Ultrasound-based stroke/cardiovascular risk stratification using Framingham Risk Score and ASCVD Risk Score based on “Integrated Vascular Age” instead of “Chronological Age”: A multi-ethnic study of Asian Indian, Caucasian, and Japanese cohorts

Background: Vascular age (VA) has recently emerged for CVD risk assessment and can either be computed using conventional risk factors (CRF) or by using carotid intima-media thickness (cIMT) derived from carotid ultrasound (CUS). This study investigates a novel method of integrating both CRF and cIMT for estimating VA [so-called integrated VA (IVA)]. Further, the study analyzes and compares CVD/stroke risk using the Framingham Risk Score (FRS)-based risk calculator when adapting IVA against VA. Methods: The system follows a four-step process: (I) VA using cIMT based using linear-regression (LR) model and its coefficients; (II) VA prediction using ten CRF using a multivariate linear regression (MLR)based model with gender adjustment; (III) coefficients from the LR-based model and MLR-based model are combined using a linear model to predict the final IVA; (IV) the final step consists of FRS-based risk stratification with IVA as inputs and benchmarked against FRS using conventional method of CA. Area-under-the-curve (AUC) is computed using IVA and benchmarked against CA while taking the response variable as a standardized combination of cIMT and glycated hemoglobin. Results: The study recruited 648 patients, 202 were Japanese, 314 were Asian Indian, and 132 were Caucasians. Both left and right common carotid arteries (CCA) of all the population were scanned, thus a total of 1,287 ultrasound scans. The 10-year FRS using IVA reported higher AUC (AUC =0.78) compared with 10-year FRS using CA (AUC =0.66) by ~18%

Multimodality carotid plaque tissue characterization and classification in the artificial intelligence paradigm: a narrative review for stroke application

Cardiovascular disease (CVD) is one of the leading causes of morbidity and mortality in the United States of America and globally. Carotid arterial plaque, a cause and also a marker of such CVD, can be detected by various non-invasive imaging modalities such as magnetic resonance imaging (MRI), computer tomography (CT), and ultrasound (US). Characterization and classification of carotid plaque-type in these imaging modalities, especially into symptomatic and asymptomatic plaque, helps in the planning of carotid endarterectomy or stenting. It can be challenging to characterize plaque components due to (I) partial volume effect in magnetic resonance imaging (MRI) or (II) varying Hausdorff values in plaque regions in CT, and (III) attenuation of echoes reflected by the plaque during US causing acoustic shadowing. Artificial intelligence (AI) methods have become an indispensable part of healthcare and their applications to the non-invasive imaging technologies such as MRI, CT, and the US. In this narrative review, three main types of AI models (machine learning, deep learning, and transfer learning) are analyzed when applied to MRI, CT, and the US. A link between carotid plaque characteristics and the risk of coronary artery disease is presented. With regard to characterization, we review tools and techniques that use AI models to distinguish carotid plaque types based on signal processing and feature strengths. We conclude that AI-based solutions offer an accurate and robust path for tissue characterization and classification for carotid artery plaque imaging in all three imaging modalities. Due to cost, user-friendliness, and clinical effectiveness, AI in the US has dominated the most

Cardiovascular/Stroke Risk Assessment in Patients with Erectile Dysfunction—A Role of Carotid Wall Arterial Imaging and Plaque Tissue Characterization Using Artificial Intelligence Paradigm: A Narrative Review

Purpose: The role of erectile dysfunction (ED) has recently shown an association with the risk of stroke and coronary heart disease (CHD) via the atherosclerotic pathway. Cardiovascular disease (CVD)/stroke risk has been widely understood with the help of carotid artery disease (CTAD), a surrogate biomarker for CHD. The proposed study emphasizes artificial intelligence-based frameworks such as machine learning (ML) and deep learning (DL) that can accurately predict the severity of CVD/stroke risk using carotid wall arterial imaging in ED patients. Methods: Using the PRISMA model, 231 of the best studies were selected. The proposed study mainly consists of two components: (i) the pathophysiology of ED and its link with coronary artery disease (COAD) and CHD in the ED framework and (ii) the ultrasonic-image morphological changes in the carotid arterial walls by quantifying the wall parameters and the characterization of the wall tissue by adapting the ML/DL-based methods, both for the prediction of the severity of CVD risk. The proposed study analyzes the hypothesis that ML/DL can lead to an accurate and early diagnosis of the CVD/stroke risk in ED patients. Our finding suggests that the routine ED patient practice can be amended for ML/DL-based CVD/stroke risk assessment using carotid wall arterial imaging leading to fast, reliable, and accurate CVD/stroke risk stratification. Summary: We conclude that ML and DL methods are very powerful tools for the characterization of CVD/stroke in patients with varying ED conditions. We anticipate a rapid growth of these tools for early and better CVD/stroke risk management in ED patients

Fatigue in primary Sjögren's syndrome (pSS) is associated with lower levels of proinflammatory cytokines: a validation study

Primary Sjögren’s syndrome (pSS) is a chronic autoimmune rheumatic disease with symptoms including dryness, fatigue, and pain. The previous work by our group has suggested that certain proinflammatory cytokines are inversely related to patient-reported levels of fatigue. To date, these findings have not been validated. This study aims to validate this observation. Blood levels of seven cytokines were measured in 120 patients with pSS from the United Kingdom Primary Sjögren’s Syndrome Registry and 30 age-matched healthy non-fatigued controls. Patient-reported scores for fatigue were classified according to severity and compared to cytokine levels using analysis of variance. The differences between cytokines in cases and controls were evaluated using Wilcoxon test. A logistic regression model was used to determine the most important identifiers of fatigue. Five cytokines, interferon-γ-induced protein-10 (IP-10), tumour necrosis factor-α (TNFα), interferon-α (IFNα), interferon-γ (IFN-γ), and lymphotoxin-α (LT-α) were significantly higher in patients with pSS (n = 120) compared to non-fatigued controls (n = 30). Levels of two proinflammatory cytokines, TNF-α (p = 0.021) and LT-α (p = 0.043), were inversely related to patient-reported levels of fatigue. Cytokine levels, disease-specific and clinical parameters as well as pain, anxiety, and depression were used as predictors in our validation model. The model correctly identifies fatigue levels with 85% accuracy. Consistent with the original study, pain, depression, and proinflammatory cytokines appear to be the most powerful predictors of fatigue in pSS. TNF-α and LT-α have an inverse relationship with fatigue severity in pSS challenging the notion that proinflammatory cytokines directly mediate fatigue in chronic immunological conditions

Burden of Risk Alleles for Hypertension Increases Risk of Intracerebral Hemorrhage

Background and Purpose-Genetic variation influences risk of intracerebral hemorrhage (ICH). Hypertension (HTN) is a potent risk factor for ICH and several common genetic variants (single nucleotide polymorphisms [SNPs]) associated with blood pressure levels have been identified. We sought to determine whether the cumulative burden of blood pressure-related SNPs is associated with risk of ICH and pre-ICH diagnosis of HTN. Methods-We conducted a prospective multicenter case-control study in 2272 subjects of European ancestry (1025 cases and 1247 control subjects). Thirty-nine SNPs reported to be associated with blood pressure levels were identified from the National Human Genome Research Institute genomewide association study catalog. Single-SNP association analyses were performed for the outcomes ICH and pre-ICH HTN. Subsequently, weighted and unweighted genetic risk scores were constructed using these SNPs and entered as the independent variable in logistic regression models with ICH and pre-ICH HTN as the dependent variables. Results-No single SNP was associated with either ICH or pre-ICH HTN. The blood pressure-based unweighted genetic risk score was associated with risk of ICH (OR, 1.11; 95% CI, 1.02-1.21; P=0.01) and the subset of ICH in deep regions (OR, 1.18; 95% CI, 1.07-1.30; P=0.001), but not with the subset of lobar ICH. The score was associated with a history of HTN among control subjects (OR, 1.17; 95% CI, 1.04-1.31; P=0.009) and ICH cases (OR, 1.15; 95% CI, 1.01-1.31; P=0.04). Similar results were obtained when using a weighted score. Conclusion-Increasing numbers of high blood pressure-related alleles are associated with increased risk of deep ICH as well as with clinically identified HTN. (Stroke. 2012; 43: 2877-2883.

Characteristics and Outcomes in Patients With COVID-19 and Acute Ischemic Stroke: The Global COVID-19 Stroke Registry

Recent case-series of small size implied a pathophysiological association between coronavirus disease 2019 (COVID-19) and severe large-vessel acute ischemic stroke. Given that severe strokes are typically associated with poor prognosis and can be very efficiently treated with recanalization techniques, confirmation of this putative association is urgently warranted in a large representative patient cohort to alert stroke clinicians, and inform pre- and in-hospital acute stroke patient pathways. We pooled all consecutive patients hospitalized with laboratory-confirmed COVID-19 and acute ischemic stroke in 28 sites from 16 countries. To assess whether stroke severity and outcomes (assessed at discharge or at the latest assessment for those patients still hospitalized) in patients with acute ischemic stroke are different between patients with COVID-19 and non-COVID-19, we performed 1:1 propensity score matching analyses of our COVID-19 patients with non-COVID-19 patients registered in the Acute Stroke Registry and Analysis of Lausanne Registry between 2003 and 2019. Between January 27, 2020, and May 19, 2020, 174 patients (median age 71.2 years; 37.9% females) with COVID-19 and acute ischemic stroke were hospitalized (median of 12 patients per site). The median National Institutes of Health Stroke Scale was 10 (interquartile range [IQR], 4-18). In the 1:1 matched sample of 336 patients with COVID-19 and non-COVID-19, the median National Institutes of Health Stroke Scale was higher in patients with COVID-19 (10 [IQR, 4-18] versus 6 [IQR, 3-14]), P=0.03; (odds ratio, 1.69 [95% CI, 1.08-2.65] for higher National Institutes of Health Stroke Scale score). There were 48 (27.6%) deaths, of which 22 were attributed to COVID-19 and 26 to stroke. Among 96 survivors with available information about disability status, 49 (51%) had severe disability at discharge. In the propensity score-matched population (n=330), patients with COVID-19 had higher risk for severe disability (median mRS 4 [IQR, 2-6] versus 2 [IQR, 1-4], P<0.001) and death (odds ratio, 4.3 [95% CI, 2.22-8.30]) compared with patients without COVID-19. Our findings suggest that COVID-19 associated ischemic strokes are more severe with worse functional outcome and higher mortality than non-COVID-19 ischemic strokes

Characteristics and Outcomes in Patients With COVID-19 and Acute Ischemic Stroke: The Global COVID-19 Stroke Registry.

Recent case-series of small size implied a pathophysiological association between coronavirus disease 2019 (COVID-19) and severe large-vessel acute ischemic stroke. Given that severe strokes are typically associated with poor prognosis and can be very efficiently treated with recanalization techniques, confirmation of this putative association is urgently warranted in a large representative patient cohort to alert stroke clinicians, and inform pre- and in-hospital acute stroke patient pathways. We pooled all consecutive patients hospitalized with laboratory-confirmed COVID-19 and acute ischemic stroke in 28 sites from 16 countries. To assess whether stroke severity and outcomes (assessed at discharge or at the latest assessment for those patients still hospitalized) in patients with acute ischemic stroke are different between patients with COVID-19 and non-COVID-19, we performed 1:1 propensity score matching analyses of our COVID-19 patients with non-COVID-19 patients registered in the Acute Stroke Registry and Analysis of Lausanne Registry between 2003 and 2019. Between January 27, 2020, and May 19, 2020, 174 patients (median age 71.2 years; 37.9% females) with COVID-19 and acute ischemic stroke were hospitalized (median of 12 patients per site). The median National Institutes of Health Stroke Scale was 10 (interquartile range [IQR], 4-18). In the 1:1 matched sample of 336 patients with COVID-19 and non-COVID-19, the median National Institutes of Health Stroke Scale was higher in patients with COVID-19 (10 [IQR, 4-18] versus 6 [IQR, 3-14]), P=0.03; (odds ratio, 1.69 [95% CI, 1.08-2.65] for higher National Institutes of Health Stroke Scale score). There were 48 (27.6%) deaths, of which 22 were attributed to COVID-19 and 26 to stroke. Among 96 survivors with available information about disability status, 49 (51%) had severe disability at discharge. In the propensity score-matched population (n=330), patients with COVID-19 had higher risk for severe disability (median mRS 4 [IQR, 2-6] versus 2 [IQR, 1-4], P&lt;0.001) and death (odds ratio, 4.3 [95% CI, 2.22-8.30]) compared with patients without COVID-19. Our findings suggest that COVID-19 associated ischemic strokes are more severe with worse functional outcome and higher mortality than non-COVID-19 ischemic strokes

Characteristics and Outcomes of Patients With Cerebral Venous Sinus Thrombosis in SARS-CoV-2 Vaccine–Induced Immune Thrombotic Thrombocytopenia

Importance: Thrombosis with thrombocytopenia syndrome (TTS) has been reported after vaccination with the SARS-CoV-2 vaccines ChAdOx1 nCov-19 (Oxford-AstraZeneca) and Ad26.COV2.S (Janssen/Johnson & Johnson). Objective: To describe the clinical characteristics and outcome of patients with cerebral venous sinus thrombosis (CVST) after SARS-CoV-2 vaccination with and without TTS. Design, setting, and participants: This cohort study used data from an international registry of consecutive patients with CVST within 28 days of SARS-CoV-2 vaccination included between March 29 and June 18, 2021, from 81 hospitals in 19 countries. For reference, data from patients with CVST between 2015 and 2018 were derived from an existing international registry. Clinical characteristics and mortality rate were described for adults with (1) CVST in the setting of SARS-CoV-2 vaccine-induced immune thrombotic thrombocytopenia, (2) CVST after SARS-CoV-2 vaccination not fulling criteria for TTS, and (3) CVST unrelated to SARS-CoV-2 vaccination. Exposures: Patients were classified as having TTS if they had new-onset thrombocytopenia without recent exposure to heparin, in accordance with the Brighton Collaboration interim criteria. Main outcomes and measures: Clinical characteristics and mortality rate. Results: Of 116 patients with postvaccination CVST, 78 (67.2%) had TTS, of whom 76 had been vaccinated with ChAdOx1 nCov-19; 38 (32.8%) had no indication of TTS. The control group included 207 patients with CVST before the COVID-19 pandemic. A total of 63 of 78 (81%), 30 of 38 (79%), and 145 of 207 (70.0%) patients, respectively, were female, and the mean (SD) age was 45 (14), 55 (20), and 42 (16) years, respectively. Concomitant thromboembolism occurred in 25 of 70 patients (36%) in the TTS group, 2 of 35 (6%) in the no TTS group, and 10 of 206 (4.9%) in the control group, and in-hospital mortality rates were 47% (36 of 76; 95% CI, 37-58), 5% (2 of 37; 95% CI, 1-18), and 3.9% (8 of 207; 95% CI, 2.0-7.4), respectively. The mortality rate was 61% (14 of 23) among patients in the TTS group diagnosed before the condition garnered attention in the scientific community and 42% (22 of 53) among patients diagnosed later. Conclusions and relevance: In this cohort study of patients with CVST, a distinct clinical profile and high mortality rate was observed in patients meeting criteria for TTS after SARS-CoV-2 vaccination.info:eu-repo/semantics/publishedVersio

Intracranial Aneurysm Classifier Using Phenotypic Factors: An International Pooled Analysis

Intracranial aneurysms (IAs) are usually asymptomatic with a low risk of rupture, but consequences of aneurysmal subarachnoid hemorrhage (aSAH) are severe. Identifying IAs at risk of rupture has important clinical and socio-economic consequences. The goal of this study was to assess the effect of patient and IA characteristics on the likelihood of IA being diagnosed incidentally versus ruptured. Patients were recruited at 21 international centers. Seven phenotypic patient characteristics and three IA characteristics were recorded. The analyzed cohort included 7992 patients. Multivariate analysis demonstrated that: (1) IA location is the strongest factor associated with IA rupture status at diagnosis; (2) Risk factor awareness (hypertension, smoking) increases the likelihood of being diagnosed with unruptured IA; (3) Patients with ruptured IAs in high-risk locations tend to be older, and their IAs are smaller; (4) Smokers with ruptured IAs tend to be younger, and their IAs are larger; (5) Female patients with ruptured IAs tend to be older, and their IAs are smaller; (6) IA size and age at rupture correlate. The assessment of associations regarding patient and IA characteristics with IA rupture allows us to refine IA disease models and provide data to develop risk instruments for clinicians to support personalized decision-making

Genetic Variants in CETP Increase Risk of Intracerebral Hemorrhage

OBJECTIVE: In observational epidemiologic studies, higher plasma high-density lipoprotein cholesterol (HDL-C) has been associated with increased risk of intracerebral hemorrhage (ICH). DNA sequence variants that decrease cholesteryl ester transfer protein (CETP) gene activity increase plasma HDL-C; as such, medicines that inhibit CETP and raise HDL-C are in clinical development. Here, we test the hypothesis that CETP DNA sequence variants associated with higher HDL-C also increase risk for ICH.METHODS: We performed 2 candidate-gene analyses of CETP. First, we tested individual CETP variants in a discovery cohort of 1,149 ICH cases and 1,238 controls from 3 studies, followed by replication in 1,625 cases and 1,845 controls from 5 studies. Second, we constructed a genetic risk score comprised of 7 independent variants at the CETP locus and tested this score for association with HDL-C as well as ICH risk.RESULTS: Twelve variants within CETP demonstrated nominal association with ICH, with the strongest association at the rs173539 locus (odds ratio [OR] = 1.25, standard error [SE] = 0.06, p = 6.0 × 10(-4) ) with no heterogeneity across studies (I(2) = 0%). This association was replicated in patients of European ancestry (p = 0.03). A genetic score of CETP variants found to increase HDL-C by ∼2.85mg/dl in the Global Lipids Genetics Consortium was strongly associated with ICH risk (OR = 1.86, SE = 0.13, p = 1.39 × 10(-6) ).INTERPRETATION: Genetic variants in CETP associated with increased HDL-C raise the risk of ICH. Given ongoing therapeutic development in CETP inhibition and other HDL-raising strategies, further exploration of potential adverse cerebrovascular outcomes may be warranted. Ann Neurol 2016;80:730-740