Association of Modified Rankin Scale With Recovery Phenotypes in Patients With Upper Extremity Weakness After Stroke

High-intensity interval training is feasible, credible and clinically effective in the early subacute stroke stage in the low-income country of Benin

High-intensity interval training (HIIT) has been shown to benefit stroke patients when implemented three months post-stroke. This study examined HIIT's feasibility and clinical effectiveness in the early post-stroke stage in Benin. This was a prospective interventional study comprising an HIIT programme executed on a recumbent bike, three times/week, 20-30 min/session for 6 weeks, added to a conventional physiotherapy. The primary outcomes were feasibility, credibility and expectancy assessed with credibility and expectancy questionnaire. A maximal exercise test, 6-min walking test (6MWT), 10-m walking test (10mWT), Berg balance scale (BBS) and five repetitions sit-to-stand test (5 R-STS) were performed before and after the training programme. Ten outpatients, with a median age [P25-P75]: 63.5[56.7-71.2] years; time since stroke: 15.0[9.7-21.0] days, started and completed all training sessions without serious adverse events. High scores were observed on the Credibility subscale at admission (27.0[25.7-27.0]), which remained so after intervention (26.5[25.7-27.0]). Expectancy subscale scores were high at admission (25.5[24.0-27.0]) and post-training (25.5[24.5-27.0]). Peak workload (p < 0.001), BBS (p < 0.001), 6MWT (p < 0.001), 10mWT (p < 0.001) and 5 R-STS (p = 0.004) were all improved. HIIT is feasible and safe in the early subacute post-stroke stage and is perceived by patients as highly credible, meeting their expectations of recovery.

NSF DARE-Transforming modeling in neurorehabilitation: Four threads for catalyzing progress

We present an overview of the Conference on Transformative Opportunities for Modeling in Neurorehabilitation held in March 2023. It was supported by the Disability and Rehabilitation Engineering (DARE) program from the National Science Foundation's Engineering Biology and Health Cluster. The conference brought together experts and trainees from around the world to discuss critical questions, challenges, and opportunities at the intersection of computational modeling and neurorehabilitation to understand, optimize, and improve clinical translation of neurorehabilitation. We organized the conference around four key, relevant, and promising Focus Areas for modeling: Adaptation & Plasticity, Personalization, Human-Device Interactions, and Modeling 'In-the-Wild'. We identified four common threads across the Focus Areas that, if addressed, can catalyze progress in the short, medium, and long terms. These were: (i) the need to capture and curate appropriate and useful data necessary to develop, validate, and deploy useful computational models (ii) the need to create multi-scale models that span the personalization spectrum from individuals to populations, and from cellular to behavioral levels (iii) the need for algorithms that extract as much information from available data, while requiring as little data as possible from each client (iv) the insistence on leveraging readily available sensors and data systems to push model-driven treatments from the lab, and into the clinic, home, workplace, and community. The conference archive can be found at (dare2023.usc.edu). These topics are also extended by three perspective papers prepared by trainees and junior faculty, clinician researchers, and federal funding agency representatives who attended the conference.

Transforming modeling in neurorehabilitation: clinical insights for personalized rehabilitation

Practicing clinicians in neurorehabilitation continue to lack a systematic evidence base to personalize rehabilitation therapies to individual patients and thereby maximize outcomes. Computational modeling- collecting, analyzing, and modeling neurorehabilitation data- holds great promise. A key question is how can computational modeling contribute to the evidence base for personalized rehabilitation? As representatives of the clinicians and clinician-scientists who attended the 2023 NSF DARE conference at USC, here we offer our perspectives and discussion on this topic. Our overarching thesis is that clinical insight should inform all steps of modeling, from construction to output, in neurorehabilitation and that this process requires close collaboration between researchers and the clinical community. We start with two clinical case examples focused on motor rehabilitation after stroke which provide context to the heterogeneity of neurologic injury, the complexity of post-acute neurologic care, the neuroscience of recovery, and the current state of outcome assessment in rehabilitation clinical care. Do we provide different therapies to these two different patients to maximize outcomes? Asking this question leads to a corollary: how do we build the evidence base to support the use of different therapies for individual patients? We discuss seven points critical to clinical translation of computational modeling research in neurorehabilitation- (i) clinical endpoints, (ii) hypothesis- versus data-driven models, (iii) biological processes, (iv) contextualizing outcome measures, (v) clinical collaboration for device translation, (vi) modeling in the real world and (vii) clinical touchpoints across all stages of research. We conclude with our views on key avenues for future investment (clinical-research collaboration, new educational pathways, interdisciplinary engagement) to enable maximal translational value of computational modeling research in neurorehabilitation.

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/ .

Post-Stroke Brain Health Monitoring and Optimization: A Narrative Review

Significant advancements have been made in recent years in the acute treatment and secondary prevention of stroke. However, a large proportion of stroke survivors will go on to have enduring physical, cognitive, and psychological disabilities from suboptimal post-stroke brain health. Impaired brain health following stroke thus warrants increased attention from clinicians and researchers alike. In this narrative review based on an open timeframe search of the PubMed, Scopus, and Web of Science databases, we define post-stroke brain health and appraise the body of research focused on modifiable vascular, lifestyle, and psychosocial factors for optimizing post-stroke brain health. In addition, we make clinical recommendations for the monitoring and management of post-stroke brain health at major post-stroke transition points centered on four key intertwined domains: cognition, psychosocial health, physical functioning, and global vascular health. Finally, we discuss potential future work in the field of post-stroke brain health, including the use of remote monitoring and interventions, neuromodulation, multi-morbidity interventions, enriched environments, and the need to address inequities in post-stroke brain health. As post-stroke brain health is a relatively new, rapidly evolving, and broad clinical and research field, this narrative review aims to identify and summarize the evidence base to help clinicians and researchers tailor their own approach to integrating post-stroke brain health into their practices.

Sensitivity to Change and Responsiveness of the Upper Extremity Fugl-Meyer Assessment in Individuals With Moderate to Severe Acute Stroke

BACKGROUND

The Fugl-Meyer Assessment-Upper Extremity (FMA-UE) is a widely used outcome measure for quantifying motor impairment in stroke recovery. Meaningful change (responsiveness) in the acute to subacute phase of stroke recovery has not been determined.

OBJECTIVE

Determine responsiveness and sensitivity to change of the FMA-UE from 1-week to 6-weeks (subacute) after stroke in individuals with moderate to severe arm impairment who received standard clinical care.

METHODS

A total of 51 participants with resulting moderate and severe UE hemiparesis after stroke had FMA-UE assessment at baseline (within 2 weeks of stroke) and 6-weeks later. Sensitivity to change was assessed using Glass's delta, standardized response means (SRM), standard error of measure (SEM), and minimal detectable change (MDC). Responsiveness was assessed with the minimal clinically important difference (MCID), estimated using receiver operating characteristic curve analysis with patient-reported global rating of change scales (GROC) and a provider-reported modified Rankin Scale (mRS) as anchors.

RESULTS

The MCID estimates were 13, 12, and 9 anchored to the GROC Arm Weakness, GROC Recovery, and mRS. Glass's delta and the SRM revealed large effect sizes, indicating high sensitivity to change, (∆ = 1.24, 95% CI [0.64, 1.82], SRM = 1.10). Results for the SEM and MDC were 2.46 and 6.82, respectively.

CONCLUSION

The estimated MCID for the FMA-UE for individuals with moderate to severe motor impairment from 1 to 6-weeks after stroke is 13. These estimates will provide clinical context for FMA-UE change scores by helping to identify the change in upper-extremity motor impairment that is both beyond measurement error and clinically meaningful.

Staged use of ordinal and linear disability scales: a practical approach to granular assessment of acute stroke outcome

Background

The modified Rankin Scale (mRS) assessment of global disability is the most common primary endpoint in acute stroke trials but lacks granularity (7 broad levels) and is ordinal (scale levels unknown distances apart), which constrains study power. Disability scales that are linear and continuous may better discriminate outcomes, but computerized administration in stroke patients is challenging. We, therefore, undertook to develop a staged use of an ordinal followed by a linear scale practical to use in multicenter trials.

Methods

Consecutive patients undergoing 3-month final visits in the NIH FAST-MAG phase 3 trial were assessed with the mRS followed by 15 mRS level-specific yes-no items of the Academic Medical Center Linear Disability Score (ALDS), a linear disability scale derived using item response theory.

Results

Among 55 patients, aged 71.2 (SD ± 14.2), 67% were men and the entry NIHSS was 10.7 (SD ± 9.5). At 90 days, the median mRS score was 3 (IQR, 1-4), and the median ALDS score was 78.8 (IQR, 3.3-100). ALDS scores correlated strongly with 90 days outcome measures, including the Barthel Index (r = 0.92), NIHSS (r = 0.87), and mRS (r = 0.94). ALDS scores also correlated modestly with entry NIHSS (r = 0.38). At 90 days, the ALDS showed greater scale granularity than the mRS, with fewer patients with identical values, 1.9 (SD ± 3.2) vs. 8.0 (SD ± 3.6), p < 0.001. When treatment effect magnitudes were small to moderate, projected trial sample size requirements were 2-12-fold lower when the ALDS rather than the mRS was used as the primary trial endpoint.

Conclusion

Among patients enrolled in an acute neuroprotective stroke trial, the ALDS showed strong convergent validity and superior discrimination characteristics compared with the modified Rankin Scale and increased projected trial power to detect clinically meaningful treatment benefits.

The relevance of rich club regions for functional outcome post-stroke is enhanced in women

This study aimed to investigate the influence of stroke lesions in predefined highly interconnected (rich-club) brain regions on functional outcome post-stroke, determine their spatial specificity and explore the effects of biological sex on their relevance. We analyzed MRI data recorded at index stroke and ~3-months modified Rankin Scale (mRS) data from patients with acute ischemic stroke enrolled in the multisite MRI-GENIE study. Spatially normalized structural stroke lesions were parcellated into 108 atlas-defined bilateral (sub)cortical brain regions. Unfavorable outcome (mRS > 2) was modeled in a Bayesian logistic regression framework. Effects of individual brain regions were captured as two compound effects for (i) six bilateral rich club and (ii) all further non-rich club regions. In spatial specificity analyses, we randomized the split into "rich club" and "non-rich club" regions and compared the effect of the actual rich club regions to the distribution of effects from 1000 combinations of six random regions. In sex-specific analyses, we introduced an additional hierarchical level in our model structure to compare male and female-specific rich club effects. A total of 822 patients (age: 64.7[15.0], 39% women) were analyzed. Rich club regions had substantial relevance in explaining unfavorable functional outcome (mean of posterior distribution: 0.08, area under the curve: 0.8). In particular, the rich club-combination had a higher relevance than 98.4% of random constellations. Rich club regions were substantially more important in explaining long-term outcome in women than in men. All in all, lesions in rich club regions were associated with increased odds of unfavorable outcome. These effects were spatially specific and more pronounced in women.

Domain-Specific Outcome Measures in Clinical Trials of Therapies Promoting Stroke Recovery: A Suggested Blueprint

Different deficits recover to different degrees and with different time courses after stroke, indicating that plasticity differs across the brain's neural systems after stroke. To capture these differences, domain-specific outcome measures have received increased attention. Such measures have potential advantages over global outcome scales, which combine recovery across many domains into a single score and so blur the ability to capture individual measures of stroke recovery. Use of a global end point to rate disability can overlook substantial recovery in specific domains, such as motor or language, and may not differentiate between good and poor recovery for specific neurological domains. In light of these points, a blueprint is proposed for using domain-specific outcome measures in stroke recovery trials. Key steps include selecting a domain in the context of preclinical data, picking a domain-specific clinical trial end point, anchoring inclusion criteria to this end point, scoring this end point both before and after treatment, and then pursuing regulatory approval on the basis of the domain-specific results. This blueprint is intended to foster clinical trials that, by using domain-specific end points, are able to demonstrate favorable results in clinical trials of therapies that promote stroke recovery.

SMS-text messaging for collecting outcome measures after acute stroke

Introduction

Traditional methods for obtaining outcomes for patients after acute stroke are resource-intensive. This study aimed to examine the feasibility, reliability, cost, and acceptability of collecting outcomes after acute stroke with a short message service (SMS)-text messaging program.

Methods

Patients were enrolled in an SMS-text messaging program at acute stroke hospitalization discharge. Participants were prompted to complete assessments including the modified Rankin scale (mRS) and Patient-Reported Outcomes Measurement (PROM) Information System Global-10 at 30, 60, and 90 days postdischarge via SMS-text. Agreement and cost of SMS-text data collection were compared to those obtained from traditional follow-up methods (via phone or in the clinic). Participant satisfaction was surveyed upon program conclusion.

Results

Of the 350 patients who agreed to receive SMS texts, 40.5% responded to one or more assessments. Assessment responders were more likely to have English listed as their preferred language (p = 0.009), have a shorter length of hospital stay (p = 0.01), lower NIH stroke scale upon admission (p < 0.001), and be discharged home (p < 0.001) as compared to nonresponders. Weighted Cohen's kappa revealed that the agreement between SMS texting and traditional methods was almost perfect for dichotomized (good vs. poor) (κ = 0.8) and ordinal levels of the mRS score (κ = 0.8). Polychoric correlations revealed a significant association for PROM scores ( ρ  = 0.4, p < 0.01 and ρ  = 0.4, p < 0.01). A cost equation showed that gathering outcomes via SMS texting would be less costly than phone follow-up for cohorts with more than 181 patients. Nearly all participants (91%) found the program acceptable and not burdensome (94%), and most (53%) felt it was helpful. Poststroke outcome data collection via SMS texting is feasible, reliable, low-cost, and acceptable. Reliability was higher for functional outcomes as compared to PROMs.

Conclusions

While further validation is required, our findings suggest that SMS texting is a feasible method for gathering outcomes after stroke at scale to evaluate the efficacy of acute stroke treatments.

Deep profiling of multiple ischemic lesions in a large, multi-center cohort: Frequency, spatial distribution, and associations to clinical characteristics

Background purpose

A substantial number of patients with acute ischemic stroke (AIS) experience multiple acute lesions (MAL). We here aimed to scrutinize MAL in a large radiologically deep-phenotyped cohort.

Materials and methods

Analyses relied upon imaging and clinical data from the international MRI-GENIE study. Imaging data comprised both Fluid-attenuated inversion recovery (FLAIR) for white matter hyperintensity (WMH) burden estimation and diffusion-weighted imaging (DWI) sequences for the assessment of acute stroke lesions. The initial step featured the systematic evaluation of occurrences of MAL within one and several vascular supply territories. Associations between MAL and important imaging and clinical characteristics were subsequently determined. The interaction effect between single and multiple lesion status and lesion volume was estimated by means of Bayesian hierarchical regression modeling for both stroke severity and functional outcome.

Results

We analyzed 2,466 patients (age = 63.4 ± 14.8, 39% women), 49.7% of which presented with a single lesion. Another 37.4% experienced MAL in a single vascular territory, while 12.9% featured lesions in multiple vascular territories. Within most territories, MAL occurred as frequently as single lesions (ratio ∼1:1). Only the brainstem region comprised fewer patients with MAL (ratio 1:4). Patients with MAL presented with a significantly higher lesion volume and acute NIHSS (7.7 vs. 1.7 ml and 4 vs. 3, p FDR < 0.001). In contrast, patients with a single lesion were characterized by a significantly higher WMH burden (6.1 vs. 5.3 ml, p FDR = 0.048). Functional outcome did not differ significantly between patients with single versus multiple lesions. Bayesian analyses suggested that the association between lesion volume and stroke severity between single and multiple lesions was the same in case of anterior circulation stroke. In case of posterior circulation stroke, lesion volume was linked to a higher NIHSS only among those with MAL.

Conclusion

Multiple lesions, especially those within one vascular territory, occurred more frequently than previously reported. Overall, multiple lesions were distinctly linked to a higher acute stroke severity, a higher total DWI lesion volume and a lower WMH lesion volume. In posterior circulation stroke, lesion volume was linked to a higher stroke severity in multiple lesions only.