Coronary Artery Bypass Grafting
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Last Posted: May 05, 2023
- Effect of the machine learning-derived Hypotension Prediction Index (HPI) combined with diagnostic guidance versus standard care on depth and duration of intraoperative and postoperative hypotension in elective cardiac surgery patients: HYPE-2 - study protocol of a randomised clinical trial.
Santino R Rellum et al. BMJ Open 2023 13(5) e061832 - Prediction of new onset postoperative atrial fibrillation using a simple Nomogram.
Siming Zhu et al. J Cardiothorac Surg 2023 18(1) 139 - Machine Learning to Identify Patients at Risk of Developing New-Onset Atrial Fibrillation after Coronary Artery Bypass.
Orlando Parise et al. Journal of cardiovascular development and disease 2023 10(2) - Machine learning improves mortality prediction in three-vessel disease.
Xinxing Feng et al. Atherosclerosis 2023 3671-7 - Using machine learning to aid treatment decision and risk assessment for severe three-vessel coronary artery disease.
Jie Liu et al. Journal of geriatric cardiology : JGC 2022 19(5) 367-376 - Diagnostic Model of In-Hospital Mortality in Patients with Acute ST-Segment Elevation Myocardial Infarction Used Artificial Intelligence Methods.
Li Yong et al. Cardiology research and practice 2022 20228758617 - Electrocardiography-Based Artificial Intelligence Algorithm Aids in Prediction of Long-term Mortality After Cardiac Surgery.
Mahayni Abdulah A et al. Mayo Clinic proceedings 2021 96(12) 3062-3070 - Predicting Postoperative Length of Stay for Isolated Coronary Artery Bypass Graft Patients Using Machine Learning.
Alshakhs Fatima et al. International journal of general medicine 2020 13751-762 - Comparison of Machine Learning Methods With National Cardiovascular Data Registry Models for Prediction of Risk of Bleeding After Percutaneous Coronary Intervention.
Mortazavi Bobak J et al. JAMA network open 2019 Jul 2(7) e196835 - Burden of familial heterozygous hypercholesterolemia in Uzbekistan: Time is muscle.
Shek Aleksandr et al. Atherosclerosis 2018 Oct 277524-529
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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 the CDC Office of Public Health Genomics 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, 2023
- Page last updated:Jun 05, 2023
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