Ramesh Sahathevan

Interest in dementia has increased over the past few decades. Stroke is an important cause of cognitive problems. The term vascular cognitive impairment is now used to describe dementia attributed to stroke or deep white matter lesions detected on imaging. Although vascular cognitive impairment is increasingly diagnosed, Alzheimer's disease remains the most common dementia worldwide. The relationship between Alzheimer's disease and vascular cognitive impairment is unclear, although there exists significant overlap, which prompts physicians to consider them opposite ends of a disease spectrum, rather than separate entities. There is also substantial evidence that stroke risk factors such as hypertension, diabetes; lipid disorders, etc. are independently associated with an increased risk of Alzheimer's disease and vascular cognitive impairment. Evidence suggests that these risk factors have a cumulative effect on Alzheimer's disease development but not on vascular cognitive impairment. This is more marked in Alzheimer's disease patients in the presence of the ε4 allelic variant of apolipoprotein E. How these risk factors increase the risk of dementia is largely unknown. Physicians must be aware that stroke causes dementia; that vascular risk factors appear to be independent risk factors in developing dementia, and that poststroke care must include cognitive assessment.

BACKGROUND: The greatest potential to reduce the burden of stroke is by primary prevention of first-ever stroke, which constitutes three quarters of all stroke. In addition to population-wide prevention strategies (the 'mass' approach), the 'high risk' approach aims to identify individuals at risk of stroke and to modify their risk factors, and risk, accordingly. Current methods of assessing and modifying stroke risk are difficult to access and implement by the general population, amongst whom most future strokes will arise. To help reduce the burden of stroke on individuals and the population a new app, the Stroke Riskometer(TM) , has been developed. We aim to explore the validity of the app for predicting the risk of stroke compared with current best methods. METHODS: 752 stroke outcomes from a sample of 9501 individuals across three countries (New Zealand, Russia and the Netherlands) were utilized to investigate the performance of a novel stroke risk prediction tool algorithm (Stroke Riskometer(TM) ) compared with two established stroke risk score prediction algorithms (Framingham Stroke Risk Score [FSRS] and QStroke). We calculated the receiver operating characteristics (ROC) curves and area under the ROC curve (AUROC) with 95% confidence intervals, Harrels C-statistic and D-statistics for measure of discrimination, R(2) statistics to indicate level of variability accounted for by each prediction algorithm, the Hosmer-Lemeshow statistic for calibration, and the sensitivity and specificity of each algorithm. RESULTS: The Stroke Riskometer(TM) performed well against the FSRS five-year AUROC for both males (FSRS = 75.0% (95% CI 72.3%-77.6%), Stroke Riskometer(TM) = 74.0(95% CI 71.3%-76.7%) and females [FSRS = 70.3% (95% CI 67.9%-72.8%, Stroke Riskometer(TM) = 71.5% (95% CI 69.0%-73.9%)], and better than QStroke [males - 59.7% (95% CI 57.3%-62.0%) and comparable to females = 71.1% (95% CI 69.0%-73.1%)]. Discriminative ability of all algorithms was low (C-statistic ranging from 0.51-0.56, D-statistic ranging from 0.01-0.12). Hosmer-Lemeshow illustrated that all of the predicted risk scores were not well calibrated with the observed event data (P < 0.006). CONCLUSIONS: The Stroke Riskometer(TM) is comparable in performance for stroke prediction with FSRS and QStroke. All three algorithms performed equally poorly in predicting stroke events. The Stroke Riskometer(TM) will be continually developed and validated to address the need to improve the current stroke risk scoring systems to more accurately predict stroke, particularly by identifying robust ethnic/race ethnicity group and country specific risk factors.

BACKGROUND: The burden of stroke in low- and middle-income countries (LMICs) is large and increasing, challenging the already stretched health-care services. AIMS AND OBJECTIVES: To determine the quality of existing stroke-care services in LMICs and to highlight indigenous, inexpensive, evidence-based implementable strategies being used in stroke-care. METHODS: A detailed literature search was undertaken using PubMed and Google scholar from January 1966 to October 2015 using a range of search terms. Of 921 publications, 373 papers were shortlisted and 31 articles on existing stroke-services were included. RESULTS: We identified efficient models of ambulance transport and pre-notification. Stroke Units (SU) are available in some countries, but are relatively sparse and mostly provided by the private sector. Very few patients were thrombolysed; this could be increased with telemedicine and governmental subsidies. Adherence to secondary preventive drugs is affected by limited availability and affordability, emphasizing the importance of primary prevention. Training of paramedics, care-givers and nurses in post-stroke care is feasible. CONCLUSION: In this systematic review, we found several reports on evidence-based implementable stroke services in LMICs. Some strategies are economic, feasible and reproducible but remain untested. Data on their outcomes and sustainability is limited. Further research on implementation of locally and regionally adapted stroke-services and cost-effective secondary prevention programs should be a priority.