Early Rehabilitation After Stroke: a Narrative Review

My god, no understanding of the neuronal cascade of death in the first days at all. The therapy initiated in the first 24 hours doesn't cause harm. You fuckers need to understand cause and effect.  This lack of knowledge is appalling
https://link.springer.com/article/10.1007/s11883-017-0686-6

• Authors
• Authors and affiliations

• Elisheva R. ColemanEmail author
• Rohitha Moudgal
• Kathryn Lang
• Hyacinth I. Hyacinth
• Oluwole O. Awosika
• Brett M. Kissela
• Wuwei Feng

• Elisheva R. Coleman
  • 1
  Email author
• Rohitha Moudgal
  • 2
• Kathryn Lang
  • 3
• Hyacinth I. Hyacinth
  • 4
• Oluwole O. Awosika
  • 1
• Brett M. Kissela
  • 1
• Wuwei Feng
  • 5

1. 1.Department of Neurology and Rehabilitation MedicineUniversity of Cincinnati Gardner Neuroscience InstituteCincinnatiUSA
2. 2.University of Cincinnati College of MedicineCincinnatiUSA
3. 3.Department of Rehabilitation ServicesUniversity of CincinnatiCincinnatiUSA
4. 4.Aflac Cancer and Blood Disorder Center of Children’s Healthcare of Atlanta and Emory University Department of PediatricsAtlantaUSA
5. 5.Department of NeurologyMedical University of South CarolinaCharlestonUSA

Cardiovascular Disease and Stroke (S. Prabhakaran, Section Editor)

First Online: 07 November 2017

Part of the following topical collections:

1. Topical Collection on Cardiovascular Disease and Stroke

Abstract

Purpose of Review

Despite current rehabilitative strategies, stroke remains a leading cause of disability in the USA. There is a window of enhanced neuroplasticity early after stroke, during which the brain’s dynamic response to injury is heightened and rehabilitation might be particularly effective. This review summarizes the evidence of the existence of this plastic window, and the evidence regarding safety and efficacy of early rehabilitative strategies for several stroke domain-specific deficits.

Recent Findings

Overall, trials of rehabilitation in the first 2 weeks after stroke are scarce. In the realm of very early mobilization, one large and one small trial found potential harm from mobilizing patients within the first 24 h after stroke, and only one small trial found benefit in doing so. For the upper extremity, constraint-induced movement therapy appears to have benefit when started within 2 weeks of stroke. Evidence for non-invasive brain stimulation in the acute period remains scant and inconclusive. For aphasia, the evidence is mixed, but intensive early therapy might be of benefit for patients with severe aphasia. Mirror therapy begun early after stroke shows promise for the alleviation of neglect. Novel approaches to treating dysphagia early after stroke appear promising, but the high rate of spontaneous improvement makes their benefit difficult to gauge.

Summary

The optimal time to begin rehabilitation after a stroke remains unsettled, though the evidence is mounting that for at least some deficits, initiation of rehabilitative strategies within the first 2 weeks of stroke is beneficial. Commencing intensive therapy in the first 24 h may be harmful.

Combined arm stretch positioning and neuromuscular electrical stimulation during rehabilitation does not improve range of motion, shoulder pain or function in patients after stroke: a randomised trial

Well fuck, was this research from Nov. 2013 not good enough to answer the question? And you wasted time and money on this? I'm thinking that NO one in stroke knows anything about previous research, I blame the mentors and senior researchers for allowing this. A database would solve this lack of knowledge of previous research.

Oops, it is the same research, it just showed up in one of my feeds again. 

Combined arm stretch positioning and neuromuscular electrical stimulation during rehabilitation does not improve range of motion, shoulder pain or function in patients after stroke: a randomised trial Nov. 2013

Combined arm stretch positioning and neuromuscular electrical stimulation during rehabilitation does not improve range of motion, shoulder pain or function in patients after stroke: a randomised trial

de Jong LD¹, Dijkstra PU, Gerritsen J, Geurts AC, Postema K.

Author information

Abstract

QUESTION:

Does static stretch positioning combined with simultaneous neuromuscular electrical stimulation (NMES) in the subacute phase after stroke have beneficial effects on basic arm body functions and activities?

DESIGN:

Multicentre randomised trial with concealed allocation, assessor blinding, and intention-to-treat analysis.

PARTICIPANTS:

Forty-six people in the subacute phase after stroke with severe arm motor deficits (initial Fugl-Meyer Assessment arm score ≤ 18).

INTERVENTION:

In addition to conventional stroke rehabilitation, participants in the experimental group received arm stretch positioning combined with motor amplitude NMES for two 45-minute sessions a day, five days a week, for eight weeks. Control participants received sham arm positioning (ie, no stretch) and sham NMES (ie, transcutaneous electrical nerve stimulation with no motor effect) to the forearm only, at a similar frequency and duration.

OUTCOME MEASURES:

The primary outcome measures were passive range of arm motion and the presence of pain in the hemiplegic shoulder. Secondary outcome measures were severity of shoulder pain, restrictions in performance of activities of daily living, hypertonia, spasticity, motor control and shoulder subluxation. Outcomes were assessed at baseline, mid-treatment, at the end of the treatment period (8 weeks) and at follow-up (20 weeks).

RESULTS:

Multilevel regression analysis showed no significant group effects nor significant time × group interactions on any of the passive range of arm motions. The relative risk of shoulder pain in the experimental group was non-significant at 1.44 (95% CI 0.80 to 2.62).

CONCLUSION:

In people with poor arm motor control in the subacute phase after stroke, static stretch positioning combined with simultaneous NMES has no statistically significant effects on range of motion, shoulder pain, basic arm function, or activities of daily living.

TRIAL REGISTRATION:

NTR1748.

Copyright © 2013 Australian Physiotherapy Association. Published by Elsevier B.V. All rights reserved.

KEYWORDS:

Activities of daily living; Electrical stimulation; Muscle stretching exercises; Randomized controlled trial; Stroke; Upper extremity

PMID:

24287218

DOI:

10.1016/S1836-9553(13)70201-7

[Indexed for MEDLINE]

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Scientists have just discovered that this one green vegetable might be the key to staying young - broccoli

I bet your hospital isn't adding broccoli to your coffee, you probably aren't even getting coffee even though there are many many benefits of coffee. 110 posts on coffee here. Bet your doctor still won't create a diet stroke protocol. Your are fucking screwed for the lack of knowledge your doctor has to get you 100% recovered. NOTHING LESS THAN 100%! AND NO  'All strokes are different, all stroke recoveries are different' is not a valid response. That is just a fucking stupid answer and shows the intellectual capacity of your doctor, not in a good way.
http://hellogiggles.com/vegetable-staying-young-broccoli/
 
It’s not exactly the fountain of youth, but promising new research shows that there’s one particular enzyme that can actually slow down the onset of some of the chronic issues that come with age. Where can we find this enzyme, you ask? Well, in broccoli for starters.

Love it or hate it, we have several compelling reasons to give broccoli some serious respect: lately, it’s been proving it’s a pretty incredible little vegetable. From having some incredibly good effects for the skin, and even being added to coffee to boost your intake of antioxidants, broccoli is kind of the hero veggie of the moment.

In a paper published in Cell Metabolism, a Washington University School of Medicine-led research team found a way to make cells behave as though they’re younger than they actually are.

Now, the cells the researchers were working on were in mice, not humans – but it’s still a pretty exciting find. The agent that makes cells act younger is called nicotinamide mononucleotide (NMN), and it’s involved in the production of another compound. That compound is necessary for energy metabolism.
When the researchers gave mice (who were aging normally) infusions of NMN, the bodies of these mice made more of that second compound – and, most importantly, some of the issues that go hand-in-hand with aging basically disappeared. The mice treated with NMN showed improvement in their eyesight and blood sugar levels, and a lowering in age-associated weight gain.

Dr. Shin-Ichiro Imai, who is the senior author of the paper as well as a professor of developmental biology and medicine at Washington University says: “It’s clear that in humans and in rodents, we lose energy with age. We are losing the enzyme NMN. But if we can bypass that process by adding NMN, we can make energy again. These results provide a very important foundation for the human studies.”

NMN is also found in cabbage, edamame, and cucumbers as well as broccoli – however, it’s probably unlikely that you’d be able to eat yourself young naturally.

According to Dr. Imai, “If you do the math, I wouldn’t say it’s impossible entirely but probably very difficult to get the whole amount [you need] simply from natural foods.”

Part of the research team is based at Keio University in Tokyo and will begin an early study by giving NMN supplements to human participants in pill form. Until then, though, we have to say: Pass the broccoli.

A Bayesian Approach to the Brain

Hopefully your doctor has a good understanding of this when choosing which protocols will address specific damage.  I almost spit my soda thru my nose just rereading that sentence.  But stroke is too serious to joke about, so you may as well start crying about the lack of knowledge on how exactly you will recover. 
http://www.dana.org/Publications/ReportOnProgress/A_Bayesian_Approach_to_the_Brain/
Florent Meyniel, Ph.D.Cognitive Neuroimaging Unit, Neurospin, CEA, University Paris-Saclay, France

Bayesian concepts are appealing to many researchers in fundamental and applied research, including neuroscience. Bayesian tools, part of probability theory, are useful whenever quantitative analysis is needed, such as in statistics, data mining, or forecasting. However, Bayesian concepts have much further reaching implications in neuroscience. They are essential to the way we think about the brain.
BAYES’ RULE BASICS
The mathematical foundation of Bayesian concepts stems from the so-called Bayes’ rule, named after one of its contributors, the 18^th century British Reverend Thomas Bayes. Let's consider a practical example of how Bayes' rule works. A medical doctor faced with the following data D, a patient with a cough, contemplates three hypothetical diseases: a lung cancer (H₁), a cold (H₂) or gastroenteritis (H₃). The relative merit of each hypothesis can be deconstructed as follows according to Bayes’ rule. Patients usually cough when afflicted by lung cancer or a cold but rarely in the case of gastroenteritis. Therefore, the likelihood of the potential cause for the cough is high under H₁ and H₂ and low under H₃. Second, a cold and gastroenteritis are much more prevalent diseases than lung cancer in the general population. The a priori likelihood of H₂ and H₃ is much higher than that of H₁. Given that only H₂ scores high both in a priori and current evidence, the most likely disease given the symptoms is a cold.
Stated more generally, Bayes’ rule says that our degree of belief in a hypothesis H given some current data D depends on the a priori likelihood of this hypothesis (what we know about it, independent of the current data), and the likelihood of the current data given this hypothesis. Formally, degrees of belief and likelihoods correspond to probabilities [1] and Bayes’ rule reads:
p(H|D) = p(D|H)*p(H)/p(D).
Bayes’ rule distinguishes between our belief a priori in the hypothesis p(H) and our belief in this hypothesis a posteriori, p(H|D), once particular data are considered to evaluate it. The notation p(D|H) is a shorthand for the probability of D given that we know H (the so-called likelihood of the data) and p(H|D) for the probability of H given that we know D.
Several aspects of Bayes’ rule are noteworthy. First, it is extremely general – H and D may be any sort of variables as long as they can be assigned a probability. Second, Bayes’ rule is quantitative: the posterior probability on the left hand side accepts only one value that depends on the terms in the right hand side. This means that Bayes’ rule offers a unique way to combine uncertain quantities such as current evidence and prior knowledge in order to estimate the likelihood of a conclusion.In that sense, Bayes’ rule is normative: any other estimate is an over- or under-estimation of the likelihood of the conclusion. This normative nature of Bayes’ rule can be seen as an extension of classical logic. With classical logic, one can derive the validity of a conclusion, which is either true or false from premises that are known for sure. With Bayes’ rule, one can derive the likelihood of a conclusion, which varies on a continuum, from premises that suffer from uncertainty.  Another key aspect of Bayes’ rule is its symmetry: p(H|D) and p(D|H) appear on opposite sides of the equation which allows going from one to the other. The likelihood of current data given a particular hypothesis – p(D|H) – corresponds to solving a direct or “forward” problem: estimating what should be observed given a known cause. Bayes’ rule allows reversing the logic to infer what might be the unknown cause of particular observations – P(H|D).
HOW THE BRAIN IS BAYESIAN
With these mathematical foundations in mind, the brain can be said to be Bayesian in at least three ways. A first key idea is that the brain computes and represents quantities that are probabilistic [2]. In the perceptual domain, this means that every feature of a visual scene is represented by probabilities. For instance, the orientation of a line is not encoded as a single tilt value, but as a distribution of tilt values across several neurons in the visual cortex. Indeed, each of these neurons is tuned for a particular orientation and it responds more intensely when the input data conform to its preferred orientation. Such a neuron therefore acts as a “likelihood detector”: its activity signals the probability of the line having its preferred orientation. Because different neurons are tuned to different orientations, their activity collectively encodes the likelihood of the tilt [3]. This probabilistic view may contrast with the apparent “oneness” of perception. When viewing a scene, we access only one percept at a time, and not distinct hypothetical percepts associated with probabilities. However, recent theories show that this all-or-none processing is the exception rather than the rule in the brain. This “oneness” results from conscious processes that select and amplify one possible interpretation among many [4]. By contrast, most brain processes operate without consciousness and rely on distributions of values and probabilistic computations.
A second Bayesian view of the brain is that the internal knowledge and percepts represented by neurons are constructed following Bayes' rule. This internal knowledge therefore constitutes a posterior belief about the causes of the inputs received by the brain [5,6]. This inference is usually fraught with uncertainty as the brain must make sense of the world based on inputs that are limited and ambiguous. For instance, different three-dimensional shapes in the world may result in the same image once they are projected onto our eyes. There is therefore a real challenge for the brain to perceive the world despite the paucity and the ambiguity of its inputs. This is an old idea in psychology, identified by the 19^th century German scientist von Helmholtz. The Bayesian framework is made to handle inference from uncertain data, and it  even offers a principled remedy: combining the uncertain evidence provided by sensory inputs with prior knowledge.
There is ample experimental evidence that perception relies on prior information to compensate for the poverty of the inputs received. Many biases and visual illusions reveal this automatic reliance on prior information. For instance, when observers are asked to evaluate the tilt of a line, they tend to perceive lines that are nearly vertical as purely vertical, and nearly horizontal as purely horizontal. These orientations are indeed much more frequent in our world. The perceived orientation of a line that weakly departs from these frequent orientations is therefore dominated by our prior expectations [7]. Studies in non-human animals showed that these priors are learned during development from experience. As a result, priors become part of our cortical networks in such a way that they shape their spontaneous activity [8]. When there is no stimulus to drive neuronal activity, the spontaneous activity is dominated by prior expectations. This is because in the absence of input data, the posterior probability in Bayes' rule boils down to the prior probability.
Lastly, Bayes' rule allows for inferring the causes of current observations. By building on this knowledge of the causes, one can in turn predict future observations [5]. This predictive nature of Bayes' rule is the third pillar of the Bayesian view of the brain. Brain imaging and recordings of neurons show that the brain constantly uses previous observations to form expectations about the upcoming events. Such expectations can build up rapidly even in very simple contexts. For instance, upon hearing the four tones “bip”, “bip”, “bip”, “bip” in a row, you may expect that the fifth sound will be another “bip”. Several brain regions increase their activity if the fifth sound is “bop” instead of “bip” [9–11]. This increased activity signals that there is an error: the current expectation appears violated. Interestingly, this error signal is much larger when the expectation was high. An even larger response is recorded if the deviant sound occurs after ten repetitions of “bip” as compared to only four such repetitions. These error signals are actually quantitative: in this simple experiment, they match the expected frequency of sounds that can be inferred using Bayes' rule and the sounds already presented. It is noteworthy that individuals with schizophrenia exhibit significantly lower error signals on electroencephalograms than do healthy individuals in this kind of paradigm, suggesting that statistical inference might be impaired in this pathology. [12,13]. Other experiments used carefully designed sequences of stimuli to show that the brain is capable of learning more complex statistics and even abstract rules [14,15]. Remarkably, experiments in infants and babies showed that this Bayesian machinery operates early in life. Young babies are already capable of quantitative predictions based only on a few observations [16,17].
A major strength of the Bayesian view of the brain is its unifying power. The few examples reported here show that many brain processes can be accounted for by Bayesian principles. It is true across species (in humans and other animals), spatial scales (from single neurons to neuronal networks to brain-scale circuits), cognitive domains (perception, learning, decision making) and stages of development (in neonates, infants and adults). It may even be true of evolution. This is because Bayes' rule is normative: if a particular process deviates from it, then other processes, closer to Bayes' rule, will do better. By selection, processes should gradually approach Bayes' rule, as we see in well-tuned systems such as the human visual cortex.
FUTURE CHALLENGES
This Bayesian view has proved quite successful in neuroscience, although controversy should be acknowledged [18,19]. Challenges nonetheless remain for the future. The most critical one is that Bayesian principles constrain what computations should be, but they leave their implementation entirely open. Indeed, there are often many different ways to solve the same computation. Future works will aim at identifying the specific algorithms that the brain uses for Bayesian computations.
Another challenge is that Bayesian views have been applied so far mostly to perception because this is the domain in which neuroscience is the most advanced. However, future works will probe Bayesian computations in other domains, such as decision making [20–23]. They should also probe the extent to which Bayesian computations and their associated uncertainty levels are accessible to introspection. Recent studies showed that the “sense of confidence” – the degree of belief that we attach to our percepts, memories and decisions – is actually much more sophisticated in humans than previously envisaged [24,25].

New Frontiers in the Management of Multiple Sclerosis

I wonder where the similar CME for stroke is? There seem to be none which is an obvious indictment of the pathetic state of stroke knowledge.
http://www.med-iq.com/neurology/free-cme-course/new-frontiers-in-the-management-of-multiple-sclerosis.html

Maximum Credits:
• 0.5 AMA PRA Category 1 Credit(s)™

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Overview: This interactive online publication explores emerging agents for the treatment of multiple sclerosis, with a focus on cellular pathways, molecular targets, and mechanisms of action. Recent clinical trial data from studies with these agents are also discussed.

Endovascular therapy not superior to standard treatment for stroke

Substantial disability after treatment is because you have done nothing to stop the
neuronal cascade of death.  This lack of knowledge seems endemic amongst stroke researchers and our great stroke association should be contacting all of them to explain the basic facts of life on stroke to them. With such lack of understanding of the problem needing to be solved, we will NEVER make progress.
http://www.healio.com/cardiology/stroke/news/online/%7Ba46ae889-b4c7-42f9-b451-21740946988a%7D/endovascular-therapy-not-superior-to-standard-treatment-for-stroke

Results from two studies presented at the International Stroke Conference 2013 showed similar outcomes for endovascular therapy and intravenous tissue plasminogen activator for the treatment of patients with acute ischemic stroke.

"The development of more effective intravenous lytic agents and endovascular devices to treat patients with acute ischemic stroke is imperative, since the majority of such patients still have substantial disability after treatment, as these trials show," Marc I. Chimowitz, MB, ChB, from the department of neurosciences at Medical University of South Carolina, wrote in a commentary published in The New England Journal of Medicine.
Comparison of endovascular therapy vs. tPA
For one study, Alfonso Ciccone, MD, from the stroke unit and department of neurology at Niguarda Ca' Granda Hospital, Italy, and colleagues analyzed 362 patients with acute ischemic stroke. Within 4.5 hours after stroke onset, patients were randomly assigned to endovascular therapy (intraarterial thrombolysis with recombinant tissue plasimogen activator (tPA), mechanical clot disruption or retrieval, or a combination of approaches; n=181) or IV tPA (n=181). The primary outcome was survival without disability at 3 months.
At 3 months, 30.4% of the endovascular therapy group and 34.8% of the intravenous tPA group were alive without disability (OR=0.71; 95% CI, 0.44-1.14). Six percent of patients in each group experienced a fatal or nonfatal intracranial hemorrhage within 7 days. The researchers found no significant differences in rates of other serious adverse events or case fatality rate between the endovascular therapy and tPA groups.
"This trial did not show that endovascular therapy achieves superior outcomes as compared with IV thrombolysis, and our findings do not provide support for the use of the more invasive and expensive endovascular therapy over intravenous treatment,” the researchers wrote in the study, which was simultaneously published in NEJM.
Similar safety outcomes reported
Functional dependence at 90 days after a stroke was no better for patients who received endovascular therapy and IV tPA or tPA alone, according to results of a second study.
The IMS III study included 656 patients with moderate-to-severe acute ischemic stroke randomly assigned to tPA within 3 hours of symptom onset plus endovascular therapy (n=434) or tPA alone (n=222). The study was stopped early after the study’s Data and Safety Monitoring Board noticed that 90-day outcomes were not different between the two groups.
Results showed no significant difference in the proportion of participants with a modified Rankin score of 2 or less at 90 days (40.8% with endovascular therapy vs. 38.7% with tPA; absolute adjusted difference, 1.5 percentage points) and no significant difference for predefined subgroups of patients with a National Institutes of Health Stroke Scale score of 20 or higher (6.8 percentage points) or 19 or lower (–1 percentage point).
Mortality at 90 days was 19.1% in patients assigned endovascular therapy and 21.6% in patients assigned tPA (P=.52). The proportion of patients who had symptomatic intracerebral hemorrhage within 30 hours after tPA initiation was also similar (6.2% vs. 5.9%, respectively; P=.83).

 

Joseph P. Broderick

"Endovascular therapy is a tool that is not going away," Joseph P. Broderick, MD, from the department of neurology at the University of Cincinnati Neuroscience Institute, said during a press conference. "The issue is how best to use the devices to treat the population of stroke patients."
Trial of imaging selection
Neuroimaging did not identify patients who could benefit from endovascular therapy after acute ischemic stroke, according to a third study presented at the conference and simultaneously published in NEJM.
The MR RESCUE trial evaluated outcomes in 118 patients (mean age, 65.5 years; mean time to enrollment, 5.5 hours). Within 8 hours of acute ischemic stroke, the patients were randomly assigned to mechanical embolectomy (Merci Retriever or Penumbra System) or to standard care. All patients underwent MRI or CT of the brain before treatment.