Story of dataset:

You can reach the original source of the data from the link. Original Dataset

While working on this data, there were 210 missing data available in a single numeric column. I've tried handling missing values, treating this just like a regression problem. I got some very interesting results. And I have even seen a similar designation in one of the top-rated notebooks, but there had been no performance evaluation for this designation and I considered it a shortcoming. Then considering R_square, I thought that the pipeline-based data assignment actually had a very low result and shouldn't even be considered a regression because the result was less than 0.3 :

Instead, I found the most suitable algorithms (GradientBoostingRegressor, CatBoost, Light_GBM, and XGBoost) for a similar assignment with PyCaret, optimized it with optuna, and then developed a model using the blend method.

Validation data values with this model;

R_sqr: 0.3035774217245104

MSE: 32.60242593653817

I know still low, but this model showed the highest performance among all models. Then I wanted to make it available to all Kagglers.

In terms of model estimation, you can try my previous notebook: My notebook

**## Context: **

A stroke is a medical condition in which poor blood flow to the brain causes cell death. There are two main types of stroke: ischemic, due to lack of blood flow, and hemorrhagic, due to bleeding. Both cause parts of the brain to stop functioning properly. Signs and symptoms of a stroke may include an inability to move or feel on one side of the body, problems understanding or speaking, dizziness, or loss of vision to one side. Signs and symptoms often appear soon after the stroke has occurred. If symptoms last less than one or two hours, the stroke is a transient ischemic attack (TIA), also called a mini-stroke. A hemorrhagic stroke may also be associated with a severe headache. The symptoms of a stroke can be permanent. Long-term complications may include pneumonia and loss of bladder control.

The main risk factor for stroke is high blood pressure. Other risk factors include high blood cholesterol, tobacco smoking, obesity, diabetes mellitus, a previous TIA, end-stage kidney disease, and atrial fibrillation. An ischemic stroke is typically caused by blockage of a blood vessel, though there are also less common causes. A hemorrhagic stroke is caused by either bleeding directly into the brain or into the space between the brain's membranes. Bleeding may occur due to a ruptured brain aneurysm. Diagnosis is typically based on a physical exam and supported by medical imaging such as a CT scan or MRI scan. A CT scan can rule out bleeding, but may not necessarily rule out ischemia, which early on typically does not show up on a CT scan. Other tests such as an electrocardiogram (ECG) and blood tests are done to determine risk factors and rule out other possible causes. Low blood sugar may cause similar symptoms.

Prevention includes decreasing risk factors, surgery to open up the arteries to the brain in those with problematic carotid narrowing, and warfarin in people with atrial fibrillation. Aspirin or statins may be recommended by physicians for prevention. A stroke or TIA often requires emergency care. An ischemic stroke, if detected within three to four and half hours, may be treatable with a medication that can break down the clot. Some hemorrhagic strokes benefit from surgery. Treatment to attempt recovery of lost function is called stroke rehabilitation, and ideally takes place in a stroke unit; however, these are not available in much of the world.

##Attribute Information

1) gender: "Male", "Female" or "Other" 2) age: age of the patient 3) hypertension: 0 if the patient doesn't have hypertension, 1 if the patient has hypertension 4) heart_disease: 0 if the patient doesn't have any heart diseases, 1 if the patient has a heart disease 5) ever_married: "No" or "Yes" 6) work_type: "children", "Govt_jov", "Never_worked", "Private" or "Self-employed" 7) Residence_type: "Rural" or "Urban" 8) avg_glucose_level: average glucose level in blood 9) bmi: body mass index 10) smoking_status: "formerly smoked", "never smoked", "smokes" or "Unknown"* 11) stroke: 1 if the patient had a stroke or 0 if not

*Note: "Unknown" in smoking_status means that the information is unavailable for this patient

Real data sources:

Data IEEE article

2019 - 2021, county-level U.S. stroke death rates. Dataset developed by the Centers for Disease Control and Prevention, Division for Heart Disease and Stroke Prevention.Create maps of U.S. stroke death rates by county. Data can be stratified by age, race/ethnicity, and sex.Visit the CDC Atlas of Heart Disease and Stroke for additional data and maps. Atlas of Heart Disease and StrokeData SourceMortality data were obtained from the National Vital Statistics System. Bridged-Race Postcensal Population Estimates were obtained from the National Center for Health Statistics. International Classification of Diseases, 10th Revision (ICD-10) codes: I60-I69; underlying cause of death.Data DictionaryData for counties with small populations are not displayed when a reliable rate could not be generated. These counties are represented in the data with values of '-1.' CDC excludes these values when classifying the data on a map, indicating those counties as 'Insufficient Data.'Data field names and descriptionsstcty_fips: state FIPS code + county FIPS codeOther fields use the following format: RRR_S_aaaa (e.g., API_M_35UP)  RRR: 3 digits represent race/ethnicity    All - Overall    AIA - American Indian and Alaska Native, non-Hispanic    ASN - Asian, non-Hispanic    BLK - Black, non-Hispanic    HIS - Hispanic NHP – Native Hawaiian or Other Pacific Islander, non-Hispanic MOR – More than one race, non-Hispanic    WHT - White, non-Hispanic  S: 1 digit represents sex    A - All    F - Female    M - Male  aaaa: 4 digits represent age. The first 2 digits are the lower bound for age and the last 2 digits are the upper bound for age. 'UP' indicates the data includes the maximum age available and 'LT' indicates ages less than the upper bound. Example: The column 'BLK_M_65UP' displays rates per 100,000 black men aged 65 years and older.MethodologyRates are calculated using a 3-year average and are age-standardized in 10-year age groups using the 2000 U.S. Standard Population. Rates are calculated and displayed per 100,000 population. Rates were spatially smoothed using a Local Empirical Bayes algorithm to stabilize risk by borrowing information from neighboring geographic areas, making estimates more statistically robust and stable for counties with small populations. Data for counties with small populations are coded as '-1' when a reliable rate could not be generated. County-level rates were generated when the following criteria were met over a 3-year time period within each of the filters (e.g., age, race, and sex).At least one of the following 3 criteria:At least 20 events occurred within the county and its adjacent neighbors.ORAt least 16 events occurred within the county.ORAt least 5,000 population years within the county.AND all 3 of the following criteria:At least 6 population years for each age group used for age adjustment if that age group had 1 or more event.The number of population years in an age group was greater than the number of events.At least 100 population years within the county.More Questions?Interactive Atlas of Heart Disease and StrokeData SourcesStatistical Methods

Analysis of ‘Stroke Deaths 30 days hospital admission.’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/mpwolke/cusersmarildownloadsstrokecsv on 14 February 2022.

--- Dataset description provided by original source is as follows ---

Context

Deaths occurring in hospital and after discharge between 0 and 29 days (inclusive) of an emergency admission to hospital with a stroke. This indicator is available for males, females and persons at the following breakdowns: England/Region of residence/Local authority of residence/County of residence/London authority of residence/Provider

https://digital.nhs.uk/data-and-information/publications/clinical-indicators/compendium-of-population-health-indicators/compendium-hospital-care/current/deaths-within-30-days/deaths-within-30-days-of-emergency-admission-to-hospital-stroke-indirectly-standardised-rate-all-ages-annual-trend-f-m-p#

General Enquiries: enquiries@nhsdigital.nhs.uk

Freedom of Information (FOI) requests : FOI Enquiries enquiries@nhsdigital.nhs.uk https://digital.nhs.uk/about-nhs-digital/contact-us/freedom-of-information

Content

Some people with stroke die before they can be admitted to hospital. There are variations in death rates among those who survive long enough to be admitted, and some of these deaths may potentially be preventable. The National Service Framework for older people cites evidence that people who have strokes are more likely to survive if admitted promptly to a hospital-based stroke unit with treatment and care provided by a specialist coordinated stroke team within an integrated service. The National Health Service (NHS) may be helped to prevent some of these deaths by seeing comparative figures and learning lessons from follow-up investigations.

Legacy unique identifier: P02167

Acknowledgements

digital.nhs.uk

Photo by K. Mitch Hodge on Unsplash

Inspiration

Stroke cases among Covid-19 patients outside the risk group that puzzle American doctors. Studies attempt to unravel the relationship between strokes and younger people infected with coronavirus

--- Original source retains full ownership of the source dataset ---

Years of life lost due to mortality from stroke (ICD-10 I60-I69). Years of life lost (YLL) is a measure of premature mortality. Its primary purpose is to compare the relative importance of different causes of premature death within a particular population and it can therefore be used by health planners to define priorities for the prevention of such deaths. It can also be used to compare the premature mortality experience of different populations for a particular cause of death. The concept of years of life lost is to estimate the length of time a person would have lived had they not died prematurely. By inherently including the age at which the death occurs, rather than just the fact of its occurrence, the calculation is an attempt to better quantify the burden, or impact, on society from the specified cause of mortality. Legacy unique identifier: P00323

Mortality from stroke (ICD-10 I60-I69, equivalent to ICD-9 430-438). To reduce deaths from stroke. Legacy unique identifier: P00679

This dataset includes 24,783 older people (aged 65 years and older) and 29,333 spells, designed to support research which improves cerebrovascular events and unplanned care for older people.

All patients admitted to hospital from year 2000 and onwards, curated to focus on Stroke. Longitudinal & individually linked, so that the preceding & subsequent health journey can be mapped & healthcare utilisation prior to & after admission understood. The dataset includes highly granular patient demographics & co-morbidities taken from ICD-10 & SNOMED-CT codes. Serial, structured data pertaining to acute care process (mortality, timings and wards). Along with presenting complaints, microbiology results, referrals, therapies, all physiology readings (pulse, blood pressure, respiratory rate, oxygen saturations and others), all blood results (urea, albumin, platelets, white blood cells and others). Includes all prescribed & administered treatments and all outcomes. Linked images are also available (radiographs, CT scans, MRI).

Geography: The West Midlands (WM) has a population of 6 million & includes a diverse ethnic & socio-economic mix. UHB is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & > 120 ITU bed capacity. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”.

Data set availability: Data access is available via the PIONEER Hub for projects which will benefit the public or patients. This can be by developing a new understanding of disease, by providing insights into how to improve care, or by developing new models, tools, treatments, or care processes. Data access can be provided to NHS, academic, commercial, policy and third sector organisations. Applications from SMEs are welcome. There is a single data access process, with public oversight provided by our public review committee, the Data Trust Committee. Contact pioneer@uhb.nhs.uk or visit www.pioneerdatahub.co.uk for more details.

Available supplementary data: Matched controls; ambulance and community data. Unstructured data (images). We can provide the dataset in OMOP and other common data models and can build synthetic data to meet bespoke requirements.

Available supplementary support: Analytics, model build, validation & refinement; A.I. support. Data partner support for ETL (extract, transform & load) processes. Bespoke and “off the shelf” Trusted Research Environment (TRE) build and run. Consultancy with clinical, patient & end-user and purchaser access/ support. Support for regulatory requirements. Cohort discovery. Data-driven trials and “fast screen” services to assess population size.

Cardiovascular diseases (CVDs) are the leading cause of death globally, encompassing conditions like coronary heart disease, cerebrovascular disease, rheumatic heart disease, and other heart and blood vessel disorders. According to the World Health Organization, 17.9 million people die from CVDs annually. Heart attacks and strokes account for over 80% of these deaths, with one-third occurring before the age of 70. A comprehensive dataset has been created to analyze factors that contribute to heart attacks. This dataset contains 1,319 samples with nine fields: eight input variables and one output variable. The input variables include age, gender (0 for female, 1 for male), heart rate, systolic blood pressure (pressurehight), diastolic blood pressure (pressurelow), blood sugar (glucose), CK-MB (kcm), and Test-Troponin (troponin). The output variable indicates the presence or absence of a heart attack, categorized as either negative (no heart attack) or positive (heart attack).

SUMMARYThis analysis, designed and executed by Ribble Rivers Trust, identifies areas across England with the greatest levels of stroke and transient ischaemic attack (in persons of all ages). Please read the below information to gain a full understanding of what the data shows and how it should be interpreted.ANALYSIS METHODOLOGYThe analysis was carried out using Quality and Outcomes Framework (QOF) data, derived from NHS Digital, relating to stroke and transient ischaemic attack (in persons of all ages).This information was recorded at the GP practice level. However, GP catchment areas are not mutually exclusive: they overlap, with some areas covered by 30+ GP practices. Therefore, to increase the clarity and usability of the data, the GP-level statistics were converted into statistics based on Middle Layer Super Output Area (MSOA) census boundaries.The percentage of each MSOA’s population (all ages) to have suffered a stroke or transient ischaemic attack was estimated. This was achieved by calculating a weighted average based on:The percentage of the MSOA area that was covered by each GP practice’s catchment areaOf the GPs that covered part of that MSOA: the percentage of registered patients that have that illness The estimated percentage of each MSOA’s population to have suffered a stroke or transient ischaemic attack was then combined with Office for National Statistics Mid-Year Population Estimates (2019) data for MSOAs, to estimate the number of people in each MSOA who have suffered a stroke or transient ischaemic attack, within the relevant age range.Each MSOA was assigned a relative score between 1 and 0 (1 = worst, 0 = best) based on:A) the PERCENTAGE of the population within that MSOA who are estimated to have had a stroke or transient ischaemic attackB) the NUMBER of people within that MSOA who are estimated to have had a stroke or transient ischaemic attackAn average of scores A & B was taken, and converted to a relative score between 1 and 0 (1= worst, 0 = best). The closer to 1 the score, the greater both the number and percentage of the population in the MSOA that are estimated to have had a stroke or transient ischaemic attack, compared to other MSOAs. In other words, those are areas where it’s estimated a large number of people suffer from stroke and transient ischaemic attack, and where those people make up a large percentage of the population, indicating there is a real issue with stroke and transient ischaemic attack within the population and the investment of resources to address that issue could have the greatest benefits.LIMITATIONS1. GP data for the financial year 1st April 2018 – 31st March 2019 was used in preference to data for the financial year 1st April 2019 – 31st March 2020, as the onset of the COVID19 pandemic during the latter year could have affected the reporting of medical statistics by GPs. However, for 53 GPs (out of 7670) that did not submit data in 2018/19, data from 2019/20 was used instead. Note also that some GPs (997 out of 7670) did not submit data in either year. This dataset should be viewed in conjunction with the ‘Health and wellbeing statistics (GP-level, England): Missing data and potential outliers’ dataset, to determine areas where data from 2019/20 was used, where one or more GPs did not submit data in either year, or where there were large discrepancies between the 2018/19 and 2019/20 data (differences in statistics that were > mean +/- 1 St.Dev.), which suggests erroneous data in one of those years (it was not feasible for this study to investigate this further), and thus where data should be interpreted with caution. Note also that there are some rural areas (with little or no population) that do not officially fall into any GP catchment area (although this will not affect the results of this analysis if there are no people living in those areas).2. Although all of the obesity/inactivity-related illnesses listed can be caused or exacerbated by inactivity and obesity, it was not possible to distinguish from the data the cause of the illnesses in patients: obesity and inactivity are highly unlikely to be the cause of all cases of each illness. By combining the data with data relating to levels of obesity and inactivity in adults and children (see the ‘Levels of obesity, inactivity and associated illnesses: Summary (England)’ dataset), we can identify where obesity/inactivity could be a contributing factor, and where interventions to reduce obesity and increase activity could be most beneficial for the health of the local population.3. It was not feasible to incorporate ultra-fine-scale geographic distribution of populations that are registered with each GP practice or who live within each MSOA. Populations might be concentrated in certain areas of a GP practice’s catchment area or MSOA and relatively sparse in other areas. Therefore, the dataset should be used to identify general areas where there are high levels of stroke and transient ischaemic attack, rather than interpreting the boundaries between areas as ‘hard’ boundaries that mark definite divisions between areas with differing levels of stroke and transient ischaemic attack.TO BE VIEWED IN COMBINATION WITH:This dataset should be viewed alongside the following datasets, which highlight areas of missing data and potential outliers in the data:Health and wellbeing statistics (GP-level, England): Missing data and potential outliersLevels of obesity, inactivity and associated illnesses (England): Missing dataDOWNLOADING THIS DATATo access this data on your desktop GIS, download the ‘Levels of obesity, inactivity and associated illnesses: Summary (England)’ dataset.DATA SOURCESThis dataset was produced using:Quality and Outcomes Framework data: Copyright © 2020, Health and Social Care Information Centre. The Health and Social Care Information Centre is a non-departmental body created by statute, also known as NHS Digital.GP Catchment Outlines. Copyright © 2020, Health and Social Care Information Centre. The Health and Social Care Information Centre is a non-departmental body created by statute, also known as NHS Digital. Data was cleaned by Ribble Rivers Trust before use.COPYRIGHT NOTICEThe reproduction of this data must be accompanied by the following statement:© Ribble Rivers Trust 2021. Analysis carried out using data that is: Copyright © 2020, Health and Social Care Information Centre. The Health and Social Care Information Centre is a non-departmental body created by statute, also known as NHS Digital.CaBA HEALTH & WELLBEING EVIDENCE BASEThis dataset forms part of the wider CaBA Health and Wellbeing Evidence Base.

Objective: To determine contemporary trends in case fatality, discharge destination, and admission to long-term care after acute ischemic stroke and intracerebral hemorrhage (ICH) in a large, population-based cohort.

Methods: We used linked administrative data to identify all emergency department visits and hospital admissions for first-ever ischemic stroke or ICH in Ontario, Canada from 2003-2017. We calculated crude and age/sex-standardized risk of death at 30 days and 1 year from stroke onset. We stratified crude trends by stroke type, age, and sex and used the Kendall τ-b correlation coefficient to evaluate the significance of trends. We determined trends in discharge home and to rehabilitation, and admission to long-term care at 1 year. We used Cox proportional hazard and logistic regression models to assess whether trends in outcomes persisted after adjustment for baseline factors, estimated stroke severity, and use of life-sustaining care.

Results: There were 163,574 people with acute ischemic stroke or ICH across the study period. Between 2003 and 2017, age/sex-standardized 30-day stroke case fatality decreased from 20.5% to 13.2% (7.3% absolute and 36% relative reduction) while that at 1 year decreased from 32.2 to 22.8 (9.3% absolute and 29% relative reduction). Findings were consistent across age, sex, and stroke type, and after adjustment for comorbid conditions, stroke severity and use of life-sustaining care. There was a reduction in long-term care admission after ischemic stroke, and an increase in discharge home or to rehabilitation for both stroke types.

Conclusion: We observed substantial reductions in acute stroke case fatality from 2003-2017 with a concurrent increase in discharge to home or rehabilitation and a decrease in long-term care admissions, suggesting continuous improvements in stroke systems of care.

Introduction: Stroke is the third leading condition with the highest mortality and death and is a major cause of disability. The estimated number of deaths due to stroke is about 5.71 million people as per the WHO data, and is estimated to peak at 7.8 million in 2030. Citicoline is believed to exert neuroprotection and neurorestoration intracellularly by supporting cellular phospholipid synthesis. Citicoline is used in Acute Ischemic Stroke patients, however, there is not enough statistical evidence available for the benefits of the same. Hence, this calls for a meta-analysis of RCTs on a larger scale to generate highest level of scientific evidence regarding the controversial negative studies of Citicoline when tested against placebo. Objectives: To review and analyze statistical evidence from existing randomized controlled trials, the therapeutic efficacy of Citicoline in acute ischemic stroke (AIS) patients. Materials & Methods: A total of 17 studies, involving 5127 patients were included. The studies were double-blind, randomized, and placebo-controlled clinical trials studying the effect of Citicoline on patients with Acute Ischemic Stroke. The included patients suffered from an Acute Ischemic Stroke with a minimum therapeutic window of 6 hours. The treatments tested were either Citicoline, with doses ranging from 250 to 4000 mg daily, or placebo. The duration of the treatment ranged from 10 days to 9 weeks. The principal summary measures were the Odd’s Ratio(OR) and Relative Risk (RR). For the measurement of treatment effect Rev Man 5.4.1 version software by Cochrane Database will be utilized to calculate Odd’s ratio and Relative Risk(RR). Results: The RR(for 5127 participants) was 1.301(random effect model) and 1.163(fixed effect model)[95% confidence interval (CI) 1.081 to 1.2521 (fixed effect model), p<0.001] and the overall OR(for 5127 participants) was 1.769(random effect model) and 1.281(fixed effect model)[95% confidence interval (CI) 1.137 to 1.443 (fixed effect model), p<0.001], indicating a slight advantage of Citicoline over placebo treatment. Citicoline was also found to be associated with a less number of adverse events and deaths compared to placebo. Conclusion: Citicoline is proven to be slightly more efficacious than placebo, with lesser adverse events and deaths. However the margin of benefit is narrow. Future trials comparing the same, on higher number of patients is necessary to draw a final conclusion on it.

Key Words: Citicoline, Placebo, Stroke, Randomized Controlled Trials, Meta Analysis

The objective was to determine the association between material deprivation and direct healthcare costs and clinical outcomes following stroke in the context of a publicly funded universal healthcare system. In this population-based cohort study of patients with ischemic and hemorrhagic stroke admitted to hospital between 2008 and 2017 in Ontario, Canada, we used linked administrative data to identify the cohort, predictor variables, and outcomes. The exposure was a five-level neighborhood material deprivation index. The primary outcome was direct healthcare costs incurred by the public payer in the first year. Secondary outcomes were death and admission to long-term care. Among 90,289 patients with stroke, the mean (standard deviation) per-person costs increased with increasing material deprivation, from $50,602 ($55,582) in the least deprived quintile to $56,292 ($59,721) in the most deprived quintile (unadjusted relative cost ratio and 95% confidence intervals 1.11 [1.08,1.13] and adjusted relative cost ratio 1.07 [1.05,1.10] for least compared to most deprived quintile). People in the most deprived quintile had higher mortality within one year compared to the least deprived quintile (adjusted hazard ratio (HR) 1.07 [1.03,1.12]) as well as within three years (adjusted HR 1.09 [1.05,1.13]). Admission to long-term care increased incrementally with material deprivation and those in the most deprived quintile had an adjusted HR of 1.33 [1.24,1.43]) compared to those in the least deprived quintile. Material deprivation is a risk factor for increased costs and poor outcomes after stroke. Interventions targeting health inequities due to social determinants of health are needed.

Background: Little is known about the risk of recurrent stroke in low- and middle-income countries. This study was designed to identify the long-term risk of stroke recurrence and its associated factors. Methods: From November 21, 2006 for a period of 1 year, 624 patients with first-ever stroke (FES) were registered from the residents of 3 neighborhoods in Mashhad, Iran. Patients were followed up for the next 5 years after the index event for any stroke recurrence or death. We used competing risk analysis and cause-specific Cox proportional hazard models to estimate the cumulative incidence of stroke recurrence and its associated variables. Results: The cumulative incidence of stroke recurrence was 14.5% by the end of 5 years, with the largest rate during the first year after FES (5.6%). Only advanced age (adjusted hazard ratio [HR] 1.02; 95% CI 1.01–1.04) and severe stroke (National Institutes of Health Stroke Scale score >20; HR 2.23; 95% CI 1.05–4.74) were independently associated with an increased risk of 5-year recurrence. Case fatality at 30 days after first recurrent stroke was 43.2%, which was significantly greater than the case fatality at 30 days after FES of 24.7% (p = 0.001). Conclusion: A substantial number of our patients either died or had stroke recurrences during the study period. Advanced age and the severity of the index stroke significantly increased the risk of recurrence. This is an important finding for health policy makers and for designing preventive strategies in people surviving their stroke.

Objective: We examined fatal and non-fatal Serious Adverse Events (SAEs) at 14 days within AVERT.

Method: A prospective, parallel group, assessor blinded, randomized international clinical trial comparing very early intensive mobilization training (VEM) with usual care (UC); with follow up to 3 months. Included: Patients with ischaemic and haemorrhagic stroke within 24 hours of onset and physiological parameters within set limits. Treatment with thrombolytics allowed. Excluded: Patients with severe premorbid disability and/or comorbidities. Interventions continued for 14 days or hospital discharge if less. The primary early safety outcome was fatal SAEs within 14 days. Secondary outcomes were non-fatal SAEs classified as neurological, immobility-related, and other. Mortality influences were assessed using binary logistic regression adjusted for baseline stroke severity (NIHSS) and age.

Results: 2104 participants were randomized to VEM (n=1054) or UC (n=1050) with a median age of 72 years (IQR 63-80) and NIHSS 7 (IQR 4-12). By 14 days, 48 had died in VEM, 32 in UC, adjusted Odds Ratio of 1.76 (95% CI 1.06-2.92, P=.029). Stroke progression was more common in VEM. Exploratory subgroup analyses showed higher odds of death in those; >80 years, and with intracerebral haemorrhage, but there was no significant treatment by subgroup interaction. No difference in non-fatal SAEs found.

Conclusions: While the overall case fatality at 14 days post-stroke was only 3.8%, age and severity adjusted mortality was increased with high dose, intensive training compared to usual care. Stroke progression was more common in VEM. Data suggests that older people and those with intracerebral haemorrhage are at higher risk.

Classification of Evidence: This study provides Class I evidence that very early mobilization increases mortality at 14 days post stroke

The decrease in stroke mortality will increase the burden of survivors with functional dependence (FD). The aim of this study was to evaluate how many patients become functionally dependent over 3 years after an incident event in Joinville, Brazil. The proportion of FD (defined as a modified Rankin score 3 to 5) among stroke survivors from the Joinville Stroke Registry was assessed using a validated telephone interview. Incidence of FD after stroke in Joinville in one year was 23.24 per 100,000 population. The overall proportion of FD among stroke survivors at discharge was 32.7%. Of 303 patients with first-ever ischaemic stroke (IS), one-third were FD at discharge, and 12%, 9% and 8%, respectively at 1, 2 and 3 years. Among 37 patients with haemorrhagic stroke (HS), 38% were dependent at discharge, 16% after 1 and 2 years and 14% after 3. Among 27 patients with subarachnoid haemorrhage (SAH), 19% were dependent at discharge and 4% from 1 to 3 years. Among IS subtypes, cardioembolic ones had the worst risk of FD. (RR 19.8; 95% CI: 2.2 to 175.9). Our results showed that one-third of stroke survivors have FD during the first year after stroke in Brazil. Therefore, a city with half a million people might expect 120 new stroke patients with FD each year.

Mortality from hypertensive disease and stroke for ages 35-64 (ICD-10 I10-I15, I60-I69; equivalent to ICD-9 401-405, 430-438). To reduce deaths from stroke. Legacy unique identifier: P00678

The casemix adjusted standardised mortality ratio of people with known stroke type who die within 30 days of hospital admission.

Current version updated: Mar-17

Next version due: Dec-17

Objective: To analyze evidence in the literature on the main difficulties faced by elderly people diagnosed with stroke after the hospitalization period.MethodsThe process for developing this integrative review will follow the subsequent steps: formulation of the research question, search for primary studies in the literature, data extraction, evaluation of studies included in the review, analysis and synthesis of the results, and presentation of the review (MENDES; SILVEIRA; GALVÃO, 2008).Research question:For the first stage of the project, the following question is proposed: What are the main difficulties faced by the elderly diagnosed with stroke after the hospitalization period? Regarding the PICO strategy, an acronym for patient, intervention, comparison, and outcomes (SANTOS; PIMENTA; NOBRE, 2007), it will be used to design the search strategy, as described in Chart 1 below:Chart 1- Elements within the PICO strategyAcronymDefinitionDescriptionPPatient or issueElderly with a diagnosis of stroke, who were hospitalizedIIntervention or topic of interestDifficulties faced after the hospitalization periodCComparisonNot applicableOOutcome or resultQuality of life after the hospitalization periodLiterature search: The search for primary studies will be conducted online to access the databases: Latin American and Caribbean Health Sciences Literature (LILACS), National Library of Medicine and the National Institutes of Health (PubMed), Embase, in addition to an academic search engine (Google Scholar). The controlled descriptors from the Medical Subject Headings (MeSH), Embase Indexing and Emtree, and the Health Sciences Descriptors (DeCS), delimited according to each database, will be identified, to then outline a unique search strategy, adapted for each listed database. Boolean operators AND and OR will be used in the combination of cross-references among the elements of the PICO strategy, with the aim of obtaining a manageable number of studies for the conduct of the research.Study selection: To ensure methodological rigor, after conducting the search in the selected databases, the results will be exported to the reference management software (EndNote), where they will be organized and duplicate articles will be removed (MENDES; SILVEIRA; GALVÃO, 2019). For the selection of studies, they will be exported to the Rayyan software, where descriptive labels for the reasons for exclusion or inclusion of each study will be created (OUZZANI et al., 2016).Selection criteria: Primary studies addressing difficulties faced by elderly individuals diagnosed with stroke after hospitalization will be included, published in English, Portuguese, and Spanish, within the last five years (2018 to 2023). Furthermore, to ensure rigor in method execution, some recommendations from the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) will be utilized (Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement, 2015).Data extraction: For data extraction from the selected studies, we will employ a tailored script to gather details about the original article's identification, methodological characteristics, assessment of methodological rigor, and findings. Two researchers will chart the data from the included studies using this script, while another researcher will validate the information.Study evaluation: Evaluation of the studies will be guided by two priority criteria: methodological approach (quantitative or qualitative) and strength of evidence. The method identification in each included study will be conducted using the terminology provided by the authors themselves for defining the research design. In the absence of explicit method identification, the concepts outlined by Polit and Beck (2018) will be adopted. Regarding the evidence classification system, the evidence hierarchy classification by Fineout-Overholt and Melnyk (2019) will be employed, which advocates for a different classification regarding the hierarchy of evidence, according to the type of clinical question (meaning, prognosis/prediction, etiology, and intervention/treatment or diagnosis/diagnostic test) (MELNYK; FINEOUT-OVERHOLT, 2019).Analysis and synthesis of results: The presentation of data extracted from the selected studies for the Integrative Review will be performed descriptively, through the creation of a synthesis table, which will include information regarding identification, objective, and main results found in each of them, which may be grouped into thematic categories. The discussion of results will be based on publications related to the subject.Presentation of the integrative review: The results of this study will be disseminated in the academic community in the form of a scientific article indexed in a national or international database. The synthesis of the presented knowledge aims to provide data on the main difficulties faced by elderly individuals diagnosed with stroke after hospitalization, as well as methodological limitations, knowledge gaps, and directions for future research on this topic.

Objective: To examine the comparative efficacy and safety of antithrombotic treatments (apixaban, dabigatran, edoxaban, rivaroxaban and vitamin K antagonists (VKA) at a standard adjusted dose (target international normalised ratio 2.0–3.0), acetylsalicylic acid (ASA), ASA and clopidogrel) for non-valvular atrial fibrillation and among subpopulations. Design: Systematic review and network meta-analysis. Data sources: A systematic literature search strategy was designed and carried out using MEDLINE, EMBASE, the Cochrane Register of Controlled Trials and the grey literature including the websites of regulatory agencies and health technology assessment organisations for trials published in English from 1988 to January 2014. Eligibility criteria for selecting studies: Randomised controlled trials were selected for inclusion if they were published in English, included at least one antithrombotic treatment and involved patients with non-valvular atrial fibrillation eligible to receive anticoagulant therapy. Results: For stroke or systemic embolism, dabigatran 150 mg and apixaban twice daily were associated with reductions relative to standard adjusted dose VKA, whereas low-dose ASA and the combination of clopidogrel plus low-dose ASA were associated with increases. Absolute risk reductions ranged from 6 fewer events per 1000 patients treated for dabigatran 150 mg twice daily to 15 more events for clopidogrel plus ASA. For major bleeding, edoxaban 30 mg daily, apixaban, edoxaban 60 mg daily and dabigatran 110 mg twice daily were associated with reductions compared to standard adjusted dose VKA. Absolute risk reductions with these agents ranged from 18 fewer per 1000 patients treated each year for edoxaban 30 mg daily to 24 more for medium dose ASA. Conclusions: Compared with standard adjusted dose VKA, new oral anticoagulants were associated with modest reductions in the absolute risk of stroke and major bleeding. People on antiplatelet drugs experienced more strokes compared with anticoagulant drugs without any reduction in bleeding risk. To fully elucidate the comparative benefits and harms of antithrombotic agents across the various subpopulations, rigorously conducted comparative studies or network meta-regression analyses of patient-level data are required.

Deaths occurring in hospital and after discharge between 0 and 29 days (inclusive) of an emergency admission to hospital with a stroke. This indicator is available for males, females and persons at the following breakdowns: England Region of residence Local authority of residence County of residence London authority of residence Provider Some people with stroke die before they can be admitted to hospital. There are variations in death rates among those who survive long enough to be admitted, and some of these deaths may potentially be preventable. The National Service Framework for older people cites evidence that people who have strokes are more likely to survive if admitted promptly to a hospital-based stroke unit with treatment and care provided by a specialist coordinated stroke team within an integrated service. The National Health Service (NHS) may be helped to prevent some of these deaths by seeing comparative figures and learning lessons from follow-up investigations. The next release date for this indicator is to be confirmed. Legacy unique identifier: P02167

BackgroundMost of the epidemiological studies that have examined the detrimental effects of ambient hot and cold temperatures on human health have been conducted in high-income countries. In India, the limited evidence on temperature and health risks has focused mostly on the effects of heat waves and has mostly been from small scale studies. Here, we quantify heat and cold effects on mortality in India using a nationally representative study of the causes of death and daily temperature data for 2001–2013.Methods and findingsWe applied distributed-lag nonlinear models with case-crossover models to assess the effects of heat and cold on all medical causes of death for all ages from birth (n = 411,613) as well as on stroke (n = 19,753), ischaemic heart disease (IHD) (n = 40,003), and respiratory diseases (n = 23,595) among adults aged 30–69. We calculated the attributable risk fractions by mortality cause for extremely cold (0.4 to 13.8°C), moderately cold (13.8°C to cause-specific minimum mortality temperatures), moderately hot (cause-specific minimum mortality temperatures to 34.2°C), and extremely hot temperatures (34.2 to 39.7°C). We further calculated the temperature-attributable deaths using the United Nations’ death estimates for India in 2015. Mortality from all medical causes, stroke, and respiratory diseases showed excess risks at moderately cold temperature and hot temperature. For all examined causes, moderately cold temperature was estimated to have higher attributable risks (6.3% [95% empirical confidence interval (eCI) 1.1 to 11.1] for all medical deaths, 27.2% [11.4 to 40.2] for stroke, 9.7% [3.7 to 15.3] for IHD, and 6.5% [3.5 to 9.2] for respiratory diseases) than extremely cold, moderately hot, and extremely hot temperatures. In 2015, 197,000 (121,000 to 259,000) deaths from stroke, IHD, and respiratory diseases at ages 30–69 years were attributable to moderately cold temperature, which was 12- and 42-fold higher than totals from extremely cold and extremely hot temperature, respectively. The main limitation of this study was the coarse spatial resolution of the temperature data, which may mask microclimate effects.ConclusionsPublic health interventions to mitigate temperature effects need to focus not only on extremely hot temperatures but also moderately cold temperatures. Future absolute totals of temperature-related deaths are likely to depend on the large absolute numbers of people exposed to both extremely hot and moderately cold temperatures. Similar large-scale and nationally representative studies are required in other low- and middle-income countries to better understand the impact of future temperature changes on cause-specific mortality.