Stratifying Stroke Risk in Atrial Fibrillation: Beyond Clinical Risk Scores.
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ABSTRACT:
SUBMITTER: Yaghi S
PROVIDER: S-EPMC5679200 | biostudies-literature | 2017 Oct
REPOSITORIES: biostudies-literature
Publications
Stratifying Stroke Risk in Atrial Fibrillation: Beyond Clinical Risk Scores.
Stroke 20170915 10
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Project description:BackgroundPolygenic scores incorporating varying numbers of single nucleotide polymorphisms (SNPs) have been demonstrated to exert a prominent role in atrial fibrillation (AF). We sought to compare the relative discriminatory capacities of 2 previously validated polygenic scores in "lone" AF.MethodsA total of 186 lone AF cases of European ancestry underwent SNP genotyping. A genome-wide polygenic score (GPS) and polygenic risk score (PRS) involving 6,730,541 and 1168 SNPs, respectively, were calculated for 186 cases and 423 controls of European ancestry from the 1000 Genomes (1KG) Project. The distribution of the polygenic scores was compared between the cases and controls and their discriminatory capacities were evaluated using receiver operating characteristic (ROC) curves.ResultsA total of 34.4% of patients with lone AF had GPS scores greater than the top 10th percentile of 1KG controls, corresponding to a 4.64-fold increased odds (95% confidence interval [CI], 2.99-7.18; P < 0.001) for AF. A PRS score in the top 10th percentile of 1KG controls was observed in 26.3% of cases, which equated to a 3.16-fold increased odds (95% CI, 2.01-4.98; P < 0.001) for AF. Comparison of C-statistics from ROC curves indicated improved discriminatory capacity of the GPS (0.76) relative to the PRS (0.70) (P = 0.002).ConclusionsOur study evaluating 2 polygenic scores for AF suggests that the GPS, containing more than 6.7 million SNPs, exhibits an improved discriminatory capacity in lone AF compared with a PRS possessing 1168 SNPs. Our findings suggest that genetic risk scores for AF that maximally leverage genomic data may provide improved predictive power.
| S-EPMC8209371 | biostudies-literature
Project description:Atrial fibrillation (AF) is the most common serious heart rhythm disorder, with a lifetime incidence of 1 in 4 for patients >40 years of age[1]. AF is a major cause of death and disability, as it is associated with a 4-5 fold increase in the risk of ischemic stroke[2]. In patients with AF, oral anticoagulation (OAC) therapy can reduce the risk of stroke by about two-thirds and the risk of all-cause mortality by approximately one-quarter, but is associated with an increased risk of bleeding[3], [4]. Atrial fibrillation (AF) is the most common serious heart rhythm disorder and is associated with an increased risk of ischemic stroke. This risk can be moderated with oral anticoagulation therapy, but the decision to do so must be balanced against the risks of bleeding. Herein, we discuss three emerging areas where more high-quality evidence is required to guide risk stratification: 1) the relationships between the pattern and burden of AF and stroke 2) the risk conferred by short episodes of device-detected "sub-clinical" atrial fibrillation (SCAF) and 3) the significance of AF that occurs transiently with stress (AFOTS), as is often detected during medical illness or after surgery. Risk stratification is important to identify patients with AF who can benefit from OAC therapy. There are, however, several common clinical scenarios where guidelines do not yet provide direction for stroke prevention; or do so based on limited high-quality evidence.
| S-EPMC5673333 | biostudies-literature
Project description:AimsDiversified cardiovascular/non-cardiovascular multi-morbid risk and efficient machine learning algorithms may facilitate improvements in stroke risk prediction, especially in newly diagnosed non-anticoagulated atrial fibrillation (AF) patients where initial decision-making on stroke prevention is needed. Therefore the aims of this article are to study common clinical risk assessment for stroke risk prediction in AF/non-AF cohorts together with cardiovascular/ non-cardiovascular multi-morbid conditions; to improve stroke risk prediction using machine learning approaches; and to compare the improved clinical prediction rules for multi-morbid conditions using machine learning algorithms.Methods and resultsWe used cohort data from two health plans with 6 457 412 males/females contributing 14,188,679 person-years of data. The model inputs consisted of a diversified list of comorbidities/demographic/ temporal exposure variables, with the outcome capturing stroke event incidences. Machine learning algorithms used two parametric and two nonparametric techniques. The best prediction model was derived on the basis of non-linear formulations using machine learning criteria, with the highest c-index was obtained for logistic regression [0.892; 95% confidence interval (CI) 0.886-0.898] with consistency on external validation (0.891; 95% CI 0.882-0.9). These were significantly higher than those based on the conventional stroke risk scores (CHADS2: 0.7488, 95% CI 0.746-0.7516; CHA2DS2-VASc: 0.7801, 95% CI 0.7772-0.7831) and multi-morbid index (0.8508, 95% CI 0.8483-0.8532). The machine learning algorithm had good internal and external calibration and net benefit values.ConclusionIn this large cohort of newly diagnosed non-anticoagulated AF/non-AF patients, large improvements in stroke risk prediction can be shown with cardiovascular/non-cardiovascular multi-morbid index and a machine learning approach accounting for dynamic changes in risk factors.
| S-EPMC9382661 | biostudies-literature
Project description:Atrial fibrillation (AF) is a major risk factor for cardioembolic stroke. Anticoagulant drugs are effective in preventing AF-related stroke. However, the high frequency of anticoagulant-associated major bleeding is a major concern particularly when antiplatelet treatment is simultaneously administered. Here, microarray analysis in peripheral blood cells in eight patients with AF and stroke and eight AF subjects without stroke identified a stroke related gene expression pattern. HSPA1B, which encodes for heat-shock protein 70 kDa (Hsp70), was the most differentially expressed gene. This gene was downregulated in stroke subjects, a finding confirmed further in an independent AF cohort of 200 individuals. Hsp70 knock-out (KO) mice subjected to different thrombotic challenges developed thrombosis significantly earlier than their wild-type (WT) counterparts. In WT mice, Hsp70 inducers (TRC051384, or tubastatin A) delayed thrombus formation. Remarkably, Hsp70 inducers did not increase the bleeding risk even when aspirin was concomitantly administered. Hsp70 induction was associated with an increased vascular thrombomodulin expression, higher circulating levels of activated protein C (APC) upon thrombotic stimulus and increased protection against endothelial apoptosis. Thus, Hsp70 induction is a novel approach to delay thrombus formation with minimal bleeding risk, being especially promising in situations where there is a major bleeding hazard. Microarray analysis in peripheral blood cells includes eight patients with AF and stroke and eight AF subjects without stroke
2016-04-01 | E-GEOD-66724 | biostudies-arrayexpress
Project description:AimsThe increasing prevalence of ischaemic stroke (IS) can partly be explained by the likewise growing number of patients with chronic kidney disease (CKD). Risk scores have been developed to identify high-risk patients, allowing for personalized anticoagulation therapy. However, predictive performance in CKD is unclear. The aim of this study is to validate six commonly used risk scores for IS in atrial fibrillation (AF) patients across the spectrum of kidney function.Methods and resultsOverall, 36 004 subjects with newly diagnosed AF from SCREAM (Stockholm CREAtinine Measurements), a healthcare utilization cohort of Stockholm residents, were included. Predictive performance of the AFI, CHADS2, Modified CHADS2, CHA2DS2-VASc, ATRIA, and GARFIELD-AF risk scores was evaluated across three strata of kidney function: normal kidney function [estimated glomerular filtration rate (eGFR) >60 mL/min/1.73 m2], mild CKD (eGFR 30-60 mL/min/1.73 m2), and advanced CKD (eGFR <30 mL/min/1.73 m2). Predictive performance was assessed by discrimination and calibration. During 1.9 years, 3069 (8.5%) patients suffered an IS. Discrimination was dependent on eGFR: the median c-statistic in normal eGFR was 0.75 (range 0.68-0.78), but decreased to 0.68 (0.58-0.73) and 0.68 (0.55-0.74) for mild and advanced CKD, respectively. Calibration was reasonable and largely independent of eGFR. The Modified CHADS2 score showed good performance across kidney function strata, both for discrimination [c-statistic: 0.78 (95% confidence interval 0.77-0.79), 0.73 (0.71-0.74) and 0.74 (0.69-0.79), respectively] and calibration.ConclusionIn the most clinically relevant stages of CKD, predictive performance of the majority of risk scores was poor, increasing the risk of misclassification and thus of over- or undertreatment. The Modified CHADS2 score performed good and consistently across all kidney function strata, and should therefore be preferred for risk estimation in AF patients.
| S-EPMC8046502 | biostudies-literature
Project description:AbstractThe performance of scoring systems for risk stratification in patients with atrial fibrillation (AF) was not validated well in patients with stroke. The purpose of this study was to evaluate whether the risk scoring systems predict vascular outcomes in stroke patients with AF.Data were obtained from a nationwide multicenter registry for acute stroke with AF from January 1, 2013, to December 31, 2015. We investigated the predictive power of the CHADS2, CHA2DS2-VASc, ATRIA, and Essen stroke scores in stroke patients with AF. The subjects were further stratified into groups according to treatment with or without oral anticoagulants (OACs).A total of 3112 stroke with AF subjects were included. The rate of recurrent ischemic stroke and any stroke were not associated with the CHADS2, CHA2DS2-VASc, ATRIA, and Essen stroke risk scores. The risks of death and major adverse cerebrovascular and cardiovascular events (MACEs) increased sequentially with the increase of each risk score in OAC group. (the range of C-index 0.544-0.558 for recurrent ischemic stroke; 0.523-0.537 for any stroke; 0.580-0.597 for death; 0.564-0.583 for MACEs). However, in the group treated with OACs, all risk scores were significantly associated with the risk of MACEs. The C-statistics of the 4 scoring systems were 0.544 to 0.558, 0.523 to 0.537, 0.580 to 0.597, 0.564 to 0.583, respectively, for recurrent ischemic stroke, any stroke, death, and MACEs.The performance of the CHADS2, CHA2DS2-VASc, ATRIA, and Essen stroke risk scores for the prediction of recurrent stroke was unsatisfactory in stroke patients with AF whereas the performance for the prediction of recurrent stroke was not MACEs or death was good. A new risk stratification scheme that is specific for secondary stroke prevention in the AF population is needed.
| S-EPMC7837865 | biostudies-literature
Project description: Not available
| S-EPMC5367929 | biostudies-literature
Project description:Background and aimsFemale sex is associated with higher rates of stroke in atrial fibrillation (AF) after adjustment for other CHA2DS2-VASc factors. This study aimed to describe sex differences in age and cardiovascular care to examine their relationship with stroke hazard in AF.MethodsPopulation-based cohort study using administrative datasets of people aged ≥66 years diagnosed with AF in Ontario between 2007 and 2019. Cause-specific hazard regression was used to estimate the adjusted hazard ratio (HR) for stroke associated with female sex over a 2-year follow-up. Model 1 included CHA2DS2-VASc factors, with age modelled as 66-74 vs. ≥ 75 years. Model 2 treated age as a continuous variable and included an age-sex interaction term. Model 3 further accounted for multimorbidity and markers of cardiovascular care.ResultsThe cohort consisted of 354 254 individuals with AF (median age 78 years, 49.2% female). Females were more likely to be diagnosed in emergency departments and less likely to receive cardiologist assessments, statins, or LDL-C testing, with higher LDL-C levels among females than males. In Model 1, the adjusted HR for stroke associated with female sex was 1.27 (95% confidence interval 1.21-1.32). Model 2 revealed a significant age-sex interaction, such that female sex was only associated with increased stroke hazard at age >70 years. Adjusting for markers of cardiovascular care and multimorbidity further decreased the HR, so that female sex was not associated with increased stroke hazard at age ≤80 years.ConclusionOlder age and inequities in cardiovascular care may partly explain higher stroke rates in females with AF.
| S-EPMC10771362 | biostudies-literature
Project description:Atrial Fibrillation (AF) is the most common sustained arrhythmia and 1/6 strokes is attributed to AF. The cornerstone of treatment remains maintaining sinus rhythm or appropriate ventricular rate control in addition to prevention of stroke. Oral anticoagulation therapy (OAC) with vitamin K antagonists (VKAs) has been the gold standard for almost 50 years and a significant reduction in the risk of stroke in patients with AF has been demonstrated. Nonetheless, only 50% of patients with guideline recommendations for OAC treatment actually receive VKAs and half of these will discontinue therapy within 3 to 5 years with only another half achieving therapeutic ranges more than 50% of the time. The aforementioned limitations in addition with frequent blood monitoring have prompted the development of a series of new OAC therapies. The present review focuses on the current pharmacological management for stroke prevention in patients with AF based on current and emerging evidence.
| S-EPMC3763643 | biostudies-other
Project description:Psychological stress has been reported as a possible trigger of atrial fibrillation (AF). No studies have investigated whether any association between stress and AF could be modified by genetic susceptibility to AF (AF-genetic risk score (AF-GRS)). 8765 men and 13,543 women from the Malmö Diet Cancer Study, a population-based cohort, were included in the analyses. A variable representing stress was constructed from questions measuring job strain, and from one question assessing non-occupational stress. Cox proportional hazards regression models were adjusted for known covariates of AF. Mean follow-up times and number of recorded incident AF were 14.2 years and 1116 events for men, and 15.1 years and 932 events for women. Among women, high stress was associated with AF in the age adjusted model (hazard ratio [HR], 1.22; 95% confidence interval [CI], 1.01-1.47) but not following multivariable adjustment (HR, 1.15; 95% CI, 0.95-1.39). Stress was not associated with incident AF in men. AF-GRS was significantly associated with incident AF for both genders. Stress did not interact significantly with genetic susceptibility to AF in men or women. Chronic stress is not associated with long-term incident hospital diagnosed AF. This association does not appear to be modified by genetic susceptibility to AF.
| S-EPMC5307347 | biostudies-literature