Major bleeds with warfarin in AF are often fatal

Open question for your doctor. You really don't want either the stroke from afib throwing a clot or the excess bleeding from warfarin. This may go back to wrong dosing. 75% at wrong dosage? 

Major bleeds with warfarin in AF are often fatal 

Results of a large observational study of warfarin use in AF patients suggest that major bleeding rates are higher than in clinical trials and are often fatal [1].

The authors, led by Tara Gomes (University of Toronto, ON), conclude that the study "provides timely estimates of warfarin-related adverse events that may be useful to clinicians, patients, and policy-makers as new options for treatment become available."

For the study, published online in CMAJ on November 26, 2012, Gomes and colleagues linked health administrative databases on prescription drugs and hospitalizations in Ontario. They identified 125 195 patients aged 66 years or over with AF who started warfarin between April 1997 and March 2008. Over the 13-year study period, the rate of major bleeding (defined as any visit to hospital for hemorrhage) was 3.8% per person-year.

Gomes told heartwire that these results were important, as "they reflect the bleeding rates with warfarin in the real world." She said the bleeding rate was "slightly higher than we expected, given that clinical trials have shown rates of major bleeding with warfarin between 1% and 3%."

Noting that some other observational studies have suggested higher bleeding rates than this, she pointed out that many of these studies had much shorter follow-up, and bleeding rates tend to be higher in the early treatment period. "We had a long follow-up period, and as time elapses patients tend to stabilize, so bleeding rates come down." 

Tables at the link.

Relationship between Physical Activity and Brain Atrophy Progression.

Ask your doctor how to get around this catch-22. You need to be able to move to exercise and you need to exercise to be able to move. Ask how to prevent this atrophy and don't accept anything but a realistic answer, they are trained for these questions.
http://www.ncbi.nlm.nih.gov/pubmed/22776876

Abstract

INTRODUCTION:

Brain atrophy is associated with impairment in cognitive function and learning function. The aim of this study was to determine whether daily physical activity prevents age-related brain atrophy progression.

METHODS:

The participants were 381 men and 393 women who had participated in both the baseline and the follow-up surveys (mean duration = 8.2 yr). Magnetic resonance imaging of the frontal and temporal lobes was performed at the time of the baseline and follow-up surveys. The daily physical activities and total energy expenditures of the participants were recorded at baseline with uniaxial accelerometry sensors. Multiple logistic regression models were fit to determine the association between activity energy expenditure, number of steps, and total energy expenditure variables and frontal and temporal lobe atrophy progression while controlling for possible confounders.

RESULTS:

In male participants, the odds ratio of frontal lobe atrophy progression for the fifth quintile compared with the first quintile in activity energy expenditure was 3.408 (95% confidence interval = 1.205-9.643) and for the number of steps was 3.651 (95% confidence interval = 1.304-10.219). Men and women with low total energy expenditure were at risk for frontal lobe atrophy progression. There were no significant differences between temporal lobe atrophy progression and physical activity or total energy expenditure.

CONCLUSION:

The results indicate that physical activity and total energy expenditure are significant predictors of frontal lobe atrophy progression during an 8-yr period. Promoting participation in activities may be beneficial for attenuating age-related frontal lobe atrophy and for preventing dementia.

Former Farmington man bikes across the nation after stroke

Thanks to Barb for pointing this out. I however would bike across Canada if that idea were to stick in my craw. 

Former Farmington man bikes across the nation after stroke

From May to September 2012, he traveled 4,400 miles from Neah Bay, Wash., to Key West, Fla., eight years after suffering a stroke that left him struggling to speak or write.
The trip began as a personal challenge. The mission: complete a solo bike ride cutting a diagonal across the nation from north-west to south-east while sleeping in a one person tent along the way.

More at link.

Alternative and Complementary Medicine for Stroke

I thought I'd go down this rabbit hole to see how deep into the weeds it is and whether it makes any sense at all. My opinion is that this makes no sense at all. I could find NO references to scientific studies and thus they make as much sense as  these nostroms, potions, tonics and patent medicines I talked about earlier, and none of these exist today.
Homeopathic Remedies for Stroke
Homeopathy and Stroke

Homeopathy and Health Forum

Homeopathic treatment of cerebral stroke

Can homeopathy help stroke patient?

Homeopathic Remedy For Stroke?

Maximizing the use of Homeopathy in a CerebroVascular Accident

Homeopathic Remedy For Stroke?

Loneliness Is Bad for Your Health -- Here's What to Do

I used the constraint method, moving to a new location with no friends/relatives nearby, requiring me to use those dormant social skills. But I know nothing, don't listen to me
http://www.caring.com/articles/loneliness-and-health?utm_medium=email&utm_source=suggests&utm_content=20121127

Cholesterol Myths that May Surprise You

More from Yahoo health, run these by your doctor.
http://health.yahoo.net/experts/dayinhealth/cholesterol-myths-may-surprise-you
full details at the link.

Myth: Cholesterol is inherently evil.

Myth: Low cholesterol is always a sign of good health.

Myth: High LDL means you could be headed for a heart attack.

Myth: All LDL particles are equally dangerous.

Myth: Americans have the world’s highest cholesterol levels.

Myth: Triglycerides trigger heart disease.

Myth: Eggs clogs up arteries.

Myth: There are no visible symptoms of high cholesterol.

Stroke leads to sensory scramble

Trying to map proprioception. See if your doctor can figure out how to use this in your recovery.
http://www.6minutes.com.au/news/latest-news/stroke-leads-to-sensory-scramble
Stroke may scramble the brain’s sensory map of the hand, Australian researchers report for the first time - an effect they warn is missed by routine examinations.
Dr Ingvars Birznieks and his team from Neuroscience Research Australia applied a non-painful stimulus to 25 locations on a stroke patient’s hand. In only one instance did the patient, who could not see the hand, correctly identify the location of the stimulus – in the middle of the palm.
The 24 other touch points were drastically scrambled. A touch on the thumb was interpreted as a touch on the index finger, a touch on the pinky was believed to be a touch on the middle finger, and a touch on the middle finger was mistaken... (more if you register)
Image from the link:

At The Stroke Of A Key: Online Therapies To Improve Sight Of Stroke Survivors

Your therapist and doctor  should be able to tell you if these are worthwhile.
Demos at the link.
http://www.rttnews.com/2012601/at-the-stroke-of-a-key-online-therapies-to-improve-sight-of-stroke-survivors.aspx?type=bio

The Shot That Prevents Heart Attacks

From Yahoo health. Reasoning at the link.
 http://health.yahoo.net/experts/dayinhealth/shot-prevents-heart-attacks
Scientists from TIMU Study Group and Network for Innovation in Clinical Research analyzed published clinical trials involving a total of 3,227 patients, half of whom had been diagnosed with heart disease. Participants, whose average age was 60, were randomly assigned to either receive flu vaccine or a placebo shot, then their health was tracked for 12 months.
Those who got the flu shot were 50 percent less likely to suffer major cardiac events (such as heart attacks or strokes) and 40 percent less likely to die of cardiac causes. Similar trends were found in patients with and without previous heart disease. The findings suggest “that flu vaccine is a heart vaccine,” lead study author Jacob Udell told Fox News.

Study: Biking Restores Brain Connectivity in Parkinson's

Its going to do the same for me after stroke, but don't listen to anything I have to say, I'm just a stupid stroke survivor. Your doctor and therapists know everything. 

Study: Biking Restores Brain Connectivity in Parkinson's

Stroke patients may perform better in clinical tests when offered a reward

I could see insurance paying 10 cents on the dollar compared to the savings they get by your recovery.  Why not pay for performance? It worked for Pavlovs' dog and your job.
source here: 4 pages worth.
 http://jnnp.bmj.com/content/early/2012/10/28/jnnp-2012-303169.full.pdf
translation to readable here:
http://www.news-medical.net/news/20121126/Stroke-patients-may-perform-better-in-clinical-tests-when-offered-a-reward.aspx

Stroke patients who have difficulty paying attention to part of their visual field may perform better when offered a reward, a study by Imperial College London and Brunel University researchers has found.
Between a third and half of stroke patients suffer from spatial neglect - a disorder of visual attention that means they do not notice objects on one side of their field of view. In some cases, sufferers have been known to shave only one side of their face or leave half of a meal on their plate.
The new study found for the first time that patients with neglect did better in clinical tests when they were promised a financial reward. The findings point towards new behavioural therapies for stroke patients, and also highlight a system in the brain that can be targeted by drugs.
Dr Paresh Malhotra, from the Division of Brain Sciences at Imperial College London, who led the study, said: "There's been a lot of research recently on how reward can improve attention in healthy people. We wanted to see if performance would improve in patients with spatial neglect if you offer them a reward."
Ten patients with spatial neglect at Imperial College Healthcare NHS Trust were given a test that required them to circle images of coins on a sheet of paper with lots of similar-looking round objects. They were told they would get a pound for each coin they circled. They also performed a similar test with buttons instead of coins, and were told there was no reward for this test.
After their first visit, all the participants were given £15 worth of vouchers, although they were told that the value was determined by their performance in the coin test. They were given the same tests again on a later date, with a reward again promised for the coin test but not the button test. On the second visit, eight out of ten did better on the coin test. There was no improvement on the button test.
"Clearly we can't offer patients money, but the results suggest that other sorts of motivational stimuli might be useful in stroke rehab," Dr Malhotra said.
The researchers think the improvement in performance might be down to a brain chemical called dopamine, which has been found to improve attention in some patients with this condition. Many previous studies in healthy people and animals have found that dopamine levels rise when we anticipate a reward, such as food, sex or money. Dopamine is thought to make people feel motivated to behave in ways that bring about the reward.
The researchers did not measure dopamine levels in this study, but the two patients whose performance didn't improve when offered a reward had damage in a brain region called the striatum, which is recognised as a key area where dopamine is released. "There's a lot of work linking reward with dopamine, so another implication is that we are harnessing the brain's own dopamine system to bring about these effects," Dr Malhotra said.

Study confirms high biocompatibility of polymeric materials with brain tissue

Signs of Steve Austin in the Bionic Man or maybe the Cylons from Battlestar Gallactica, or for those who are into books, H. G. Wells -The Island of Doctor Moreau
 http://www.news-medical.net/news/20121126/Study-confirms-high-biocompatibility-of-polymeric-materials-with-brain-tissue.aspx

Professor José Miguel Soria, a member of the Institute of Biomedical Sciences, Universidad CEU Cardenal Herrera, has co-directed with Professor Manuel Monleón of the Universitat Politècnica de València a study on the compatibility of polymeric biomaterials in the brain and its effectiveness to favour neuroregeneration in areas with some kind of damage or brain injury.
The research carried out has shown that these types of implants, made of a biocompatible synthetic material, are colonized within two months by neural progenitor cells and irrigated by new blood vessels. This allows the generation, within these structures, of new neurons and glia, capable of repairing injured brain tissue caused by trauma, stroke or neurodegenerative disease, among other causes.

The synthetic structures used in this study are made with a porous and biocompatible polymeric material called acrylate copolymer. In the first phase of the project, the structures have been studied in vitro by implanting them into neural tissue, and subsequently also in vivo, when implanted in two areas of the adult rat brain: the cerebral cortex and the subventricular zone, the most important source of generation of adult neural stem cells.

The study has confirmed the high biocompatibility of polymeric materials, such as acrylate copolymer, with brain tissue and opens new possibilities of the effectiveness of the implementation of these structures in the brain, seeking optimum location for developing regenerative strategies of the central nervous system.
Furthermore, the results are particularly relevant when one considers that in the adult brain neuroregeneration capacity is more limited than in younger individuals and that the main impediment for this is the lack of revascularization of damaged tissue, something that the biomaterial studied has shown to favour.

Gait Speed and Survival in Older Adults

So your doctor should be using these objective measurements to gauge your survival instead of just pulling something off the top of their heads.
http://download.journals.elsevierhealth.com/pdfs/journals/0002-9343/PIIS0002934312004913.pdf


http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3080184/

Abstract

Context

Survival estimates help individualize goals of care for geriatric patients, but life tables fail to account for the great variability in survival. Physical performance measures, such as gait speed, might help account for variability, allowing clinicians to make more individualized estimates.

Objective

To evaluate the relationship between gait speed and survival.

Design, Setting, and Participants

Pooled analysis of 9 cohort studies (collected between 1986 and 2000), using individual data from 34 485 community-dwelling older adults aged 65 years or older with baseline gait speed data, followed up for 6 to 21 years. Participants were a mean (SD) age of 73.5 (5.9) years; 59.6%, women; and 79.8%, white; and had a mean (SD) gait speed of 0.92 (0.27) m/s.

Main Outcome Measures

Survival rates and life expectancy.

Results

There were 17 528 deaths; the overall 5-year survival rate was 84.8% (confidence interval [CI], 79.6%–88.8%)and 10-year survival rate was 59.7% (95%CI, 46.5%–70.6%). Gait speed was associated with survival in all studies (pooled hazard ratio per 0.1 m/s, 0.88; 95% CI, 0.87–0.90; P<. 001). Survival increased across the full range of gait speeds, with significant increments per 0.1 m/s. At age 75, predicted 10-year survival across the range of gait speeds ranged from 19% to 87% in men and from 35% to 91% in women. Predicted survival based on age, sex, and gait speed was as accurate as predicted based on age, sex, use of mobility aids, and self-reported function or as age, sex, chronic conditions, smoking history, blood pressure, body mass index, and hospitalization.

Conclusion

In this pooled analysis of individual data from 9 selected cohorts, gait speed was associated with survival in older adults.

Remaining years of life vary widely in older adults, and physicians should consider life expectancy when assessing goals of care and treatment plans.¹ However, life expectancy based on age and sex alone provides limited information because survival is also influenced by health and functional abilities.² There are currently no well-established approaches to predicting life expectancy that incorporate health and function, although several models have been developed from individual data sources.^3–5 Gait speed, also often termed walking speed, has been shown to be associated with survival among older adults in individual epidemiological cohort studies ^6–12 and has been shown to reflect health and functional status.¹³ Gait speed has been recommended as a potentially useful clinical indicator of well-being among the older adults.¹⁴ The purpose of this study is to evaluate the association of gait speed with survival in older adults and to determine the degree to which gait speed explains variability in survival after accounting for age and sex.

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METHODS

Overview

We used individual participant data from 9 cohort studies, baseline data for which were collected between 1986 and 2000 (Table 1).^{8,15,16,18–23} Each study, which included more than 400 older adults with gait speed data at baseline, monitored survival for at least 5 years. Analyses performed herein were conducted in 2009 and 2010. All studies required written informed consent and institutional review board approval.

Table 1

Characteristics of Participants in the 9 Cohort Studies

Populations

All studies recruited community-dwelling older adults. Although some sought representative samples,^8,15,20,23 others focused on healthier participants,^16,17 single sex,^19,22 or older adults from primary care practices.²¹ Only participants 65 years and older with baseline gait speed data were included in this study. Individual study goals, recruitment methods, and target populations have been published.^8,15–23

Measures

Gait speed was calculated for each participant using distance in meters and time in seconds. All studies used instructions to walk at usual pace and from a standing start. The walk distance varied from 8 ft to 6 m. For 8 ft, we converted to 4-m gait speed by formula.²⁴ For 6 m, we created a conversion formula (4-m speed=−0.0341 + (6-mspeed)×0.9816 withR²=0.93, based on a cohort of 61 individuals with concurrent 4- and 6-m walks). For 15 feet (4.57 m),²³ speed was simply meters divided by time. Where available, data on fast gait speed (walk as fast as comfortably able²⁵) and the Short Physical Performance Battery were obtained.²⁶ Survival for each individual used study monitoring methods, including the National Death Index and individual study follow-up. Time from gait speed baseline to death was calculated in days. Five-year survival status was confirmed for more than 99% of participants.

Additional variables include sex, age, race/ethnicity (white, black, Hispanic, other, defined by participant), height(centimeters), weight(kilograms), body mass index (BMI), calculated as weight in kilograms divided by height in meters squared (<25 25="25" and="and">30), smoking (never, past, current), use of mobility aids (none, cane, walker), systolic blood pressure, self-reports of health (excellent or very good vs good, fair, or poor), hospitalization in the past year (yes/no), and physician-diagnosed medical conditions (cancer, arthritis, diabetes, and heart disease, all yes/no). Measures of self-reported functional status were not collected in all studies and varied in content and form. We created a dichotomous variable reflecting dependence in basic activities of daily living (ADLs) based on report of being unable or needing help from another person to perform any basic activity, including eating, toileting, hygiene, transfer, bathing, and dressing. For individuals independent in ADLs, we created a dichotomous variable reflecting difficulty in instrumental ADLs based on report of difficulty or dependence with shopping, meal preparation, or heavy housework due to a health or physical problem. Participants were then classified into 1 of 3 groups; dependent in ADLs, difficulty with instrumental ADLs, or independent. Physical activity data were collected in 6 studies, but time frames and items varied widely. Two studies used the Physical Activity Scale for the Elderly (PASE).²⁷ We dichotomized the PASEs core at 100.²⁸ We created operational definitions of other covariates that were reasonably consistent across studies. Covariates were identical for height, weight, BMI, and systolic blood pressure. Hospitalization within the prior year was determined largely by self-report, and chronic conditions were by self-report of physician diagnosis, with heart disease encompassing angina, coronary artery disease, heart attack, and heart failure.

Statistical Analysis

Descriptive statistics summarized participant characteristics, follow-up period, and median survival from baseline. A study-wide a priori P value of .002 provides a conservative Bonferroni correction accounting for atleast 25 individual statistical comparisons. Kaplan-Meier product-limit survival curves graphically summarize lifetimes for each gait speed category.²⁹ For graphical purposes, gait speed was categorized into 0.2-m/s increments with lower and upper extremes being grouped as less than 0.4 m/s and higher than 1.4 m/s.

Cox proportional hazards regression models were used to assess associations between gait speed and survival, adjusting for age at baseline, for which hazard ratios (HRs) correspond to a 0.1-m/s difference in gait speed. The analyses were repeated adjusting for height, sex, race, BMI, smoking history systolic blood pressure, diseases, prior hospitalization, and self-reported heath. Proportionality of hazards was verified by examining Schoenfeld residual plots.³⁰ Appropriateness of using gait speed as a continuous predictor was confirmed by observing linearity in Cox models with ordered 0.2-m/s gait speed categories. To examine the influence of early deaths, we repeated analyses excluding deaths within 1 year of gait speed measurement and moved up the 0 time for survival assessment (results were similar; eTable 1 available at http://www.jama.com). Subgroup analyses were repeated in strata by age (65–74, 75–84, or ≥85 years), sex, race, self-reported health status, smoking history, BMI, functional status, use of mobility aids, and hospitalization and by report of cancer, arthritis, diabetes, and heart disease.²⁹ Results were pooled across sex because no substantial sex differences existed in HRs within subgroup strata.

To obtain simple and clinically usable estimates of survival probability based on sex, age, and gait speed, we fit logistic regression models separately for each sex with dichotomized 5- and 10-year survival as the response variable and age, gait speed, and their interaction as continuous predictors. To obtain estimates of median survival (further life expectancy), we fit Weibull accelerated failure–time models separately for each with time to death as the response variable, and age, gait speed, and their interaction as continuous predictors. To compare ability to predict survival among candidate variables and to determine whether gait speed improves predictive accuracy beyond other clinical measures, we fit logistic regression models with dichotomized 5-year or 10-year survival as the response variable and various combinations of predictors as independent variables with both linear and squared terms for BMI. The area under the receiver operating characteristic (ROC) curve or C statistic was used as a measure predictive of accuracy for mortality. All study-specific statistical analyses were performed using SAS version 9.2 (SAS Institute Inc, Cary, North Carolina).

Age-adjusted HRs were pooled from all studies using standard meta-analytic statistical methodology. Heterogeneity of HRs across studies was assessed using the Q and I² statistics.^31,32 We used a random-effects model to appropriately pool the HRs on the log scale while incorporating any heterogeneity among study estimates and then transform back to obtain an overall HR, along with a 95% confidence interval(CI)and P value.³³ Sensitivity of the results was assessed by fitting a shared frailty³⁴ (unrelated to the geriatric syndrome frailty) model to individual participant data with a γ-distributed frailty parameter to account for study effect (results similar; not shown).^34,35 Five-and 10-year pointwise survival rates from the Kaplan-Meier curves for each sex, age-group, and gait speed category combination were pooled across studies using a random-effects model on the complementary log-log scale³⁶ and then appropriately inverted to obtain overall estimates of survival, as presented in the tables. We further used the standard random effects meta-analytic model to combine sex-specific regression coefficients for age, gait speed, and their interaction from logistic regression models for 5- and 10-year survival and used the overall estimates to construct clinically usable survival probability nomograms; combine sex-specific regression coefficients for age, gait speed, and their interaction from accelerated failure time models for time to death and used the overall estimates to construct clinically usable life-expectancy nomograms; and combine areas under ROC curves obtained from 9 studies. An increase of 0.025 in overall area under ROC curve was interpreted as clinically relevant better accuracy.³⁷ To appropriately combine entire survival curves across the 9 studies, we used the generalized least squares method for joint analysis of survival curves.³⁸ We used a random-effects model with weights obtained by inverse of the variance of the survival function at the median life times to pool the median survival times for each sex, age group, and gait speed category. We used Comprehensive Meta Analysis version 2.2 (Biostat Inc, Englewood, New Jersey) for all meta-analytic methods and Stata SE 8 (StataCorp, College Station, Texas) for fitting shared frailty models.

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RESULTS

The 9 participating studies contributed a total of 34 485 participants (Table 1). Although most studies included men and women, 2 were sex specific.^19,22 Of the total, 59.6% were women. There were substantial numbers of African American (n=3852) and Hispanic (n=2650) participants. The studies had a wide age range, including 1765 persons older than 85 years. Similarly, there was a wide range of gait speeds, from less than 0.4 m/s (n=1247) to more than 1.4 m/s (n=1491). Study follow-up time ranged from 6.0 to 21.0 years, with participants followed up for a mean of 12.2 and a median of 13.8 years. There were 17 528 total deaths across all studies, with rates varying from 18.40% to 91.87% in individual studies. Mortality rates appear to be related to length of follow-up (Table 1).

To assess consistency across studies, risk of death was estimated per 0.1-m/s higher gait speed. Age-adjusted HRs by study ranged from 0.83 to 0.94 and all were significant (P<.001; Figure 1). We also examined the survival HRs for gait speed by study in subgroups, including age, sex, race/ethnicity, BMI, smoking history, use of mobility aids, prior hospitalization, self-reported health, functional status, and selected chronic diseases. There were consistent associations across studies, although given the large sample sizes, Q statistics were often statistically significant (details available in eFigure 1A–M available at http://www.jama.com). For the 3 levels of functional status (independent, difficulty with instrumental ADLs, and dependent in ADLs), the pooled HR per 0.1-m/s increase in gait speed for those who were independent was 0.92 (P= .005), for those with difficulty in instrumental activities was also 0.92 (P<.001) but was 0.94 (P=.02) among those dependent in ADLs. Because physical activity measures were not sufficiently consistent across studies, effects could not be pooled. The Osteoporotic Fractures in Men (MrOS)¹⁹ and Hispanic Established Populations for Epidemiologic Studies of the Elderly (EPESE)⁸ used the Physical Activity Scale for the Elderly (PASE). When dichotomized at a score of 100 into low and high activity, MrOS had consistent and statistically significant HRs for low (HR, 0.85; 95% CI, 0.81–0.88) and high (HR, 0.87; 95% CI, 0.84–0.90) physical activity. In the Hispanic EPESE, the HR for low physical activity was significant (0.92; 95% CI, 0.88–0.96) but the HR for higher physical activity was not (0.99; 95% CI, 0.95–1.04). Pooled HRs for all subgroups except functional status were consistently in the range of 0.81 to 0.92 and all were significant (P<.002).

Figure 1

Age-Adjusted Hazard Ratio for Death per 0.1-m/s Higher Gait Speed

The overall HR for survival per each 0.1 m/s faster gait speed was 0.88 (95% CI, 0.87–0.90; P<.001) when pooled across all studies using a random-effects meta-analytic statistical approach (Figure 1 and eFigure 1 available at http://www.jama.com). Further adjustment for sex, BMI, smoking status, systolic blood pressure, diseases, prior hospitalization, and self-reported health did not change the results (overall HR, 0.90; 95% CI, 0.89–0.91; P<.001). Using data from all studies, we created for each sex, 5- and 10-year survival tables (Table 2, data derived from pooled Kaplan-Meier estimates evaluated at 5 and 10 years, presented in 3 age groups) and graphs (eFigure 3 and eFigure 4 predicted survival based on pooled logistic regression coefficients, data presented with age as a continuous variable). Gait speed was associated with differences in the probability of survival at all ages in both sexes, but was especially informative after age 75 years. In men, the probability of 5-year survival at age 85 ranged from 0.3 to 0.88 (eFigure 3A) and the probability of 10-year survival at age 75 years ranged from 0.18 to 0.86 (eFigure 4A). In women, the probability of 5-year survival remained greater than 0.5 until advanced age (eFigure 3B), but 10-year survival at age 75 years ranged from 0.34 to 0.92 and at age 80 years from 0.22 to 0.86 (eFigure 4B). Stratification by sex-specific median height failed to show systematic differences in survival rates between short and tall participants, so results presented are not stratified by height. Stratification by race/ethnicity (non-Hispanic white, black, Hispanic) suggested generally similar survival rates by gait speed among age and sex groups. Confidence intervals were often wide. In some subsets of slow walkers of Hispanic descent, survival rates were 10% to 20% higher than in other groups (eTable 2).

Table 2

Five- and 10-Year Survival in Men and Women by Age and Gait Speed Group

We also used our analyses to estimate median years of remaining life based on sex, age, and gait speed. (Figure 2, predicted survival data are based on an accelerated failure time model with Weibull distribution, with age as a continuous variable, and eTable 3, data are derived from pooled Kaplan-Meier estimates evaluated at 5 and 10 years in 3 age groups.) In the pooled sample, median survival in years for the age groups 65 through 74 years was 12.6 for men and 16.8 for women; for 75 through 84 years, 7.9 for men and 10.5 for women; and for 85 years or older, 4.6 for men and 6.4 years for women (eTable 3 available at http://www.jama.com). Predicted years of remaining life for each sex and age increased as gait speed increased, with a gait speed of about 0.8 m/s at the median life expectancy at most ages for both sexes (Figure 2; a PDF of enlarged graphs is available at http://www.jama.com). Gait speeds of 1.0 m/s or higher consistently demonstrated survival that was longer than expected by age and sex alone. In this older adult population, the relationship of gait speed with remaining years of life was consistent across age groups, but the absolute number of expected remaining years of life was larger at younger ages. For 70-year-old men, life expectancy ranged from 7 to 23 years and for women, from 10 to 30 years.

Figure 2

Predicted Median Life Expectancy by Age and Gait Speed

To compare the 5-year survival predictive ability between demographics and gait speed vs other combinations of variables, we used areas under the ROC curve (C statistics) in logistic regression models for individual studies and pooled across studies (Table 3). Gait speed added substantially³⁷ to age and sex in 7 of the 9 studies and in the pooled analysis. C statistics for age, sex, and gait speed were greater than those for age, sex, and chronic diseases in 4 of 9 studies, approximately equivalent in 5 studies and inferior in no studies. C statistics for age, sex, and gait speed were approximately equivalent to those for age, sex, chronic diseases, BMI, systolic blood pressure, and prior hospitalization in all 9 studies and in the pooled analysis. There were 4 studies that had sufficiently consistent data on functional status to create 3 categories: dependent in ADLs, difficulty with instrumental ADLs, and independent. For these studies, gait speed, age, and sex yielded a C statistic (0.741) that was not significantly different (P=.78) from age, sex, mobility aids, and functional status (P=.75; Table 3).

Table 3

Predictive Accuracy for 5- and 10-Year Survival by Individual Study and Pooled Data Presented as Area Under the Receiver Operating Characteristic Curves

For 10-year survival, 6 studies had sufficient follow-up time to perform many of the analyses (Table 3). Gait speed added predictive ability to age and sex in 4 of 6 studies and in the pooled analysis. C statistics for age, sex, and gait speed were not significantly different from C statistics with all the other factors for any study nor for the pooled analysis. Three studies had sufficiently consistent data on functional status at baseline to allow pooling. Gait speed, age, and sex yielded a C statistic (0.734) that was not significantly different from age, sex, mobility aids, and functional status (0.732; (P=.95; Table 3).

In addition, we used C statistics to assess the ability of usual gait speed to predict survival compared with other physical performance measures, such as fast gait speed and the Short Physical Performance Battery (SPPB), a brief measure that includes walk speed, chair rise ability, and balance. We assessed usual vs fast gait speed in the single study with both measures (Invecciare in Chianti¹⁸ study: usual, 0.727 [95% CI, 0.678–0.776]; fast, 0.684 [95% CI, 0.630–0.739]), suggesting that fast walks did not have an advantage in survival prediction over usual-paced walks. Gait speed was superior to the SPPB in the Hispanic Established Populations for the Epidemiological Study of the Elderly⁸ (gait speed, 0.617; 95% CI, 0.585–0.649; SPPB, 0.574; 95% CI, 0.539–0.649); was equivalent in the following 3 studies: Health, Aging, and Body Composition (ABC) study and ABC¹⁶ (gait speed, 0.579; 95% CI, 0.548–0.610; SPPB, 0.560; 95% CI, 0.528–0.592); Invecciare in Chianti (gait speed, 0.727; 95% CI, 0.678–0.776; SPPB, 0.738; 95% CI, 0.690–0.735); Predicting Elderly Performance study¹⁸ (gait speed, 0.667; 95% CI, 0.610–0.724; SPPB, 0.691; 95% CI, 0.637–0.744); and worse than SPPB in the Established Populations for the Epidemiological Study of the Elderly¹⁵ (gait speed, 0.638; 95% CI, 0.610–0.777; SPPB, 0.663; 95% CI, 0.636–0.691).

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COMMENT

Gait speed, age, and sex may offer the clinician tools for assessing expected survival to contribute to tailoring goals of care in older adults. The accuracy of predictions based on these 3 factors appears to be approximately similar to more complex models involving multiple other health-related factors, or for age, sex, use of mobility aids, and functional status. Gait speed might help refine survival estimates in clinical practice or research because it is simple and informative.

Why would gait speed predict survival? Walking requires energy, movement control, and support and places demands on multiple organ systems, including the heart, lungs, circulatory, nervous, and musculoskeletal systems. Slowing gait may reflect both damaged systems and a high-energy cost of walking.^13,39–54 Gait speed could be considered a simple and accessible summary indicator of vitality because it integrates known and unrecognized disturbances in multiple organ systems, many of which affect survival. In addition, decreasing mobility may induce a vicious cycle of reduced physical activity and de-conditioning that has a direct effect on health and survival.⁶

The association between gait speed and survival is known.^{6,7,9–12,55,56} Prior analyses used single cohorts and presented results as relative rather than absolute risk, as done herein. Similarly, mortality prediction models have been developed.^{3–5,57–60} Some models use self-reported information but others also include physiological or performance data, for a total of 4 to more than 10 predictive factors. Only a few models assess overall predictive capacity using C statistics; the reported values are in the range found in the present study (published area under the curve range, 0.66–0.82⁶¹ vs this study, 0.717 and 0.737).

The strengths of this study are the very large sample of individual participant data from multiple diverse populations of community-dwelling elders who were followed up for many years and use of consistent measures of performance and outcome. We provide survival estimates for a broad range of gait speeds and calculate absolute rates and median years of survival. Compared with prior studies that were too small to assess potential effect modification by age, sex, race/ethnicity, and other subgroups, we were able to assess multiple subgroup effects with substantial power. This study has the limitations of observational research; it cannot establish causal relationships and is vulnerable to various forms of healthy volunteer bias. The participating study cohorts, while large and diverse, do not represent the universe of possible data. Our survival estimates should be validated in additional data sets. Only 1 of the 9 studies was based in clinical practice,²¹ and advanced dementia is rare in populations who are competent to consent for research. However, median years of survival in this study resemble estimates for US adults across the sex and age range assessed.⁶² We were unable to assess the association of physical activity with survival in pooled analyses because measures of activity were highly variable across studies. Also, participants in these studies had no prior knowledge about the meaning of walking speed. In clinical use, participants might walk differently if they are aware of the implications of the results. Although this study provides information on survival, further work is needed to examine associations of other important pooled outcomes such as disability and health care use and to examine effects in populations more completely based in clinical practice.

Because gait speed can be assessed by nonprofessional staff using a 4-m walkway and a stopwatch,²¹ it is relatively simple to measure compared with many medical assessments. Nevertheless, methodological issues such as distance and verbal instructions remain.^63,64 Self-report is an alternative to gait speed for reflecting function. However, significant challenges remain in the use of self-report as well, such as choice of items and reliability, some of which can be addressed by emerging techniques such as computer adaptive testing based on item-response theory.⁶⁵ The results found herein suggest that gait speed appears to be especially informative in older persons who report either no function all imitations or only difficulty with instrumental ADLs and may be less helpful for older adults who already report dependence in basic ADLs. The research studies analyzed herein used trained staff to measure gait speed. Staffin clinical settings would need initial training and may produce more variable results. Long-distance walks have become accepted in some medical fields and may contribute information beyond short walks.^66–68 However, the longer distance and time to perform the test may limit feasibility in many clinical settings. Although the sample size of very slow walkers was small, our data suggest that there may be a subpopulation who walk very slowly but survive for long periods. It would be valuable to further characterize this subgroup.

Although the gait speed–survival relationship seems continuous across the entire range, cut points may help interpretation. Several authors have proposed that gait speeds faster than 1.0 m/s suggest healthier aging while gait speeds slower than 0.6 m/s increase the likelihood of poor health and function.^7,21 Others propose one cutoff around 0.8 m/s.¹³ In our data, predicted life expectancy at the median for age and sex occurs at about 0.8 m/s; faster gait speeds predict life expectancy beyond the median. Perhaps a gait speed faster than 1.0 m/s suggests better than average life expectancy and above 1.2 m/s suggests exceptional life expectancy, but additional research will be necessary to determine this relationship.

How might gait speed be used clinically? First, gait speed might help identify older adults with a high probability of living for 5 or 10 more years, who may be appropriate targets for preventive interventions that require years for benefit. Second, gait speed might be used to identify older adults with increased risk of early mortality, perhaps those with gait speeds slower than 0.6 m/s. In these patients, further examination is targeted at potentially modifiable risks to health and survival. A recommended evaluation and management of slow walking includes cardiopulmonary, neurological and musculoskeletal systems.^6,18 Third, gait speed might promote communication. Primary clinicians might characterize an older adult as likely to be in poor health and function because the gait speed is 0.5 m/s. In research manuscripts, baseline gait speed might help to characterize the overall health of older research participants. Fourth, gait speed might be monitored overtime, with a decline indicating a new health problem that requires evaluation. Fifth, gait speed might be used to stratify risks from surgery or chemotherapy. Finally, medical and behavioral interventions might be assessed for their effect on gait speed in clinical trials. Such true experiments could then evaluate causal pathways to determine whether interventions that improve gait speed lead to improvements in function, health, and longevity.

The data provided herein are intended to aid clinicians, investigators, and health system planners who seek simple indicators of health and survival in older adults. Gait speed has potential to be implemented in practice, using a stop watch and a 4-m course. From a standing start, individuals are instructed to walk at their usual pace, as if they were walking down the street, and given no further encouragement or instructions. The data in this article can be used to help interpret the results. Gait speed may be a simple and accessible indicator of the health of the older person.

References at the link.

A ‘Party Drug’ May Help the Brain Cope With Trauma - Ecstasy

Do not attempt this on your own.

A ‘Party Drug’ May Help the Brain Cope With Trauma - Ecstasy

Selected paragraphs here:

  Hundreds of Iraq and Afghanistan veterans with post-traumatic stress have recently contacted a husband-and-wife team who work in suburban South Carolina to seek help. Many are desperate, pleading for treatment and willing to travel to get it.

NOVEL APPROACH Michael and Ann Mithoefer are evaluating MDMA for trauma.

The soldiers have no interest in traditional talking cures or prescription drugs that have given them little relief. They are lining up to try an alternative: MDMA, better known as Ecstasy, a party drug that surfaced in the 1980s and ’90s that can induce pulses of euphoria and a radiating affection. Government regulators criminalized the drug in 1985, placing it on a list of prohibited substances that includes heroin and LSD. But in recent years, regulators have licensed a small number of labs to produce MDMA for research purposes. 

In a paper posted online Tuesday by the Journal of Psychopharmacology, Michael and Ann Mithoefer, the husband-and-wife team offering the treatment — which combines psychotherapy with a dose of MDMA — write that they found 15 of 21 people who recovered from severe post-traumatic stress in the therapy in the early 2000s reported minor to virtually no symptoms today. Many said they have received other kinds of therapy since then, but not with MDMA.

 

Motor Neurons that Multitask

If these multitask with proprioception, I want to know what protocol will bring them back after stroke.
Motor Neurons that Multitask

Summary

Animals use a form of sensory feedback termed proprioception to monitor their body position and modify the motor programs that control movement. In this issue of Neuron, Wen et al. (2012) provide evidence that a subset of motor neurons function as proprioceptors in C. elegans, where B-type motor neurons sense body curvature to control the bending movements that drive forward locomotion.

The novel role of epigenetics in primary prevention of cardiovascular diseases

I'm sure your doctor can tell you exactly how to prevent CV events.
http://scholar.google.com/scholar_url?hl=en&q=http://pagepressjournals.org/index.php/cardiogen/article/view/cardiogenetics.2012.e12/pdf&sa=X&scisig=AAGBfm2cd5v34ELJYFuTqQKH1JMGOAQQzA&oi=scholaralrt
Abstract
A great deal of evidences indicate that
impaired fetal growth and in utero exposure to
risk factors, especially maternal hypercholesterolemia,
may be relevant for human pathophysiological
signs of atherosclerosis and subsequent
development of cardiovascular disease
(CVD) during different life stages. Despite the
underlying mechanisms of fetal programming
are still unknown, epigenetics has been suggested
as one of the possible explanations for
the associations between intrauterine risk factors
and CVD development. Indeed, a lot of
translational studies support the hypothesis
that epigenetic changes are related to
increased CVD risk although it is still not possible
to establish a direct causality in humans.
Notably, epigenetic modifications can be
reversible through therapeutic approaches
employing histone deacetylase inhibitors, histone
acetyltransferase inhibitors and commonly
used drugs like statins. Thus, the whole
comprehension of these mechanisms will provide
in the next future the rationale for the
development of novel tools to be used in the
primary prevention and therapy of CVD.

Neuroimaging in aphasia treatment research: Quantifying brain lesions after stroke

Maybe they are finally getting an understanding of damage  and correlation to aphasia.
http://scholar.google.com/scholar_url?hl=en&q=http://www.researchgate.net/publication/230588372_Neuroimaging_in_aphasia_treatment_research_Quantifying_brain_lesions_after_stroke/file/9fcfd50a5106e7fc68.pdf&sa=X&scisig=AAGBfm1RMEgSyt_9xRyVDdP_pUxouxfe9w&oi=scholaralrt
a b s t r a c t

New structural and functional neuroimaging methods continue to rapidly develop, offering promising tools for cognitive neuroscientists. In the last 20 years, advanced magnetic resonance imaging (MRI) techniques have provided invaluable insights into how language is represented and processed in the brain and how it can be disrupted by damage to, or dysfunction of, various parts of the brain. Current functional MRI (fMRI) approaches have also allowed researchers to purposefully investigate how individuals recover language after stroke. This paper presents recommendations for quantification of brain lesions derived from discussions among international researchers at the Neuroimaging in Aphasia Treatment Research Workshop held at Northwestern University (Evanston, Illinois, USA). Methods for detailing and characterizing the brain damage that can influence results of fMRI studies in chronic aphasic stroke patients are discussed. Moreover, we aimed to provide the reader with a set of general practical guidelines and references to facilitate choosing adequate structural imaging strategies that facilitate fMRI studies in aphasia treatment research.

The mechanobiology of brain function

You doctor should be subscribing to Nature Reviews Neuroscience so ask them about this one and apply it to your recovery.
http://www.nature.com/nrn/journal/v13/n12/abs/nrn3383.html#top

All cells are influenced by mechanical forces. In the brain, force-generating and load-bearing proteins twist, turn, ratchet, flex, compress, expand and bend to mediate neuronal signalling and plasticity. Although the functions of mechanosensitive proteins have been thoroughly described in classical sensory systems, the effects of endogenous mechanical energy on cellular function in the brain have received less attention, and many working models in neuroscience do not currently integrate principles of cellular mechanics. An understanding of cellular-mechanical concepts is essential to allow the integration of mechanobiology into ongoing studies of brain structure and function.

Understanding sleep problems in rehabilitation inpatients after stroke

Demand your doctor get this and figure out your sleep problems, Ask him/her what role dreams play and if you should be dreaming pre or post stroke or maybe lucid dreaming. It may just be a thesis but its probably a lot more than you doctor knows about.
http://theses.gla.ac.uk/3646/

Abstract

Background and Purpose: Sleep problems are commonly reported by stroke patients. Poor sleep quality can detrimentally impact upon multiple clinical variables, including mood, physical health, cognition and the rehabilitation process itself. However, the relationship between sleep and stroke is complex and not fully understood. Pre-sleep cognitions and pre-sleep arousal have been proposed as contributing factors in sleep disturbance within the general population and this novel study investigates these variables as potential factors associated with sleep post-stroke. Methods: Stroke rehabilitation inpatients (N=21) were classified as good or poor sleepers using the Pittsburgh Sleep Quality Index (PSQI) and compared using measures of pre-sleep cognitions and pre-sleep arousal; relevant factors including daytime sleepiness, fatigue, mood and environmental disturbance were also explored. Results: Poor sleepers reported a significantly higher level of pre-sleep cognitions, pre-sleep cognitive arousal, fatigue and mood disturbance than good sleepers. The level of daytime sleepiness and perceptions of environmental disturbance did not differ significantly between groups. Conclusions: This study revealed a high level of poor sleep within the current sample (48%) based on the PSQI and pre-sleep cognitions and cognitive arousal appear potentially important factors in sleep quality post-stroke. Theoretical and practical implications and future directions for research are discussed.

BCL6 controls neurogenesis through Sirt1-dependent epigenetic repression of selective Notch targets

So have your researcher notch up another mark for his/her figurative gun.
http://www.nature.com/neuro/journal/vaop/ncurrent/full/nn.3264.html

Abstract

• Abstract
• Author information
• Supplementary information

During neurogenesis, neural stem/progenitor cells (NPCs) undergo an irreversible fate transition to become neurons. The Notch pathway is important for this process, and repression of Notch-dependent Hes genes is essential for triggering differentiation. However, Notch signaling often remains active throughout neuronal differentiation, implying a change in the transcriptional responsiveness to Notch during the neurogenic transition. We identified Bcl6, an oncogene, as encoding a proneurogenic factor that is required for proper neurogenesis of the mouse cerebral cortex. BCL6 promoted the neurogenic conversion by switching the composition of Notch-dependent transcriptional complexes at the Hes5 promoter. BCL6 triggered exclusion of the co-activator Mastermind-like 1 and recruitment of the NAD⁺-dependent deacetylase Sirt1, which was required for BCL6-dependent neurogenesis. The resulting epigenetic silencing of Hes5 led to neuronal differentiation despite active Notch signaling. Our findings suggest a role for BCL6 in neurogenesis and uncover Notch-BCL6-Sirt1 interactions that may affect other aspects of physiology and disease.