of acute ischemic stroke

Patients of acute ischemic stroke possess considerable chance of recovery of various levels in the first several weeks after stroke onset. Prognosis of functional recovery is important for decision-making in poststroke patient care and placement. Poststroke functional recovery has conventionally been based on demographic and clinical variables such as age, gender, and severity of stroke impairment. On the other hand, the concept of connectome has become a basis of interpreting the functional impairment and recovery of stroke patients. In this research, the connectome-based predictive modeling was used to provide predictive models for prognosing poststroke functional recovery. Predictive models were developed to use the brain connectivity at stroke onset to predict functional assessment scores at one or three months later, or to use the brain connectivity one-month poststroke to predict functional assessment scores at three months after stroke onset. The brain connectivity was computed from the resting-state fMRI signals. The functional assessment scores used in this research included modified Rankin Scale (mRS) and Barthel Index (BI). This research found significant models that used the brain connectivity at onset to predict the mRS one-month poststroke and to predict the BI three-month poststroke for patients with supratentorial infarction, as well as predictive models that used the brain connectivity one-month poststroke to predict the mRS three-month poststroke for patients with supratentorial infarction in the right hemisphere. The connectome-based predictive modeling could provide clinical value in prognosis of acute ischemic stroke.

Validity of sarcopenia diagnosis defined by calf circumference for muscle mass to predict functional outcome in patients with acute stroke

OBJECTIVE

This study examined the association between sarcopenia diagnosed by defining muscle mass with calf circumference and physical function at hospital discharge in patients with acute stroke and to perform a comparison with sarcopenia diagnosis according to the Asian Working Group for Sarcopenia.

RESEARCH METHODS & PROCEDURES

This retrospective cohort study included patients with acute stroke. We calculated the calf circumference cut-off value defining low skeletal muscle index for the Asian Working Group for Sarcopenia diagnostic criteria and used a combination of low calf circumference and low grip strength to define sarcopenia-calf circumference. A combination of low skeletal muscle index and low grip strength defined sarcopenia-Asian Working Group for Sarcopenia. Associations between sarcopenia-calf circumference, sarcopenia-Asian Working Group for Sarcopenia, and Functional Independence Measure motor score were evaluated using multiple regression analysis.

RESULTS

The study included 308 patients (198 men; mean age, 73.2 years). Sarcopenia-calf circumference prevalence was 24.7% in men and 46.4% in women. In men, sarcopenia-calf circumference (β=-0.178; 95% CI: -0.284, -0.073; p=0.001) and sarcopenia-Asian Working Group for Sarcopenia (β=-0.228; 95% CI: -0.330, -0.127; p<0.001) were significantly associated with Functional Independence Measure motor score at discharge.

CONCLUSIONS

Sarcopenia diagnosed by defining muscle mass with calf circumference was negatively associated with physical function at discharge in male patients with acute stroke. Sarcopenia diagnosed using Asian Working Group for Sarcopenia criteria was superior to sarcopenia-calf circumference for predicting physical function at discharge. Our findings suggest that the use of CC enables assessment of sarcopenia even at facilities where muscle mass measurements may be difficult.

Relationship of Functional Outcome With Sarcopenia and Objectively Measured Physical Activity in Patients With Stroke Undergoing Rehabilitation

This study aimed to investigate the relationship of Functional Independence Measure for motor function (FIM-M) with sarcopenia, and physical activity in patients with stroke undergoing rehabilitation. This cross-sectional study included patients with stroke at a single convalescent rehabilitation hospital. Sarcopenia was diagnosed based on the Asia Working Group for Sarcopenia 2019 criteria. Physical activity was measured as the duration of light-intensity physical activity and moderate to vigorous physical activity using a triaxial accelerometer. Of 80 patients (median age: 72.0 years), 46 (57.5%) were diagnosed with sarcopenia. In multivariate linear regression analysis, FIM-M score was significantly associated with sarcopenia (β = -0.15, p = .043) and light-intensity physical activity (β = 0.55, p < .001). In another model, FIM-M score was significantly associated with moderate to vigorous physical activity (β = 0.27, p = .002) but not with sarcopenia. This study demonstrated that FIM-M was partially associated with sarcopenia and associated with physical activity regardless of intensity in patients with stroke.

The impact of triglyceride-glucose index on ischemic stroke: a systematic review and meta-analysis

BACKGROUND

Strokes significantly impair quality of life and incur high economic and societal burdens. The triglyceride and glucose (TyG) index is a biochemical marker of insulin resistance (IR) and may have important value in the prediction of strokes, especially ischemic stroke (IS). Our study aims to investigate the relationship between TyG index and IS and ascertain whether TyG index is independently associated with IS adverse outcomes.

METHODS

The Cochrane, Embase, Medline, Web of Science, PubMed, and other relevant English databases and related websites were systematically searched for articles on ''TyG index'' and "stroke" published from inception to April 4, 2022. We reviewed the available literature on the TyG index and its relation to predicting IS occurrence in the general population and adverse clinical outcomes. We calculated odds ratios (OR) of TyG index and its predictability of IS occurrence and adverse outcomes. Statistical analyses were performed using the Meta Package in STATA, version 12.0.

RESULTS

A total of 18 studies and 592,635 patients were included in our analysis. The pooled effect values of all stroke types showed that higher TyG index was associated with increased the risk of IS in the general population (OR 1.37; 95% CI 1.22-1.54) in a total sample of 554,334 cases with a high level of heterogeneity (P = 0.000, I² = 74.10%). In addition, compared to IS patients with a lower TyG index, IS patients with a higher TyG index was associated with higher risk of stroke recurrence (OR: 1.50; 95% CI 1.19-1.89) and increased risk of mortality (OR 1.40 95% CI 1.14-1.71). No correlation was found in the effect value combinations of poor functional outcomes (OR 1.12; 95% CI 0.88-1.43) and neurological worsening (OR: 1.76; 95% CI 0.79-3.95) in a total sample of 38,301 cases with a high level of heterogeneity (P = 0.000; I² = 77.20%).

CONCLUSIONS

TyG index has potential value in optimizing risk stratification for IS in the general population. Furthermore, there is a significant association between high TyG index and many adverse outcomes of stroke, especially stroke recurrence and high mortality. Future studies should focus on multi-center and multi-regional designs in order to further explore the relationship between IS and TyG index.

Malnutrition on admission is associated with trunk function recovery but not with lower limb muscle strength recovery in patients with acute stroke: an observational cohort study

OBJECTIVES

The aim of this study was to determine the effects of malnutrition on trunk function and lower leg muscle strength in patients with acute stroke upon hospitalization.

METHODS

This prospective cohort study included hospitalized patients with acute stroke. Nutritional status was assessed using the Global Leadership Initiative on Malnutrition criteria. Trunk function and lower leg muscle strength were assessed using the trunk control test (TCT) and Motricity Index (MI), respectively, on admission and at discharge. Logistic regression analysis was performed to examine the relationship between malnutrition and poor improvement in TCT and MI scores at discharge.

RESULTS

Patients (N = 241) with acute stroke (median age 79 y) were included in this study. In adjusted logistic regression analysis, malnutrition was independently associated with poor TCT score improvement (adjusted odds ratio, 3.82; 95% confidence interval, 1.11-13.20; P = 0.03). In contrast, malnutrition was not independently associated with poor MI score improvement (adjusted odds ratio, 0.86; 95% confidence interval, 0.30-2.52; P = 0.79).

CONCLUSION

Malnutrition on admission leads to poor trunk function, but not lower leg muscle strength, in patients with acute stroke.

Clustering and prediction of long-term functional recovery patterns in first-time stroke patients

Objectives

The purpose of this study was to cluster long-term multifaceted functional recovery patterns and to establish prediction models for functional outcome in first-time stroke patients using unsupervised machine learning.

Methods

This study is an interim analysis of the dataset from the Korean Stroke Cohort for Functioning and Rehabilitation (KOSCO), a long-term, prospective, multicenter cohort study of first-time stroke patients. The KOSCO screened 10,636 first-time stroke patients admitted to nine representative hospitals in Korea during a three-year recruitment period, and 7,858 patients agreed to enroll. Early clinical and demographic features of stroke patients and six multifaceted functional assessment scores measured from 7 days to 24 months after stroke onset were used as input variables. K-means clustering analysis was performed, and prediction models were generated and validated using machine learning.

Results

A total of 5,534 stroke patients (4,388 ischemic and 1,146 hemorrhagic; mean age 63·31 ± 12·86; 3,253 [58.78%] male) completed functional assessments 24 months after stroke onset. Through K-means clustering, ischemic stroke (IS) patients were clustered into five groups and hemorrhagic stroke (HS) patients into four groups. Each cluster had distinct clinical characteristics and functional recovery patterns. The final prediction models for IS and HS patients achieved relatively high prediction accuracies of 0.926 and 0.887, respectively.

Conclusions

The longitudinal, multi-dimensional, functional assessment data of first-time stroke patients were successfully clustered, and the prediction models showed relatively good accuracies. Early identification and prediction of long-term functional outcomes will help clinicians develop customized treatment strategies.

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

OBJECTIVE

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

DESIGN

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

SETTING

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

PARTICIPANTS

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

INTERVENTIONS

Not applicable.

MAIN OUTCOME MEASURE(S)

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

RESULTS

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

CONCLUSIONS

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

Brain-machine Interface (BMI)-based Neurorehabilitation for Post-stroke Upper Limb Paralysis

Because recovery from upper limb paralysis after stroke is challenging, compensatory approaches have been the main focus of upper limb rehabilitation. However, based on fundamental and clinical research indicating that the brain has a far greater potential for plastic change than previously thought, functional restorative approaches have become increasingly common. Among such interventions, constraint-induced movement therapy, task-specific training, robotic therapy, neuromuscular electrical stimulation (NMES), mental practice, mirror therapy, and bilateral arm training are recommended in recently published stroke guidelines. For severe upper limb paralysis, however, no effective therapy has yet been established. Against this background, there is growing interest in applying brain-machine interface (BMI) technologies to upper limb rehabilitation. Increasing numbers of randomized controlled trials have demonstrated the effectiveness of BMI neurorehabilitation, and several meta-analyses have shown medium to large effect sizes with BMI therapy. Subgroup analyses indicate higher intervention effects in the subacute group than the chronic group, when using movement attempts as the BMI-training trigger task rather than using motor imagery, and using NMES as the external device compared with using other devices. The Keio BMI team has developed an electroencephalography-based neurorehabilitation system and has published clinical and basic studies demonstrating its effectiveness and neurophysiological mechanisms. For its wider clinical application, the positioning of BMI therapy in upper limb rehabilitation needs to be clarified, BMI needs to be commercialized as an easy-to-use and cost-effective medical device, and training systems for rehabilitation professionals need to be developed. A technological breakthrough enabling selective modulation of neural circuits is also needed.

Early prediction of upper limb functioning after stroke using clinical bedside assessments: a prospective longitudinal study

Early and accurate prediction of recovery is needed to assist treatment planning and inform patient selection in clinical trials. This study aimed to develop a prediction algorithm using a set of simple early clinical bedside measures to predict upper limb capacity at 3-months post-stroke. A secondary analysis of Stroke Arm Longitudinal Study at Gothenburg University (SALGOT) included 94 adults (mean age 68 years) with upper limb impairment admitted to stroke unit). Cluster analysis was used to define the endpoint outcome strata according to the 3-months Action Research Arm Test (ARAT) scores. Modelling was carried out in a training (70%) and testing set (30%) using traditional logistic regression, random forest models. The final algorithm included 3 simple bedside tests performed 3-days post stroke: ability to grasp, to produce any measurable grip strength and abduct/elevate shoulder. An 86-94% model sensitivity, specificity and accuracy was reached for differentiation between poor, limited and good outcome. Additional measurement of grip strength at 4 weeks post-stroke and haemorrhagic stroke explained the underestimated classifications. External validation of the model is recommended. Simple bedside assessments have advantages over more lengthy and complex assessments and could thereby be integrated into routine clinical practice to aid therapy decisions, guide patient selection in clinical trials and used in data registries.

Use of peripheral electrical stimulation on healthy individual and patients after stroke and its effects on the somatosensory evoked potentials. A systematic review

INTRODUCTION

To date, a few studies have used somatosensory evoked potentials (SEP) to demonstrate cortical sensory changes among healthy subjects or to estimate cortical plasticity and rehabilitation prognosis in stroke patients after peripheral electrical stimulation (PES) intervention. The primary aim was to systematically review whether PES has a role in changing latencies and amplitudes of SEPs in healthy subjects and stroke patients. Moreover, we searched for a correlation between sensory and motor function assessments and changes in SEP components of included studies.

METHODS

The following databases were searched: Pubmed/MEDLINE, Scopus/ScienceDirect, Web of Science/Clarivate, Cochrane Library, The Physiotherapy Evidence Database (PEDro), and ClinicalTrials.gov. Titles and abstracts, as well as full-text reports, were screened for eligibility by two independent reviewers according to a priori defined eligibility criteria. There were no study limitations concerning the treatment of the upper limb, lower limb, or torso with PES.

RESULTS

The final systematic search resulted in 11,344 records, however only 10 were evaluated. We could not find enough evidence to confirm use of SEP as a predictor to estimate the rehabilitation prognosis after stroke. However, we found a correlation between different sensory and motor function assessments and changes in SEP components. The stroke studies involving PES that initiate a voluntary contraction used for a specific movement or task indicate a positive relationship and correlation to assessments of motor function. It could be indicated that PES have a predictive impact of sensory reorganization, as mirrored by the change in SEP amplitude and latency. However, it is not possible to verify the degree of connectivity between SEP and cortical plasticity. To confirm this hypothesis, we propose the conduction of randomized controlled trials in healthy volunteers and stroke patients.

SYSTEMATIC REVIEW REGISTRATION

https://doi.org/10.17605/OSF.IO/U7PSY.

Walk the Talk: Current Evidence for Walking Recovery After Stroke, Future Pathways and a Mission for Research and Clinical Practice

Achieving safe, independent, and efficient walking is a top priority for stroke survivors to enable quality of life and future health. This narrative review explores the state of the science in walking recovery after stroke and potential for development. The importance of targeting walking capacity and performance is explored in relation to individual stroke survivor gait recovery, applying a common language, measurement, classification, prediction, current and future intervention development, and health care delivery. Findings are summarized in a model of current and future stroke walking recovery research and a mission statement is set for researchers and clinicians to drive the field forward to improve the lives of stroke survivors and their carers.

The feasibility and accuracy of machine learning in improving safety and efficiency of thrombolysis for patients with stroke: Literature review and proposed improvements

In the treatment of ischemic stroke, timely and efficient recanalization of occluded brain arteries can successfully salvage the ischemic brain. Thrombolysis is the first-line treatment for ischemic stroke. Machine learning models have the potential to select patients who could benefit the most from thrombolysis. In this study, we identified 29 related previous machine learning models, reviewed the models on the accuracy and feasibility, and proposed corresponding improvements. Regarding accuracy, lack of long-term outcome, treatment option consideration, and advanced radiological features were found in many previous studies in terms of model conceptualization. Regarding interpretability, most of the previous models chose restrictive models for high interpretability and did not mention processing time consideration. In the future, model conceptualization could be improved based on comprehensive neurological domain knowledge and feasibility needs to be achieved by elaborate computer science algorithms to increase the interpretability of flexible algorithms and shorten the processing time of the pipeline interpreting medical images.

Wearable Sensors Improve Prediction of Post-Stroke Walking Function Following Inpatient Rehabilitation

OBJECTIVE

A primary goal of acute stroke rehabilitation is to maximize functional recovery and help patients reintegrate safely in the home and community. However, not all patients have the same potential for recovery, making it difficult to set realistic therapy goals and to anticipate future needs for short- or long-term care. The objective of this study was to test the value of high-resolution data from wireless, wearable motion sensors to predict post-stroke ambulation function following inpatient stroke rehabilitation.

METHOD

Supervised machine learning algorithms were trained to classify patients as either household or community ambulators at discharge based on information collected upon admission to the inpatient facility (N=33-35). Inertial measurement unit (IMU) sensor data recorded from the ankles and the pelvis during a brief walking bout at admission (10 meters, or 60 seconds walking) improved the prediction of discharge ambulation ability over a traditional prediction model based on patient demographics, clinical information, and performance on standardized clinical assessments.

RESULTS

Models incorporating IMU data were more sensitive to patients who changed ambulation category, improving the recall of community ambulators at discharge from 85% to 89-93%.

CONCLUSIONS

This approach demonstrates significant potential for the early prediction of post-rehabilitation walking outcomes in patients with stroke using small amounts of data from three wearable motion sensors.

CLINICAL IMPACT

Accurately predicting a patient's functional recovery early in the rehabilitation process would transform our ability to design personalized care strategies in the clinic and beyond. This work contributes to the development of low-cost, clinically-implementable prognostic tools for data-driven stroke treatment.

An Effective Fusing Approach by Combining Connectivity Network Pattern and Temporal-Spatial Analysis for EEG-Based BCI Rehabilitation

Motor-modality-based brain computer interface (BCI) could promote the neural rehabilitation for stroke patients. Temporal-spatial analysis was commonly used for pattern recognition in this task. This paper introduced a novel connectivity network analysis for EEG-based feature selection. The network features of connectivity pattern not only captured the spatial activities responding to motor task, but also mined the interactive pattern among these cerebral regions. Furthermore, the effective combination between temporal-spatial analysis and network analysis was evaluated for improving the performance of BCI classification (81.7%). And the results demonstrated that it could raise the classification accuracies for most of patients (6 of 7 patients). This proposed method was meaningful for developing the effective BCI training program for stroke rehabilitation.

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

Objective

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

Methods

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

Results

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

Conclusions

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