Depression after stroke is a distressing problem that may be associated with other negative health outcomes.
To estimate the natural history, predictors and outcomes of depression after stroke.
...Studies published up to 31 August 2011 were searched and reviewed according to accepted criteria.
Out of 13 558 references initially found, 50 studies were included. Prevalence of depression was 29% (95% CI 25-32), and remains stable up to 10 years after stroke, with a cumulative incidence of 39-52% within 5 years of stroke. The rate of recovery from depression among patients depressed a few months after stroke ranged from 15 to 57% 1 year after stroke. Major predictors of depression are disability, depression pre-stroke, cognitive impairment, stroke severity and anxiety. Lower quality of life, mortality and disability are independent outcomes of depression after stroke.
Interventions for depression and its potential outcomes are required.
Summary The latest evidence on socioeconomic status and stroke shows that stroke not only disproportionately affects low-income and middle-income countries, but also socioeconomically deprived ...populations within high-income countries. These disparities are reflected not only in risk of stroke but also in short-term and long-term outcomes after stroke. Increased average levels of conventional risk factors (eg, hypertension, hyperlipidaemia, excessive alcohol intake, smoking, obesity, and sedentary lifestyle) in populations with low socioeconomic status account for about half of these effects. In many countries, evidence shows that people with lower socioeconomic status are less likely to receive good-quality acute hospital and rehabilitation care than people with higher socioeconomic status. For clinical practice, better implementation of well established treatments, effective management of risk factors, and equity of access to high-quality acute stroke care and rehabilitation will probably reduce inequality substantially. Overcoming barriers and adapting evidence-based interventions to different countries and health-care settings remains a research priority.
The nucleus accumbens is a major input structure of the basal ganglia and integrates information from cortical and limbic structures to mediate goal-directed behaviors. Chronic exposure to several ...classes of drugs of abuse disrupts plasticity in this region, allowing drug-associated cues to engender a pathologic motivation for drug seeking. A number of alterations in glutamatergic transmission occur within the nucleus accumbens after withdrawal from chronic drug exposure. These drug-induced neuroadaptations serve as the molecular basis for relapse vulnerability. In this review, we focus on the role that glutamate signal transduction in the nucleus accumbens plays in addiction-related behaviors. First, we explore the nucleus accumbens, including the cell types and neuronal populations present as well as afferent and efferent connections. Next we discuss rodent models of addiction and assess the viability of these models for testing candidate pharmacotherapies for the prevention of relapse. Then we provide a review of the literature describing how synaptic plasticity in the accumbens is altered after exposure to drugs of abuse and withdrawal and also how pharmacological manipulation of glutamate systems in the accumbens can inhibit drug seeking in the laboratory setting. Finally, we examine results from clinical trials in which pharmacotherapies designed to manipulate glutamate systems have been effective in treating relapse in human patients. Further elucidation of how drugs of abuse alter glutamatergic plasticity within the accumbens will be necessary for the development of new therapeutics for the treatment of addiction across all classes of addictive substances.
Clinical research has been central to the global response to COVID-19, and the United Kingdom (UK), with its research system embedded within the National Health Service (NHS), has been singled out ...globally for the scale and speed of its COVID-19 research response. This paper explores the impacts of COVID-19 on clinical research in an NHS Trust and how the embedded research system was adapted and repurposed to support the COVID-19 response. Using a multi-method qualitative case study of a research-intensive NHS Trust in London UK, we collected data through a questionnaire (n = 170) and semi-structured interviews (n = 24) with research staff working in four areas: research governance; research leadership; research delivery; and patient and public involvement. We also observed key NHS Trust research prioritisation meetings (40 hours) and PPI activity (4.5 hours) and analysed documents produced by the Trust and national organisation relating to COVID-19 research. Data were analysed for a descriptive account of the Trust's COVID-19 research response and research staff's experiences. Data were then analysed thematically. Our analysis identifies three core themes: centralisation; pace of work; and new (temporary) work practices. By centralising research prioritisation at both national and Trust levels, halting non-COVID-19 research and redeploying research staff, an increased pace in the setup and delivery of COVID-19-related research was possible. National and Trust-level responses also led to widescale changes in working practices by adapting protocols and developing local processes to maintain and deliver research. These were effective practical solutions borne out of necessity and point to how the research system was able to adapt to the requirements of the pandemic. The Trust and national COVID-19 response entailed a rapid large-scale reorganisation of research staff, research infrastructures and research priorities. The Trust's local processes that enabled them to enact national policy prioritising COVID-19 research worked well, especially in managing finite resources, and also demonstrate the importance and adaptability of the research workforce. Such findings are useful as we consider how to adapt our healthcare delivery and research practices both at the national and global level for the future. However, as the pandemic continues, research leaders and policymakers must also take into account the short and long term impact of COVID-19 prioritisation on non-COVID-19 health research and the toll of the emergency response on research staff.
Stroke is a common long-term condition with an increasing incidence as the population ages. This study evaluates temporal changes in the prevalence of cognitive impairment after first-ever stroke ...stratified by sociodemography, vascular risk factors, and stroke subtypes, up to 15 years after stroke.
Data were collected between 1995 and 2010 (n=4212) from the community-based South London Stroke Register covering an inner-city multiethnic population of 271 817 inhabitants. Patients were assessed for cognitive function using Abbreviated Mental Test or Mini-Mental State Examination at the onset, 3 months, and annually thereafter. All estimates were age adjusted to the European standard.
The overall prevalence of cognitive impairment 3 months after stroke and at annual follow-up remained relatively unchanged at 22% (24% 95% CI, 21.2-27.8 at 3 months; 22% 17.4-26.8 at 5 years to 21% 3.6-63.8 at 14 years). In multivariate analyses, the poststroke prevalence ratio of cognitive impairment increased with older age (2% 1-3 for each year of age), ethnicity (2.2 1.65-2.89-fold higher among black group) and socioeconomic status (42% 8-86 increased among manual workers). A significant, progressive trend of cognitive impairment was observed among patients with small vessel occlusion and lacunar infarction (average annual percentage change: 10% 7.9-12.8 and 2% 0.3-2.7, respectively, up to 5 years after stroke).
The prevalence of cognitive impairment after stroke remains persistently high over time, with variations being predominantly explained by sociodemographic characteristics. Given population growth and ageing demographics, effective preventive strategies and poststroke surveillance are needed to manage survivors with cognitive impairment.
Health Lifestyles in Late Middle Age Cockerham, William C.; D. Wolfe, Joseph; Bauldry, Shawn
Research on aging,
01/2020, Letnik:
42, Številka:
1
Journal Article
Recenzirano
Odprti dostop
A growing body of work identifies distinct health lifestyles among children, adolescents, and young adults and documents important social correlates. This study contributes to that line of research ...by identifying the health lifestyles of U.S. adults entering late middle age, assessing structural predictors of membership in different health lifestyles in this understudied age-group, and examining net associations between health lifestyles, chronic conditions, and physical health. The data come from the National Longitudinal Survey of Youth 1979 50+ Health Module. The analysis is based on respondents who answered the 50+ Health Module in 2008, 2010, 2012, or 2014 (N = 7,234). The results confirm similar relationships between health lifestyles and structural factors like class, gender, and race that prior studies observe and also reveal a unique pattern of associations between health lifestyle and health status because of diagnosed conditions that impact health behaviors in adulthood.
Machine learning (ML) has attracted much attention with the hope that it could make use of large, routinely collected datasets and deliver accurate personalised prognosis. The aim of this systematic ...review is to identify and critically appraise the reporting and developing of ML models for predicting outcomes after stroke. We searched PubMed and Web of Science from 1990 to March 2019, using previously published search filters for stroke, ML, and prediction models. We focused on structured clinical data, excluding image and text analysis. This review was registered with PROSPERO (CRD42019127154). Eighteen studies were eligible for inclusion. Most studies reported less than half of the terms in the reporting quality checklist. The most frequently predicted stroke outcomes were mortality (7 studies) and functional outcome (5 studies). The most commonly used ML methods were random forests (9 studies), support vector machines (8 studies), decision trees (6 studies), and neural networks (6 studies). The median sample size was 475 (range 70-3184), with a median of 22 predictors (range 4-152) considered. All studies evaluated discrimination with thirteen using area under the ROC curve whilst calibration was assessed in three. Two studies performed external validation. None described the final model sufficiently well to reproduce it. The use of ML for predicting stroke outcomes is increasing. However, few met basic reporting standards for clinical prediction tools and none made their models available in a way which could be used or evaluated. Major improvements in ML study conduct and reporting are needed before it can meaningfully be considered for practice.
The global epidemiological shift of disease burden towards long-term conditions means understanding long-term outcomes of cardiovascular disease is increasingly important. More people are surviving ...stroke to experience its long-term consequences, but outcomes in people living more >10 years after stroke have not been described in detail.
Data were collected for the population-based South London Stroke Register, with participants followed up annually until death. Outcomes were survival, disability, activity, cognitive impairment, quality of life, depression and anxiety.
Of 2625 people having first-ever stroke, 262 (21%) survived to 15 years. By 15 years, 61% (95% CI 55% to 67%) of the survivors were male, with a median age of stroke onset of 58 years (IQR 48-66). 87% of the 15-year survivors were living at home and 33.8% (26.2% to 42.4%) had mild disability, 14.3% (9.2% to 21.4%) moderate disability and 15.0% (9.9% to 22.3%) severe disability. The prevalence of disability increased with time but 1 in 10 of the 15-year survivors had lived with moderate-severe disability since their stroke. At 15 years, the prevalence of cognitive impairment was 30.0% (19.5% to 43.1%), depression 39.1% (30.9% to 47.9%) and anxiety 34.9% (27.0% to 43.8%), and survivors reported greater loss of physical than mental quality of life.
One in five people live at least 15 years after a stroke and poor functional, cognitive and psychological outcomes affect a substantial proportion of these long-term survivors. As the global population of individuals with cardiovascular long-term conditions grows, research and health services will need to increasingly focus on preventing and managing the long-term consequences of stroke.
To identify explanatory factors for the association between depression and increased mortality up to 5 years after stroke.
In this cohort study, data from the South London Stroke Register (1998-2013) ...were used. Patients (n = 3,722) were assessed at stroke onset. Baseline data included sociodemographics and stroke severity. Follow-up at 3 months included assessment for depression with the Hospital Anxiety and Depression Scale (scores ≥7 = depression). Associations between depression at 3 months and mortality within 5 years of stroke were estimated with Cox regression models adjusted for age, sex, ethnicity, and stroke severity, and subsequently adjusted for possible explanatory factors for the association. These factors, introduced into the model individually, included comorbidities at baseline, smoking and alcohol use, compliance with medication, treatment with selective serotonin reuptake inhibitors (SSRIs), social support, and activities of daily living at 3 months.
A total of 1,354 survivors were assessed at 3 months: 435 (32.1%) had depression and 331 (24.4%) died within 5 years. Survivors with depression had a greater risk of mortality (hazard ratio HR 1.41 95% confidence interval (CI) 1.13-1.77; p = 0.002). The association between depression and mortality was strongest in patients younger than 65 years. Adjustment for comorbidities, smoking and alcohol use, SSRI use, social support, and compliance with medication did not change these associations. SSRIs started after stroke were associated with higher mortality, independently of depression at 3 months (HR 1.72 95% CI 1.34-2.20; p < 0.001).
Depression after stroke is associated with higher mortality, particularly among younger patients. Stroke survivors taking SSRIs have an increased mortality. The association between depression and mortality is not explained by other individual medical factors.
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