Prediction of motor recovery after stroke: being pragmatic or innovative?

Increased dual-task interference during upper limb movements in stroke exceeding that found in aging - a systematic review and meta-analysis

Objective

To determine whether dual-task interference during upper limb tasks is increased in patients after stroke compared to healthy older subjects and to compare magnitude of stroke-induced change in interference to that explained by aging.

Methods

We conducted a systematic literature search in MEDLINE, CINAHL, Google Scholar and PEDro databases up to October 2023 for studies on upper limb dual-tasks in stroke and elderly healthy subjects. Eleven upper limb dual-task studies in stroke patients and 11 studies in healthy older subjects were identified and systematically reviewed. A meta-analysis was performed on seven stroke studies and on five studies in healthy older subjects that included control groups.

Results

Most stroke studies investigated proximal arm movements with kinematic measures, but few studies evaluated manual dexterity. In contrast, studies in healthy older subjects used more distal (finger tapping) tasks. The meta-analysis showed that stroke patients had on average a 19% (CI 95% = 1.0-37.3) increase in dual-task interference compared to age-matched healthy controls (Z = 2.06, p = 0.04). Older healthy subjects showed greater dual-task interference compared to younger subjects (19% greater, CI 95% = 6.5-31.2, Z = 2.98, p = 0.003).

Conclusion

Meta-analysis revealed an increase in dual-task interference during upper limb movements in stroke patients, exceeding age-related changes, supporting the presence of subclinical impairments in divided attention post-stroke that may impede motor recovery.

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.

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.

Clinical diagnostic utility of transcranial magnetic stimulation in neurological disorders. Updated report of an IFCN committee

The review provides a comprehensive update (previous report: Chen R, Cros D, Curra A, Di Lazzaro V, Lefaucheur JP, Magistris MR, et al. The clinical diagnostic utility of transcranial magnetic stimulation: report of an IFCN committee. Clin Neurophysiol 2008;119(3):504-32) on clinical diagnostic utility of transcranial magnetic stimulation (TMS) in neurological diseases. Most TMS measures rely on stimulation of motor cortex and recording of motor evoked potentials. Paired-pulse TMS techniques, incorporating conventional amplitude-based and threshold tracking, have established clinical utility in neurodegenerative, movement, episodic (epilepsy, migraines), chronic pain and functional diseases. Cortical hyperexcitability has emerged as a diagnostic aid in amyotrophic lateral sclerosis. Single-pulse TMS measures are of utility in stroke, and myelopathy even in the absence of radiological changes. Short-latency afferent inhibition, related to central cholinergic transmission, is reduced in Alzheimer's disease. The triple stimulation technique (TST) may enhance diagnostic utility of conventional TMS measures to detect upper motor neuron involvement. The recording of motor evoked potentials can be used to perform functional mapping of the motor cortex or in preoperative assessment of eloquent brain regions before surgical resection of brain tumors. TMS exhibits utility in assessing lumbosacral/cervical nerve root function, especially in demyelinating neuropathies, and may be of utility in localizing the site of facial nerve palsies. TMS measures also have high sensitivity in detecting subclinical corticospinal lesions in multiple sclerosis. Abnormalities in central motor conduction time or TST correlate with motor impairment and disability in MS. Cerebellar stimulation may detect lesions in the cerebellum or cerebello-dentato-thalamo-motor cortical pathways. Combining TMS with electroencephalography, provides a novel method to measure parameters altered in neurological disorders, including cortical excitability, effective connectivity, and response complexity.

Performance-Based Robotic Training in Individuals with Subacute Stroke: Differences between Responders and Non-Responders

The high variability of upper limb motor recovery with robotic training (RT) in subacute stroke underscores the need to explore differences in responses to RT. We explored differences in baseline characteristics and the RT dose between responders (ΔFugl-Meyer Assessment (FMA) score ≥ 9 points; n = 20) and non-responders (n = 16) in people with subacute stroke (mean [SD] poststroke time at baseline, 54 (26) days, baseline FMA score, 23 (17) points) who underwent 16 RT sessions combined with conventional therapies. Baseline characteristics were compared between groups. During RT sessions, the actual practice time (%), number of movements performed, and total distance covered (cm) in assisted and unassisted modalities were compared between groups. At baseline, participant characteristics and FMA scores did not differ between groups. During the RT, non-responders increased practice time (+15%; p = 0.02), performed more movements (+285; p = 0.004), and covered more distance (+4037 cm; p < 10^-3), with no difference between physical modalities. In contrast, responders decreased practice time (-21%; p = 0.01) and performed fewer movements (-338; p = 0.03) in the assisted modality while performing more movements (+328; p < 0.05) and covering a greater distance (+4779 cm; p = 0.01) in unassisted modalities. Despite a large amount of motor practice, motor outcomes did not improve in non-responders compared to responders: the difficulty level in RT may have been too low for them. Future studies should combine robot-based parameters to describe the treatment dose, especially in people with severe-to-moderate arm paresis, to optimize the RT and improve the recovery prognosis.

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.

Predicting later categories of upper limb activity from earlier clinical assessments following stroke: an exploratory analysis

BACKGROUND

Accelerometers allow for direct measurement of upper limb (UL) activity. Recently, multi-dimensional categories of UL performance have been formed to provide a more complete measure of UL use in daily life. Prediction of motor outcomes after stroke have tremendous clinical utility and a next step is to explore what factors might predict someone's subsequent UL performance category.

PURPOSE

To explore how different machine learning techniques can be used to understand how clinical measures and participant demographics captured early after stroke are associated with the subsequent UL performance categories.

METHODS

This study analyzed data from two time points from a previous cohort (n = 54). Data used was participant characteristics and clinical measures from early after stroke and a previously established category of UL performance at a later post stroke time point. Different machine learning techniques (a single decision tree, bagged trees, and random forests) were used to build predictive models with different input variables. Model performance was quantified with the explanatory power (in-sample accuracy), predictive power (out-of-bag estimate of error), and variable importance.

RESULTS

A total of seven models were built, including one single decision tree, three bagged trees, and three random forests. Measures of UL impairment and capacity were the most important predictors of the subsequent UL performance category, regardless of the machine learning algorithm used. Other non-motor clinical measures emerged as key predictors, while participant demographics predictors (with the exception of age) were generally less important across the models. Models built with the bagging algorithms outperformed the single decision tree for in-sample accuracy (26-30% better classification) but had only modest cross-validation accuracy (48-55% out of bag classification).

CONCLUSIONS

UL clinical measures were the most important predictors of the subsequent UL performance category in this exploratory analysis regardless of the machine learning algorithm used. Interestingly, cognitive and affective measures emerged as important predictors when the number of input variables was expanded. These results reinforce that UL performance, in vivo, is not a simple product of body functions nor the capacity for movement, instead being a complex phenomenon dependent on many physiological and psychological factors. Utilizing machine learning, this exploratory analysis is a productive step toward the prediction of UL performance. Trial registration NA.

The Effect of Action Observation Combined with Motor Imagery Training on Upper Extremity Function and Corticospinal Excitability in Stroke Patients: A Randomized Controlled Trial

This study aimed to investigate the effect of motor imagery (MI) intervention with action observation (AO) on upper extremity function and corticospinal activation in stroke patients. MI and AO are two forms of motion simulation that activate the motor system without physical activity. There are many studies that show the effect of AO and MI alone, but there are few studies in parallel. This study was conducted on 45 patients who were divided into an experimental group (n = 22) and a control group (n = 23) using a computer randomization program. The experimental group conducted AO with MI, and the control group only AO. All participants received interventions for twenty-five minutes per session, five times a week, for eight weeks. For the pre- and post-evaluation of all participants, motor evoked potential (MEP) amplitude was measured to compare corticospinal activation, and Fugl-Meyer Assessment Upper Extremity (FMA UE), Wolf Motor Function Test (WMFT) and Motor Activity Log (MAL) were evaluated for changes in upper extremity function. In comparing the amount of change before and after the intervention, a significant change was observed in the experimental group's MEP amplitude. In the comparison between groups after the intervention and the amount of change before and after the intervention, the experimental group showed significant changes in FMA UE and MAL Amount of Use (MAL AOU) items. As a result of this study, AO with MI is effective in enhancing upper extremity function and increasing cortical spinal cord activation in patients with severe stroke with limited movement.

Brain Functional Topology Alteration in Right Lateral Occipital Cortex Is Associated With Upper Extremity Motor Recovery

Stroke is a chief cause of sudden brain damage that severely disrupts the whole-brain network. However, the potential mechanisms of motor recovery after stroke are uncertain and the prognosis of poststroke upper extremity recovery is still a challenge. This study investigated the global and local topological properties of the brain functional connectome in patients with subacute ischemic stroke and their associations with the clinical measurements. A total of 57 patients, consisting of 29 left-sided and 28 right-sided stroke patients, and 32 age- and gender-matched healthy controls (HCs) were recruited to undergo a resting-state functional magnetic resonance imaging (rs-fMRI) study; patients were also clinically evaluated with the Upper Extremity Fugl-Meyer Assessment (FMA_UE). The assessment was repeated at 15 weeks to assess upper extremity functional recovery for the patient remaining in the study (12 left- 20 right-sided stroke patients). Global graph topological disruption indices of stroke patients were significantly decreased compared with HCs but these indices were not significantly associated with FMA_UE. In addition, local brain network structure of stroke patients was altered, and the altered regions were dependent on the stroke site. Significant associations between local degree and motor performance and its recovery were observed in the right lateral occipital cortex (R LOC) in the right-sided stroke patients. Our findings suggested that brain functional topologies alterations in R LOC are promising as prognostic biomarkers for right-sided subacute stroke. This cortical area might be a potential target to be further validated for non-invasive brain stimulation treatment to improve poststroke upper extremity recovery.

Outcome Prediction of Patients with Intracerebral Hemorrhage by Measurement of Lesion Volume in the Corticospinal Tract on Computed Tomography

Objective:

This study investigated the potential utility of computed tomography for outcome prediction in patients with intracerebral hemorrhage.

Methods:

Patients with putaminal and/or thalamic hemorrhage for whom computed tomography images were acquired in our hospital emergency room soon after onset were retrospectively enrolled. Outcome measurements were obtained at discharge from the convalescent rehabilitation ward of our affiliated hospital. Hemiparesis was evaluated using the total score of the motor component of the Stroke Impairment Assessment Set (SIAS-motor; null to full, 0 to 25), the motor component of the Functional Independence Measure (FIM-motor; null to full, 13 to 91), and the total length of hospital stay. After registration of the computed tomography images to the standard brain, the volumes of the hematoma lesions located in the corticospinal tract were calculated. The correlation between the corticospinal tract lesion volumes and the outcome measurements was assessed using Spearman’s rank correlation test.

Results:

Thirty patients were entered into the final analytical database. Corticospinal tract lesion volumes ranged from 0.002 to 4.302 ml (median, 1.478). SIAS-motor scores ranged from 0 to 25 (median, 20), FIM-motor scores ranged from 15 to 91 (median, 80.5), and the total length of hospital stay ranged from 31 to 194 days (median, 106.5). All correlation tests were statistically significant (P <0.01). The strongest correlation was for SIAS-motor total (R=–0.710), followed by FIM-motor (R=–0.604) and LOS (R=0.493).

Conclusions:

These findings suggest that conventional computed tomography images may be useful for outcome prediction in patients with intracerebral hemorrhage.

Transcranial magnetic stimulation implementation on stroke prognosis

INTRODUCTION

Stroke represents a major cause of functional disability with increasing prevalence. Thus, it is imperative that stroke prognosis be both timely and valid. Up to today, several biomarkers have been investigated in an attempt to forecast stroke survivors' potential for motor recovery, transcranial magnetic stimulation (TMS) being among them.

METHODS

A literature research of two databases (MEDLINE and Scopus) was conducted in order to trace all relevant studies published between 1990 and 2021 that focused on the potential utility of TMS implementation on stroke prognosis. Only full-text articles published in the English language were included.

RESULTS

Thirty-nine articles have been traced and included in this review.

DISCUSSION

Motor evoked potentials (MEPs) recording is indicative of a favorable prognosis concerning the motor recovery of upper and lower extremities' weakness, swallowing and speech difficulties, and the patient's general functional outcome. On the contrary, MEP absence is usually associated with poor prognosis. Relative correlations have also been made among other TMS variants (motor threshold, MEP amplitude, central motor conduction time) and the expected recovery rate. Overall, TMS represents a non-invasive, fast, safe, and reproducible prognostic tool poststroke that could resolve prognostic uncertainties in cases of stroke.

Accuracy of an Algorithm in Predicting Upper Limb Functional Capacity in a United States Population

OBJECTIVE

To determine the accuracy of an algorithm, using clinical measures only, on a sample of persons with first-ever stroke in the United States (US). It was hypothesized that algorithm accuracy would fall in a range of 70%-80%.

DESIGN

Secondary analysis of prospective, observational, longitudinal cohort; 2 assessments were done: (1) within 48 hours to 1 week poststroke and (2) at 12 weeks poststroke.

SETTING

Recruited from a large acute care hospital and followed over the first 6 months after stroke.

PARTICIPANTS

Adults with first-ever stroke (N=49) with paresis of the upper limb (UL) at ≤48 hours who could follow 2-step commands and were expected to return to independent living at 6 months.

INTERVENTION

Not applicable.

MAIN OUTCOME MEASURES

The overall accuracy of the algorithm with clinical measures was quantified by comparing predicted (expected) and actual (observed) categories using a correct classification rate.

RESULTS

The overall accuracy (61%) and weighted κ (62%) were significant. Sensitivity was high for the Excellent (95%) and Poor (81%) algorithm categories. Specificity was high for the Good (82%), Limited (98%), and Poor (95%) categories. Positive predictive value (PPV) was high for Poor (82%) and negative predictive value (NPV) was high for all categories. No differences in participant characteristics were found between those with accurate or inaccurate predictions.

CONCLUSIONS

The results of the present study found that use of an algorithm with clinical measures only is better than chance alone (chance=25% for each of the 4 categories) at predicting a category of UL capacity at 3 months post troke. The moderate to high values of sensitivity, specificity, PPV, and NPV demonstrates some clinical utility of the algorithm within health care settings in the US.

Implementing the PREP2 Algorithm to Predict Upper Limb Recovery Potential After Stroke in Clinical Practice: A Qualitative Study

OBJECTIVE

Predicting motor recovery after stroke is a key factor when planning and providing rehabilitation for individual patients. The Predict REcovery Potential (PREP2) prediction tool was developed to help clinicians predict upper limb functional outcome. In parallel to further model validation, the purpose of this study was to explore how PREP2 was implemented in clinical practice within the Auckland District Health Board (ADHB) in New Zealand.

METHODS

In this case study design using semi-structured interviews, 19 interviews were conducted with clinicians involved in stroke care at ADHB. To explore factors influencing implementation, interview content was coded and analyzed using the consolidated framework for implementation research. Strategies identified by the Expert Recommendations for Implementing Change Project were used to describe how implementation was undertaken.

RESULTS

Implementation of PREP2 was initiated and driven by therapists. Key factors driving implementation were as follows: the support given to staff from the implementation team; the knowledge, beliefs, and self-efficacy of staff; and the perceived benefits of having PREP2 prediction information. Twenty-six Expert Recommendations for Implementing Change strategies were identified relating to 3 areas: implementation team, clinical/academic partnerships, and training.

CONCLUSIONS

The PREP2 prediction tool was successfully implemented in clinical practice at ADHB. Barriers and facilitators to implementation success were identified, and implementation strategies were described. Lessons learned can aid future development and implementation of prediction models in clinical practice.

IMPACT

Translating evidence-based interventions into clinical practice can be challenging and slow; however, shortly after its local validation, PREP2 was successfully implemented into clinical practice at the same site in New Zealand. In parallel to further model validation, organizations and practices can glean useful lessons to aid future implementation.

Virtual Rehabilitation of the Paretic Hand and Arm in Persons With Stroke: Translation From Laboratory to Rehabilitation Centers and the Patient's Home

The anatomical and physiological heterogeneity of strokes and persons with stroke, along with the complexity of normal upper extremity movement make the possibility that any single treatment approach will become the definitive solution for all persons with upper extremity hemiparesis due to stroke unlikely. This situation and the non-inferiority level outcomes identified by many studies of virtual rehabilitation are considered by some to indicate that it is time to consider other treatment modalities. Our group, among others, has endeavored to build on the initial positive outcomes in studies of virtual rehabilitation by identifying patient populations, treatment settings and training schedules that will best leverage virtual rehabilitation's strengths. We feel that data generated by our lab and others suggest that (1) persons with stroke may adapt to virtual rehabilitation of hand function differently based on their level of impairment and stage of recovery and (2) that less expensive, more accessible home based equipment seems to be an effective alternative to clinic based treatment that justifies continued optimism and study.

Sensor-based categorization of upper limb performance in daily life of persons with and without neurological upper limb deficits

Background: The use of wearable sensor technology (e. g., accelerometers) for tracking human physical activity have allowed for measurement of actual activity performance of the upper limb (UL) in daily life. Data extracted from accelerometers can be used to quantify multiple variables measuring different aspects of UL performance in one or both limbs. A limitation is that several variables are needed to understand the complexity of UL performance in daily life.

Purpose: To identify categories of UL performance in daily life in adults with and without neurological UL deficits.

Methods: This study analyzed data extracted from bimanual, wrist-worn triaxial accelerometers from adults from three previous cohorts (N = 211), two samples of persons with stroke and one sample from neurologically intact adult controls. Data used in these analyses were UL performance variables calculated from accelerometer data, associated clinical measures, and participant characteristics. A total of twelve cluster solutions (3-, 4-, or 5-clusters based with 12, 9, 7, or 5 input variables) were calculated to systematically evaluate the most parsimonious solution. Quality metrics and principal component analysis of each solution were calculated to arrive at a locally-optimal solution with respect to number of input variables and number of clusters.

Results: Across different numbers of input variables, two principal components consistently explained the most variance. Across the models with differing numbers of UL input performance variables, a 5-cluster solution explained the most overall total variance (79%) and had the best model-fit.

Conclusion: The present study identified 5 categories of UL performance formed from 5 UL performance variables in cohorts with and without neurological UL deficits. Further validation of both the number of UL performance variables and categories will be required on a larger, more heterogeneous sample. Following validation, these categories may be used as outcomes in UL stroke research and implemented into rehabilitation clinical practice.

Does Measurement of Corticospinal Tract Involvement Add Value to Clinical Behavioral Biomarkers in Predicting Motor Recovery after Stroke?

Background

The prediction of motor recovery after stroke is an important issue, and various prediction models have been proposed using either clinical behavioral or neurological biomarkers. This study sought to identify the effects of clinical behavioral biomarkers combined with corticospinal tract (CST) injury measurement on the prediction of motor recovery after stroke.

Methods

The region of interest was drawn on the normalized brain magnetic resonance imaging scans of patients with first-ever unilateral hemispheric stroke, and the degree of CST injury was calculated in a total of 67 such subjects. Patients who had initial minor deficits and showed a ceiling effect on motor recovery were excluded. To predict the follow-up Fugl-Meyer assessment (FMA) scores, correlation and regression analyses were performed using various clinical behavioral biomarkers, including age, sex, lesion location, and initial FMA scores and CST injury measurements.

Results

Only the initial FMA-upper extremity (UE) score was statistically correlated with the follow-up FMA-UE score at ≥2 months after the onset (adjusted R 2 = 0.626), and the relationship between CST injury and follow-up FMA-UE score was unclear (n = 53). Hierarchical clustering between the initial and follow-up FMA-UE scores showed three clusters. After exclusion of a cluster with an initial FMA-UE ≥ 35, the prediction of the follow-up FMA-UE score was possible by incorporating the initial FMA-UE score and CST injury measurements (n = 39). However, the explanatory power decreased (adjusted R 2 = 0.445), and the unique contribution of the CST injury (10.1%) was lower than that of the initial FMA-UE score (26.7%). With respect to the FMA-lower extremity score, CST injury was not related to recovery.

Conclusions

Motor recovery of the upper and lower extremities after stroke could be predicted using the initial FMA score. CST injury was significant for the prediction of motor recovery of the upper extremity in patients with severe initial motor deficits (FMA-UE < 35); however, its portion of prediction of motor recovery was low. The prediction of poststroke motor recovery using the initial motor deficit was not improved by the addition of CST injury measurements.