Atrial Fibrillation
What's New
Last Posted: May 16, 2024
- Genome-wide association studies reveal differences in genetic susceptibility between single events vs. recurrent events of atrial fibrillation and myocardial infarction: the HUNT study.
Martina Hall et al. Front Cardiovasc Med 2024 111372107 - Predictors of Disease Progression and Adverse Clinical Outcomes in Patients with Moderate Aortic Stenosis Using an Artificial Intelligence-Based Software Platform.
Mahmoud Salem et al. Am J Cardiol 2024 - Deep learning evaluation of echocardiograms to identify occult atrial fibrillation.
Neal Yuan et al. NPJ Digit Med 2024 7(1) 96 - Many Models, Little Adoption-What Accounts for Low Uptake of Machine Learning Models for Atrial Fibrillation Prediction and Detection?
Yuki Kawamura et al. J Clin Med 2024 13(5) - Scalable Approach to Consumer Wearable Postmarket Surveillance: Development and Validation Study.
Richard M Yoo et al. JMIR Med Inform 2024 12e51171 - Genetic testing in cardiovascular disease.
Michael P Gray et al. Med J Aust 2024 - PRERISK: A Personalized, Artificial Intelligence-Based and Statistically-Based Stroke Recurrence Predictor for Recurrent Stroke.
Giorgio Colangelo et al. Stroke 2024 - Development and Validation of Machine Learning Algorithms to Predict 1-Year Ischemic Stroke and Bleeding Events in Patients with Atrial Fibrillation and Cancer.
Bang Truong et al. Cardiovasc Toxicol 2024 - An Arrhythmia classification approach via deep learning using single-lead ECG without QRS wave detection.
Liong-Rung Liu et al. Heliyon 2024 10(5) e27200 - Accuracy and comprehensibility of chat-based artificial intelligence for patient information on atrial fibrillation and cardiac implantable electronic devices.
Henrike A K Hillmann et al. Europace 2023 - Prediction of atrial fibrillation from at-home single-lead ECG signals without arrhythmias
M Gadaleta et al, NPJ Digital Medicine, December 12, 2023 - Single-lead ECG AI model with risk factors detects Atrial Fibrillation during Sinus Rhythm.
Stijn Dupulthys et al. Europace 2023 - A novel risk score predicting 30-day hospital re-admission of patients with acute stroke by machine learning model.
Giovanna Mercurio et al. Eur J Neurol 2023 - Predicting short-term outcomes in atrial-fibrillation-related stroke using machine learning.
Eun-Tae Jeon et al. Front Neurol 2023 141243700 - Genetic risk, adherence to healthy lifestyle and acute cardiovascular and thromboembolic complications following SARS-COV-2 infection.
Junqing Xie et al. Nat Commun 2023 14(1) 4659 - Deep learning-based NT-proBNP prediction from the ECG for risk assessment in the community.
Meraj Neyazi et al. Clin Chem Lab Med 2023 - Familial hypercholesterolemia is related to cardiovascular disease, heart failure and atrial fibrillation. Results from a population-based study.
Hayato Tada et al. Eur J Clin Invest 2023 e14119 - The GenoVA study: Equitable implementation of a pragmatic randomized trial of polygenic-risk scoring in primary care.
Jason L Vassy et al. Am J Hum Genet 2023 110(11) 1841-1852 - Artificial intelligence-enhanced 12-lead electrocardiography for identifying atrial fibrillation during sinus rhythm (AIAFib) trial: protocol for a multicenter retrospective study.
Yong-Soo Baek et al. Front Cardiovasc Med 2023 101258167 - Prediction model of atrial fibrillation recurrence after Cox-Maze IV procedure in patients with chronic valvular disease and atrial fibrillation based on machine learning algorithm.
Zenan Jiang et al. Zhong Nan Da Xue Xue Bao Yi Xue Ban 2023 48(7) 995-1007
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About HLBS-PopOmics
HLBS-PopOmics is an online, continuously updated, searchable database of published scientific literature, CDC and NIH resources, and other materials that address the translation of genomic and other precision health discoveries into improved health care and prevention related to Heart and Vascular Diseases(H), Lung Diseases(L), Blood Diseases(B), and Sleep Disorders(S)...more
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Mensah GA, Yu W, Barfield WL, Clyne M, Engelgau MM, Khoury MJ. HLBS-PopOmics: an online knowledge base to accelerate dissemination and implementation of research advances in population genomics to reduce the burden of heart, lung, blood, and sleep disorders. Genet Med. 2018 Sep 10. doi: 10.1038/s41436-018-0118-1
Disclaimer: Articles listed in the Public Health Knowledge Base are selected by Public Health Genomics Branch to provide current awareness of the literature and news. Inclusion in the update does not necessarily represent the views of the Centers for Disease Control and Prevention nor does it imply endorsement of the article's methods or findings. CDC and DHHS assume no responsibility for the factual accuracy of the items presented. The selection, omission, or content of items does not imply any endorsement or other position taken by CDC or DHHS. Opinion, findings and conclusions expressed by the original authors of items included in the update, or persons quoted therein, are strictly their own and are in no way meant to represent the opinion or views of CDC or DHHS. References to publications, news sources, and non-CDC Websites are provided solely for informational purposes and do not imply endorsement by CDC or DHHS.
- Page last reviewed:Feb 1, 2024
- Page last updated:May 14, 2024
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