How to gain evidence in neurorehabilitation: a personal view

Neurorehabilitation is an emerging field driven by developments in neuroscience and biomedical engineering. Most patients that require neurorehabilitation have had a stroke, but other diseases of the brain, spinal cord, or nerves can also be alleviated. Modern therapies in neurorehabilitation focus on reducing impairment and improving function in daily life. As compared with acute care medicine, the clinical evidence for most neurorehabilitative treatments (modern or conventional) is sparse. Clinical trials support constraint-induced movement therapy for the arm and aerobic treadmill training for walking, both high-intensity interventions requiring therapist time (i.e., cost) and patient motivation. Promising approaches for the future include robotic training, telerehabilitation at the patient's home, and supportive therapies that promote motivation and compliance. It is argued that a better understanding of the neuroscience of recovery together with results from small-scale and well-focused clinical experiments are necessary to design optimal interventions for specific target groups of patient

Cortical Plasticity during Motor Learning and Recovery after Ischemic Stroke

The motor system has the ability to adapt to environmental constraints and injury to itself. This adaptation is often referred to as a form of plasticity allowing for livelong acquisition of new movements and for recovery after stroke. We are not sure whether learning and recovery work via same or similar neural mechanisms. But, all these processes require widespread changes within the matrix of the brain. Here, basic mechanisms of these adaptations on the level of cortical circuitry and networks are reviewed. We focus on the motor cortices because their role in learning and recovery has been investigated more thoroughly than other brain regions

External Validation of the Early Prediction of Functional Outcome After Stroke Prediction Model for Independent Gait at 3 Months After Stroke

INTRODUCTION: The Early Prediction of Functional Outcome after Stroke (EPOS) model for independent gait is a tool to predict between days 2 and 9 poststroke whether patients will regain independent gait 6 months after stroke. External validation of the model is important to determine its clinical applicability and generalizability by testing its performance in an independent cohort. Therefore, this study aimed to perform a temporal and geographical external validation of the EPOS prediction model for independent gait after stroke but with the endpoint being 3 months instead of the original 6 months poststroke. METHODS: Two prospective longitudinal cohort studies consisting of patients with first-ever stroke admitted to a Swiss hospital stroke unit. Sitting balance and strength of the paretic leg were tested at days 1 and 8 post-stroke in Cohort I and at days 3 and 9 in Cohort II. Independent gait was assessed 3 months after symptom onset. The performance of the model in terms of discrimination (area under the receiver operator characteristic (ROC) curve; AUC), classification, and calibration was assessed. RESULTS: In Cohort I [N = 39, median age: 74 years, 33% women, median National Institutes of Health Stroke Scale (NIHSS) 9], the AUC (95% confidence interval (CI)] was 0.675 (0.510, 0.841) on day 1 and 0.921 (0.811, 1.000) on day 8. For Cohort II (N = 78, median age: 69 years, 37% women, median NIHSS 8), this was 0.801 (0.684, 0.918) on day 3 and 0.846 (0.741, 0.951) on day 9. DISCUSSION AND CONCLUSION: External validation of the EPOS prediction model for independent gait 3 months after stroke resulted in an acceptable performance from day 3 onward in mild-to-moderately affected patients with first-ever stroke without severe prestroke disability. The impact of applying this model in clinical practice should be investigated within this subgroup of patients with stroke. To improve the generalizability of patients with recurrent stroke and those with more severe, neurological comorbidities, the performance of the EPOS model within these patients should be determined across different geographical areas

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

Neurovascular disease in Switzerland: 10-year trends show non-traditional risk factors on the rise and higher exposure in women

BACKGROUND AND PURPOSE Effective risk factor modification is the prerequisite to prevent neurovascular disease such as stroke or vascular dementia. Non-traditional vascular risk factors (nt-vrfs) including stress significantly add to the risk of neurovascular disease arising from traditional vascular risk factors (t-vrfs). In order to discover sex-specific changes that may underlie previously reported inclines in the prevalence of neurovascular and cardiovascular disease in women, 10-year trends in the prevalence of vrfs in Switzerland were assessed. METHODS Anonymized data from 22,134 participants (51% women) of the governmental Swiss Health Survey, performed every 5 years (2007, 2012 and 2017), were obtained. Epidemiological parameters, t-vrfs and nt-vrfs were analyzed in a cross-sectional study design. RESULTS Over the observation period, the number of women having full-time jobs increased considerably (2007 38%, 2012 39%, 2017 44%). This was accompanied by a substantial rise in the prevalence of nt-vrfs in women and men including stress at work (2007, not available; 2012 women/men 58%/60%; 2017 women/men 66%/65%), low locus of control (women/men: 2007 21%/19%, 2012 22%/19%, 2017 25%/22%) and sleep disorders (women/men: 2007 30%/22%, 2012 28%/20%, 2017 32%/26%). Amongst t-vrfs, only the prevalence of obesity and hypercholesterolemia increased over time in both sexes, whilst other t-vrfs remained stable (hypertension [27%], diabetes [5%]) or decreased (smoking [9.4 cigarettes/day]). CONCLUSIONS A rise in women's economic participation alongside a higher affection with nt-vrfs in the female Swiss population emphasizes the need to improve vascular risk stratification and implement effective preventive measures for neurovascular and cardiovascular disease

An Unusual Cause of Pseudomedian Nerve Palsy

We describe a patient who presented with an acute paresis of her distal right hand suggesting a peripheral median nerve lesion. However, on clinical examination a peripheral origin could not be verified, prompting further investigation. Diffusion-weighted magnetic resonance imaging revealed an acute ischaemic lesion in the hand knob area of the motor cortex. Isolated hand palsy in association with cerebral infarction has been reported occasionally. However, previously reported cases presented predominantly as ulnar or radial palsy. In this case report, we present a rather rare finding of an acute cerebral infarction mimicking median never palsy

Ischemic stroke in COVID-19 patients: Mechanisms, treatment, and outcomes in a consecutive Swiss Stroke Registry analysis

BACKGROUND: Most case series of patients with ischemic stroke (IS) and COVID-19 are limited to selected centers or lack 3-month outcomes. The aim of this study was to describe the frequency, clinical and radiological features, and 3-month outcomes of patients with IS and COVID-19 in a nationwide stroke registry. METHODS: From the Swiss Stroke Registry (SSR), we included all consecutive IS patients ≥18 years admitted to Swiss Stroke Centers or Stroke Units during the first wave of COVID-19 (25 February to 8 June 2020). We compared baseline features, etiology, and 3-month outcome of SARS-CoV-2 polymerase chain reaction-positive (PCR+) IS patients to SARS-CoV-2 PCR- and/or asymptomatic non-tested IS patients. RESULTS: Of the 2341 IS patients registered in the SSR during the study period, 36 (1.5%) had confirmed COVID-19 infection, of which 33 were within 1 month before or after stroke onset. In multivariate analysis, COVID+ patients had more lesions in multiple vascular territories (OR 2.35, 95% CI 1.08-5.14, p = 0.032) and fewer cryptogenic strokes (OR 0.37, 95% CI 0.14-0.99, p = 0.049). COVID-19 was judged the likely principal cause of stroke in 8 patients (24%), a contributing/triggering factor in 12 (36%), and likely not contributing to stroke in 13 patients (40%). There was a strong trend towards worse functional outcome in COVID+ patients after propensity score (PS) adjustment for age, stroke severity, and revascularization treatments (PS-adjusted common OR for shift towards higher modified Rankin Scale (mRS) = 1.85, 95% CI 0.96-3.58, p = 0.07). CONCLUSIONS: In this nationwide analysis of consecutive ischemic strokes, concomitant COVID-19 was relatively rare. COVID+ patients more often had multi-territory stroke and less often cryptogenic stroke, and their 3-month functional outcome tended to be worse

Classification of functional and non-functional arm use by inertial measurement units in individuals with upper limb impairment after stroke

Background: Arm use metrics derived from wrist-mounted movement sensors are widely used to quantify the upper limb performance in real-life conditions of individuals with stroke throughout motor recovery. The calculation of real-world use metrics, such as arm use duration and laterality preferences, relies on accurately identifying functional movements. Hence, classifying upper limb activity into functional and non-functional classes is paramount. Acceleration thresholds are conventionally used to distinguish these classes. However, these methods are challenged by the high inter and intra-individual variability of movement patterns. In this study, we developed and validated a machine learning classifier for this task and compared it to methods using conventional and optimal thresholds. Methods: Individuals after stroke were video-recorded in their home environment performing semi-naturalistic daily tasks while wearing wrist-mounted inertial measurement units. Data were labeled frame-by-frame following the Taxonomy of Functional Upper Limb Motion definitions, excluding whole-body movements, and sequenced into 1-s epochs. Actigraph counts were computed, and an optimal threshold for functional movement was determined by receiver operating characteristic curve analyses on group and individual levels. A logistic regression classifier was trained on the same labels using time and frequency domain features. Performance measures were compared between all classification methods. Results: Video data (6.5 h) of 14 individuals with mild-to-severe upper limb impairment were labeled. Optimal activity count thresholds were ≥20.1 for the affected side and ≥38.6 for the unaffected side and showed high predictive power with an area under the curve (95% CI) of 0.88 (0.87,0.89) and 0.86 (0.85, 0.87), respectively. A classification accuracy of around 80% was equivalent to the optimal threshold and machine learning methods and outperformed the conventional threshold by ∼10%. Optimal thresholds and machine learning methods showed superior specificity (75-82%) to conventional thresholds (58-66%) across unilateral and bilateral activities. Conclusion: This work compares the validity of methods classifying stroke survivors' real-life arm activities measured by wrist-worn sensors excluding whole-body movements. The determined optimal thresholds and machine learning classifiers achieved an equivalent accuracy and higher specificity than conventional thresholds. Our open-sourced classifier or optimal thresholds should be used to specify the intensity and duration of arm use