Journal of the American Heart Association
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Multicenter Study Comparative Study Observational Study
Coronary Angiography and Intervention in Women Resuscitated From Sudden Cardiac Death.
Background Coronary artery disease is the primary etiology for sudden cardiac arrest in adults, but potential differences in the incidence and utility of invasive coronary testing between resuscitated men and women have not been extensively evaluated. Our aim was to characterize angiographic similarities and differences between men and women after cardiac arrest. Methods and Results Data from the International Cardiac Arrest Registry-Cardiology database included patients resuscitated from out-of-hospital cardiac arrest of presumed cardiac origin, admitted to 7 academic cardiology/resuscitation centers during 2006 to 2017. ⋯ Women were also less often re-vascularized (44% versus 52%, P<0.03). Conclusions Among cardiac arrest survivors, women are less likely to undergo angiography or percutaneous coronary intervention than men. Sex disparities for invasive therapies in post-cardiac arrest care need continued attention.
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Comparative Study
Changes in Nut Consumption and Subsequent Cardiovascular Disease Risk Among US Men and Women: 3 Large Prospective Cohort Studies.
Background We aim to evaluate the association of within-individual changes in consumption of total and specific types of nuts and the subsequent risk of incident cardiovascular disease (CVD) in US men and women. Methods and Results We included 34 103 men from the HPFS (Health Professionals Follow-Up Study) (1986-2012), 77 815 women from the NHS (Nurses' Health Study) (1986-2012), and 80 737 women from the NHS II (1991-2013). We assessed nut consumption every 4 years using validated food frequency questionnaires. ⋯ These data support the role of nut intake in the primary prevention of CVD. Registration URL: http://www.clinicaltrials.gov. Unique identifiers: NCT00005152 and NCT00005182.
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Multicenter Study
Deep Learning-Based Algorithm for Detecting Aortic Stenosis Using Electrocardiography.
Background Severe, symptomatic aortic stenosis (AS) is associated with poor prognoses. However, early detection of AS is difficult because of the long asymptomatic period experienced by many patients, during which screening tools are ineffective. The aim of this study was to develop and validate a deep learning-based algorithm, combining a multilayer perceptron and convolutional neural network, for detecting significant AS using ECGs. ⋯ During internal and external validation, the areas under the receiver operating characteristic curve of the deep learning-based algorithm using 12-lead ECG for detecting significant AS were 0.884 (95% CI, 0.880-0.887) and 0.861 (95% CI, 0.858-0.863), respectively; those using a single-lead ECG signal were 0.845 (95% CI, 0.841-0.848) and 0.821 (95% CI, 0.816-0.825), respectively. The sensitivity map showed the algorithm focused on the T wave of the precordial lead to determine the presence of significant AS. Conclusions The deep learning-based algorithm demonstrated high accuracy for significant AS detection using both 12-lead and single-lead ECGs.