Effect of lipid-lowering therapy on carotid plaque burden in older adults.

BACKGROUND: Little is known about the benefits of lipid-lowering medications in those age ≥ 75 years. We assessed the effect of lipid-lowering medications on progression to severe atherosclerosis in patients age > 75. METHODS: Data was retrospectively obtained from the Stroke Prevention & Atherosclerosis Research Centre, Canada. Atherosclerosis burden was measured as carotid total plaque area (TPA), a powerful predictor of cardiovascular risk. Survival time free of severe atherosclerosis (SFSA) was defined as the period when TPA remained <1.19 cm2. Kaplan-Meier, multiple Cox proportional hazard and hierarchical mixed-effect models were used to determine the effects of lipid-lowering medications on progression to severe atherosclerosis. RESULTS: In total 1404 cases (mean age 81 ± 4 years; women 52%) were included. Those taking lipid-lowering medications were more likely to have a history of diabetes and a higher burden of atherosclerosis at baseline. In Kaplan-Meier analysis, the SFSA was significantly longer in those receiving lipid-lowering therapy. In multivariable-adjusted analyses, those not receiving lipid lowering therapy (irrespective of their vascular disease at baseline) were more likely to have TPA > 1.19 cm2 (hazard ratio (HR) = 1.37, 95% confidence interval (CI): 1.09,0.71). Similar findings were observed in mixed effects models when plaque progression was defined as any change >0.05 cm2 per year (odds ratio (OR):2.17, 95% CI:1.38,3.57). CONCLUSION: Lipid-lowering therapy is effective in controlling the burden of atherosclerosis among older adults with and without vascular disease. The measurement of plaque burden can guide selection and follow-up of those who may benefit from treatment.

Ultrasound carotid plaque segmentation via image reconstruction-based self-supervised learning with limited training labels.

Carotid total plaque area (TPA) is an important contributing measurement to the evaluation of stroke risk. Deep learning provides an efficient method for ultrasound carotid plaque segmentation and TPA quantification. However, high performance of deep learning requires datasets with many labeled images for training, which is very labor-intensive. Thus, we propose an image reconstruction-based self-supervised learning algorithm (IR-SSL) for carotid plaque segmentation when few labeled images are available. IR-SSL consists of pre-trained and downstream segmentation tasks. The pre-trained task learns region-wise representations with local consistency by reconstructing plaque images from randomly partitioned and disordered images. The pre-trained model is then transferred to the segmentation network as the initial parameters in the downstream task. IR-SSL was implemented with two networks, UNet++ and U-Net, and evaluated on two independent datasets of 510 carotid ultrasound images from 144 subjects at SPARC (London, Canada) and 638 images from 479 subjects at Zhongnan hospital (Wuhan, China). Compared to the baseline networks, IR-SSL improved the segmentation performance when trained on few labeled images (n = 10, 30, 50 and 100 subjects). For 44 SPARC subjects, IR-SSL yielded Dice-similarity-coefficients (DSC) of 80.14-88.84%, and algorithm TPAs were strongly correlated (r=0.962-0.993, p < 0.001) with manual results. The models trained on the SPARC images but applied to the Zhongnan dataset without retraining achieved DSCs of 80.61-88.18% and strong correlation with manual segmentation (r=0.852-0.978, p < 0.001). These results suggest that IR-SSL could improve deep learning when trained on small labeled datasets, making it useful for monitoring carotid plaque progression/regression in clinical use and trials.

Carotid Vessel-Wall-Volume Ultrasound Measurement via a UNet++ Ensemble Algorithm Trained on Small Data Sets.

Vessel wall volume (VWV) is a 3-D ultrasound measurement for the assessment of therapy in patients with carotid atherosclerosis. Deep learning can be used to segment the media-adventitia boundary (MAB) and lumen-intima boundary (LIB) and to quantify VWV automatically; however, it typically requires large training data sets with expert manual segmentation, which are difficult to obtain. In this study, a UNet++ ensemble approach was developed for automated VWV measurement, trained on five small data sets (n = 30 participants) and tested on 100 participants with clinically diagnosed coronary artery disease enrolled in a multicenter CAIN trial. The Dice similarity coefficient (DSC), average symmetric surface distance (ASSD), Pearson correlation coefficient (r), Bland-Altman plots and coefficient of variation (CoV) were used to evaluate algorithm segmentation accuracy, agreement and reproducibility. The UNet++ ensemble yielded DSCs of 91.07%-91.56% and 87.53%-89.44% and ASSDs of 0.10-0.11 mm and 0.33-0.39 mm for the MAB and LIB, respectively; the algorithm VWV measurements were correlated (r = 0.763-0.795, p < 0.001) with manual segmentations, and the CoV for VWV was 8.89%. In addition, the UNet++ ensemble trained on 30 participants achieved a performance similar to that of U-Net and Voxel-FCN trained on 150 participants. These results suggest that our approach could provide accurate and reproducible carotid VWV measurements using relatively small training data sets, supporting deep learning applications for monitoring atherosclerosis progression in research and clinical trials.

The impact of socioeconomic status on the burden of atherosclerosis, and the effect of intensive preventive therapy on its progression: A retrospective cohort study.

BACKGROUND AND AIMS: Socioeconomic status (SES) is associated with cardiovascular disease. However, the relationship between SES and atherosclerosis is not well documented. This study aims to explore this relationship. METHODS: This is a retrospective cohort study in London, Ontario Canada. It includes 6,907 subjects from a vascular prevention centre at baseline, with long term follow up from 1989 to 2021 (total ultrasound examinations 27,103). Using carotid ultrasound, the burden of atherosclerosis was measured as total plaque area (TPA). The Ontario Marginalization Index (OMI) was used to identify SES of participants' neighborhoods. We used a Bayesian hierarchical regression and mixed effects model to identify associations between SES, baseline TPA and plaque progression. In 2003, we implemented more intensive therapy of vascular risk factors after 2003 (called "Treating arteries instead of risk treating factors"); therefore, we compared our findings before and after 2003. RESULTS: SES was found to have a significant association with TPA, with lower SES associated with higher TPA (adjusted odds ratio [OR] = 2.22, 95% Credible interval [CrI]: 1.37, 3.66). While we observed a higher rate of plaque progression with lower SES in those treated before 2003 (OR = 1.46, 95% CrI:1.04, 2.06), there was no significant association between plaque progression and SES after implementation more intensive therapy (OR = 0.99, 95% CrI: 0.78, 1.27). CONCLUSIONS: SES has a strong association with atherosclerosis and should be considered an important risk factor in clinical practice and vascular disease research. Intensive preventive therapy can prevent plaque progression irrespective of baseline SES.

Secular trends of ischaemic heart disease, stroke, and dementia in high-income countries from 1990 to 2017: the Global Burden of Disease Study 2017.

BACKGROUND: We assessed secular trends in the burden of ischaemic heart disease (IHD), stroke, and dementia in the Organization for Economic Co-operation and Development (OECD) countries. METHODS: Using the Global Burden of Disease (GBD) Study 2017, we compared sex-specific and age-standardized rates of disability-adjusted life years (DALY); mortality, incidence, and prevalence of IHD and stroke; and dementia per 100,000 people, in the world, OECD countries, and Canada. RESULTS: From 1990 to 2017, the crude incidence number of IHD, stroke, and dementia increased 52%, 76%, and 113%, respectively. Likewise, the prevalence of IHD (75%), stroke (95%), and dementia (119%) increased worldwide. In addition during the study period, the crude global number of deaths of IHD increased 52%, stroke by 41%, and dementia by 146% (9, 6, and 3 million deaths in 2017, respectively). Despite an increase in the crude number of these diseases, the global age-standardized incidence rate of IHD, stroke, and dementia decreased by -27%, - 11%, and - 5%, respectively. Moreover, there was a decline in their age-standardized DALY rates (- 1.17%, - 1.32%, and - 0.23% per year, respectively) and death rates (- 1.29%, - 1.46%, and - 0.17% per year, respectively), with sharper downward trends in Canada and OECD countries. Almost all trends flattened during the last decade. CONCLUSIONS: From 1990 to 2017, the age-standardized burden of IHD, stroke, and dementia decreased, more prominently in OECD countries than the world. However, their rising crude numbers mainly due to population growth and ageing require urgent identification of reversible risk and protective factors.

Case-Fatality and Functional Outcome after Subarachnoid Hemorrhage (SAH) in INternational STRoke oUtComes sTudy (INSTRUCT).

BACKGROUND: There are few large population-based studies of outcomes after subarachnoid hemorrhage (SAH) than other stroke types. METHODS: We pooled data from 13 population-based stroke incidence studies (10 studies from the INternational STRroke oUtComes sTudy (INSTRUCT) and 3 new studies; N=657). Primary outcomes were case-fatality and functional outcome (modified Rankin scale score 3-5 [poor] vs. 0-2 [good]). Harmonized patient-level factors included age, sex, health behaviours (e.g. current smoking at baseline), comorbidities (e.g.history of hypertension), baseline stroke severity (e.g. NIHSS >7) and year of stroke. We estimated predictors of case-fatality and functional outcome using Poisson regression and generalized estimating equations using log-binomial models respectively at multiple timepoints. RESULTS: Case-fatality rate was 33% at 1 month, 43% at 1 year, and 47% at 5 years. Poor functional outcome was present in 27% of survivors at 1 month and 15% at 1 year. In multivariable analysis, predictors of death at 1-month were age (per decade increase MRR 1.14 [1.07-1.22]) and SAH severity (MRR 1.87 [1.50-2.33]); at 1 year were age (MRR 1.53 [1.34-1.56]), current smoking (MRR 1.82 [1.20-2.72]) and SAH severity (MRR 3.00 [2.06-4.33]) and; at 5 years were age (MRR 1.63 [1.45-1.84]), current smoking (MRR 2.29 [1.54-3.46]) and severity of SAH (MRR 2.10 [1.44-3.05]). Predictors of poor functional outcome at 1 month were age (per decade increase RR 1.32 [1.11-1.56]) and SAH severity (RR 1.85 [1.06-3.23]), and SAH severity (RR 7.09 [3.17-15.85]) at 1 year. CONCLUSION: Although age is a non-modifiable risk factor for poor outcomes after SAH, however, severity of SAH and smoking are potential targets to improve the outcomes.

Post-stroke Cognitive Impairment and Malnutrition in the Elderly (PCIME): study design and protocol.

PURPOSE: Stroke and dementia are burdensome non communicable diseases but preventable. A joint prevention of stroke and dementia requires a comprehensive approach by controlling common risk factors. Little is known about the role of malnutrition in post-stroke cognitive impairments. The main aim of this study is to evaluate the presence of cognitive impairment after stroke and its association with malnutrition. METHODS: Post-stroke Cognitive Impairment and Malnutrition in the Elderly (PCIME) is a prospective hospital-based cohort study, started on January 2nd, 2018 at Ziaeian Hospital, a referral center for elderly, Tehran, Iran, Middle East. We have used a battery of neuropsychiatric tests, including the Montreal Cognitive Assessment and Mini-Mental State Examination. We have assessed malnutrition using the Mini Nutritional Assessment and Geriatric Nutritional Risk Index. CONCLUSION: PCIME will enable us to assess the role of malnutrition in development of post stroke dementia. This study will help clinicians and researchers develop preventive measures to reduce the burden of stroke and dementia.

Association of lewy bodies, hippocampal sclerosis and amyloid angiopathy with dementia in community-dwelling elderly: A systematic review and meta-analysis.

INTRODUCTION: We measured the proportion of Lewy body pathology (LB), hippocampal sclerosis (HS), and cerebral amyloid angiopathy (CAA) among community-dwelling people with and without dementia. METHODS: We searched for community-based cohorts with postmortem brain autopsy until 1 January 2020. We calculated the summary risk difference and 95% confidence interval (95% CI) using a random-effects model in R. RESULTS: We found 12 articles, comprising 2197 demented and 2104 non-demented participants. LB, HS, CAA were prevalent lesions among community-dwelling elderly (15%, 10%, and 24%, respectively). These significantly increased the risk of dementia (LB: risk difference 38%, 95% CI 20-56%, HS: 34%, 24-44%, CAA: 19%, 3-34%). 20% of cases with neocortical LB, 17% with bilateral HS, and 42% with moderate/severe CAA pathology remained non-demented by death. DISCUSSION: LB or HS or CAA are common neuropathologies among community-dwelling elderly. Although these lesions independently are associated with dementia, many remain non-demented, by death.

Deep Learning-Based Carotid Plaque Segmentation from B-Mode Ultrasound Images.

Carotid ultrasound measurement of total plaque area (TPA) provides a method for quantifying carotid plaque burden and monitoring changes in carotid atherosclerosis in response to medical treatment. Plaque boundary segmentation is required to generate the TPA measurement; however, training of observers and manual delineation are time consuming. Thus, our objective was to develop an automated plaque segmentation method to generate TPA from longitudinal carotid ultrasound images. In this study, a deep learning-based method, modified U-Net, was used to train the segmentation model and generate TPA measurement. A total of 510 plaques from 144 patients were used in our study, where the Monte Carlo cross-validation was used by randomly splitting the data set into 2/3 and 1/3 for training and testing. Two observers were trained to manually delineate the 510 plaques separately, which were used as the ground-truth references. Two U-Net models (M1 and M2) were trained using the two different ground-truth data sets from the two observers to evaluate the accuracy, variability and sensitivity on the ground-truth data sets used for training our method. The results of the algorithm segmentations of the two models yielded strong agreement with the two manual segmentations with the Pearson correlation coefficient r = 0.989 (p < 0.0001) and r = 0.987 (p < 0.0001). Comparison of the U-Net and manual segmentations resulted in mean TPA differences of 0.05 ± 7.13 mm2 (95% confidence interval: 14.02-13.02 mm2) and 0.8 ± 8.7 mm2 (17.85-16.25 mm2) for the two models, which are small compared with the TPA range in our data set from 4.7 to 312.8 mm2. Furthermore, the mean time to segment a plaque was only 8.3 ± 3.1 ms. The presented deep learning-based method described has sufficient accuracy with a short computation time and exhibits high agreement between the algorithm and manual TPA measurements, suggesting that the method could be used to measure TPA and to monitor the progression and regression of carotid atherosclerosis.

Transcranial Doppler to evaluate postreperfusion therapy following acute ischemic stroke: A literature review.

Cerebral vessel recanalization therapy, either intravenous thrombolysis or mechanical thrombectomy, is the main treatment that can significantly improve clinical outcomes after acute ischemic stroke. The degree of recanalization and cerebral reperfusion of the ischemic penumbra are dependent on cerebral hemodynamics. Currently, the main imaging modalities to assess reperfusion are MRI and CT perfusion. However, these imaging techniques cannot predict reperfusion-associated complications and are not readily available in many centers. It is also not feasible to repeat them frequently for sequential assessments, which is important because of the changing nature of cerebral hemodynamics following stroke. Transcranial Doppler sonography (TCD) is a valid, safe, and inexpensive technique that can assess recanalized vessels and reperfused tissue in real-time at the bedside. Post thrombectomy reocclusion, hyperperfusion syndrome, distal embolization, and remote infarction result in poor outcomes after mechanical or intravenous reperfusion therapy. Managing blood pressure following these endovascular treatments can also be a dilemma. TCD has an important role, with major clinical implications, in evaluating cerebral hemodynamics and collateral vessel status, guiding clinicians in making individualized decisions based on cerebral blood flow during acute stroke care. This review summarizes the most relevant literature on the role of TCD in evaluating patients after reperfusion therapy. We also discuss the importance of performing TCD in the first few hours following thrombolytic therapy in identifying hyperperfusion syndrome and embolic signals, predicting recurrent stroke, and detecting reocclusions, all of which may help improve patient prognosis. We recommend TCD during the hyperacute phase of stroke in comprehensive stroke centers.

Stroke Prevention: Little-Known and Neglected Aspects.

Combining available therapies has the potential to reduce the risk of stroke by 80% or more. A comprehensive review of all aspects of stroke prevention would be very lengthy; in this narrative review, we focus on some aspects of stroke prevention that are little-known and/or neglected. These include the following: (1) implementation of a Mediterranean diet; (2) B vitamins to lower homocysteine; (3) coordinated approaches to smoking cessation; (4) intensive lipid-lowering therapy; (5) lipid lowering in the elderly; (6) physiologically individualized therapy for hypertension based on renin/aldosterone phenotyping; (7) avoiding excessive blood pressure reduction in patients with stiff arteries; (8) treatment of insulin resistance with pioglitazone in stroke patients with prediabetes and diabetes; (9) impaired activation of clopidogrel in patients with variants of CYP2C19; (10) aspirin pseudoresistance due to enteric coating; (11) rationale for anticoagulation in patients with embolic stroke of unknown source; (12) pharmacologic properties of direct-acting oral anticoagulants that should be considered when choosing among them; (13) the identification of which patients with asymptomatic carotid stenosis are at a high enough risk to benefit from carotid endarterectomy or stenting; and (14) the importance of age in choosing between endarterectomy and stenting. Stroke prevention could be improved by better recognition of these issues and by implementation of the principles derived from them.

Triglyceride/High-Density Lipoprotein Cholesterol Ratio: A Clue to Metabolic Syndrome, Insulin Resistance, and Severe Atherosclerosis.

High serum levels of triglycerides (Tg) and low levels of high-density lipoprotein cholesterol (HDL-C) are characteristic of the Metabolic Syndrome (MetS). We assessed the ratio of Tg to HDL-C as a way to identify MetS and insulin resistance. We also evaluated its association with severity of carotid atherosclerosis. Data were analyzed from three cohorts totaling 13,908 participants. MetS was defined according to the International Diabetes Federation criteria. Optimal cut-off for Tg/HDL-C ratio was obtained using Youden's index in receiver-operating characteristic (ROC) curve analyses. The risk of MetS and IR in those with a Tg/HDL-C ratio above the optimum cutoff was evaluated by logistic regression analysis. A Tg/HDL-C ratio above the optimal cutoff level significantly increased the odds ratio for MetS in the three cohorts (OR 6.00, 4.04, and 3.50, least in the healthy population), identified insulin resistance defined by the homeostatic model of insulin resistance (HOMA-IR) (p < 0.0001), and was strongly associated with atherosclerosis severity (p = 0.0001). Tg/HDL-C ratio identifies persons with MetS, insulin resistance, and severe atherosclerosis. It should be used more widely to identify patients at high risk. This is clinically important because insulin resistance is treatable.

Cerebral blood flow changes during aging process and in cognitive disorders: A review.

We aimed to summarize the available evidence on cerebral blood flow (CBF) changes in normal aging and common cognitive disorders. We searched PubMed for studies on CBF changes in normal aging and cognitive disorders up to 1 January 2019. We summarized the milestones in the history of CBF assessment and reviewed the current evidence on the association between CBF and cognitive changes in normal aging, vascular cognitive impairment (VCI) and Alzheimer's disease (AD). There is promising evidence regarding the utility of CBF studies in cognition research. Age-related CBF changes could be related to a progressive neuronal loss or diminished activity and synaptic density of neurons in the brain. While a similar cause or outcome theory applies to VCI and AD, it is possible that CBF reduction might precede cognitive decline. Despite the diversity of CBF research findings, its measurement could help early detection of cognitive disorders and also understanding their underlying etiology.

Reliability and Validity of Simple Stroke Food Frequency Questionnaire (SS-FFQ) for Nutrition Monitoring in Patients with Acute Ischemic Stroke.

Objectives Standard dietary questionnaires may have some limitations in cases with stroke, particularly in those suffering from language and communication difficulties. The present study aimed to develop a dietary questionnaire appropriate for patients with acute ischemic stroke (AIS). Materials and Methods Major food groups ( n = 19) were first identified using the dietary questionnaire of the INTERHEART study. Using the Food Frequency Questionnaire (FFQ), an expert dietitian and a vascular neurologist then selected a total number of 68 corresponding food items from 168 available FFQ items. In the next phase, a panel of expert dietitians ( n = 10) assessed the face validity and the content validity of these 68 items and approved a total number of 62 items for the final questionnaire, namely, the Simple Stroke FFQ (SS-FFQ). Employing test-retest method, the intraclass correlation (ICC) of the SS-FFQ was subsequently calculated in 30 randomly selected cases affected with AIS. Ultimately, principal component analysis (PCA) was utilized for 153 cases with AIS to assess the construct validity of the questionnaire concerned. The SPSS Statistics software (version 18: SPSS Inc., Chicago, Illinois, United States) as well as descriptive tests including mean and percentage were additionally used to account for the baseline characteristics of the study participants. Results The results revealed that the reliability of the newly developed form of the SS-FFQ was perfect (ICC = 0.86). Dietary conditions were further assessed administering the SS-FFQ on 153 cases of AIS with the mean age of 63.76 ± 15.93 years. The PCA results also showed that 15 extracted items of the given questionnaire could explain 73.10% of total item variance. Conclusions It was concluded that the SS-FFQ was a valid and reliable questionnaire to assess nutrient intakes among patients with AIS.

Deep Learning-Based Measurement of Total Plaque Area in B-Mode Ultrasound Images.

Measurement of total-plaque-area (TPA) is important for determining long term risk for stroke and monitoring carotid plaque progression. Since delineation of carotid plaques is required, a deep learning method can provide automatic plaque segmentations and TPA measurements; however, it requires large datasets and manual annotations for training with unknown performance on new datasets. A UNet++ ensemble algorithm was proposed to segment plaques from 2D carotid ultrasound images, trained on three small datasets (n = 33, 33, 34 subjects) and tested on 44 subjects from the SPARC dataset (n = 144, London, Canada). The ensemble was also trained on the entire SPARC dataset and tested with a different dataset (n = 497, Zhongnan Hospital, China). Algorithm and manual segmentations were compared using Dice-similarity-coefficient (DSC), and TPAs were compared using the difference ( ∆TPA), Pearson correlation coefficient (r) and Bland-Altman analyses. Segmentation variability was determined using the intra-class correlation coefficient (ICC) and coefficient-of-variation (CoV). For 44 SPARC subjects, algorithm DSC was 83.3-85.7%, and algorithm TPAs were strongly correlated (r = 0.985-0.988; p < 0.001) with manual results with marginal biases (0.73-6.75) mm 2 using the three training datasets. Algorithm ICC for TPAs (ICC = 0.996) was similar to intra- and inter-observer manual results (ICC = 0.977, 0.995). Algorithm CoV = 6.98% for plaque areas was smaller than the inter-observer manual CoV (7.54%). For the Zhongnan dataset, DSC was 88.6% algorithm and manual TPAs were strongly correlated (r = 0.972, p < 0.001) with ∆TPA = -0.44 ±4.05 mm 2 and ICC = 0.985. The proposed algorithm trained on small datasets and segmented a different dataset without retraining with accuracy and precision that may be useful clinically and for research.

Peripheral Nervous System Manifestations Associated with COVID-19.

PURPOSE OF REVIEW: The present review discusses the peripheral nervous system (PNS) manifestations associated with coronavirus disease 2019 (COVID-19). RECENT FINDINGS: Nerve pain and skeletal muscle injury, Guillain-Barré syndrome, cranial polyneuritis, neuromuscular junction disorders, neuro-ophthalmological disorders, neurosensory hearing loss, and dysautonomia have been reported as PNS manifestations in patients with COVID-19. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes COVID-19. COVID-19 has shown syndromic complexity. Not only does SARS-CoV-2 affect the central nervous system but also it involves the PNS. The PNS involvement may be due to dysregulation of the immune system attributable to COVID-19. Here we review the broad spectrum of PNS involvement of COVID-19.

Trends in ischemic stroke outcomes in a rural population in the United States.

INTRODUCTION: The stroke mortality rate has gradually declined due to improved interventions and controlled risk factors. We investigated the associated factors and trends in recurrence and all-cause mortality in ischemic stroke patients from a rural population in the United States between 2004 and 2018. METHODS: This was a retrospective cohort study based on electronic health records (EHR) data. A comprehensive stroke database called "Geisinger NeuroScience Ischemic Stroke (GNSIS)" was built for this study. Clinical data were extracted from multiple sources, including EHR and quality data. RESULTS: The cohort included in the study comprised of 8561 consecutive ischemic stroke patients (mean age: 70.1 ± 13.9 years, men: 51.6%, 95.1% Caucasian). Hypertension was the most prevalent risk factor (75.2%). The one-year recurrence and all-cause mortality rates were 6.3% and 16.1%, respectively. Although the one-year stroke recurrence increased during the study period, the one-year stroke mortality rate decreased significantly. Age > 65 years, atrial fibrillation or flutter, heart failure, and prior ischemic stroke were independently associated with one-year all-cause mortality in stratified Cox proportional hazards model. In the Cause-specific hazard model, diabetes, chronic kidney disease and age < 65 years were found to be associated with one-year ischemic stroke recurrence. CONCLUSION: Although all-cause mortality after stroke has decreased, stroke recurrence has significantly increased in stroke patients from rural population between 2004 and 2018. Older age, atrial fibrillation or flutter, heart failure, and prior ischemic stroke were independently associated with one-year all-cause mortality while diabetes, chronic kidney disease and age less than 65 years were predictors of ischemic stroke recurrence.

Measurement of carotid plaque burden: A tool for predicting and preventing dementia?

Introduction: Carotid plaque burden is a strong predictor of stroke risk, and preventing stroke reduces the risk of dementia. Treating carotid plaque burden markedly reduces the risk of stroke. Methods: Among patients age 65-80 years attending a stroke prevention clinic, we identified those with a carotid plaque burden in the top 20% of Total Plaque Area (High TPA) and the bottom 20% (Low TPA) and performed cognitive tests: The Montreal Cognitive Assessment test (MoCA), the WAIS-III Digit Symbol-Coding Test (DSST) and Trail-Making Test (TMT) part A and B. Results: There were 31 patients recruited; 11 Low TPA (5 men) and 20 High TPA (17 men), p = 0.04. TPA was 35 ± 25 mm2 in the Low TPA vs.392 ± 169 mm2 in the High TPA group (0.0001). Patients with a high plaque burden had significantly worse performance on all the cognitive tests, all p< 0.05. Discussion: A high carotid plaque burden identifies patients at risk of cognitive impairment. Because carotid plaque burden is treatable, and treating it markedly reduces the risk of stroke, we suggest that measurement of plaque burden is a useful tool for both prediction of cognitive impairment, and prevention of dementia.