Machine Learning Identifies Metabolic Signatures that Predict the Risk of Recurrent Angina in Remitted Patients after Percutaneous Coronary Intervention: A Multicenter Prospective Cohort Study-Reference-Cited by-同舟云学术

Machine Learning Identifies Metabolic Signatures that Predict the Risk of Recurrent Angina in Remitted Patients after Percutaneous Coronary Intervention: A Multicenter Prospective Cohort Study

Published:2021-03-08 Issue:10 Volume:8 Page:2003893
ISSN:2198-3844
Container-title:Advanced Science
language:en
Short-container-title:Adv. Sci.

Author:

Cui Song¹,Li Li²,Zhang Yongjiang²,Lu Jianwei²,Wang Xiuzhen²,Song Xiantao¹,Liu Jinghua¹,Li Kefeng³^ORCID

Affiliation:

1. Department of Cardiology Beijing Anzhen Hospital Capital University of Medical Sciences Beijing 100029 China

2. Department of Cardiology Qufu People's Hospital Qufu Shandong 273100 China

3. School of Medicine University of California San Diego CA 92093 USA

Publisher

Wiley

Subject

General Physics and Astronomy,General Engineering,Biochemistry, Genetics and Molecular Biology (miscellaneous),General Materials Science,General Chemical Engineering,Medicine (miscellaneous)

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/advs.202003893

Reference46 articles.

1. Heart Disease and Stroke Statistics—2017 Update: A Report From the American Heart Association

2. Traditional Management of Chronic Stable Angina

3. Temporal Trends in the Use of Percutaneous Coronary Intervention and Coronary Artery Bypass Surgery in New York State and Ontario

4. Recurrent angina after coronary angioplasty: mechanisms, diagnostic and therapeutic options

5. Angina after percutaneous coronary intervention: The need for precision medicine

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