A machine learning risk score predicts mortality across the spectrum of left ventricular ejection fraction
Author:
Affiliation:
1. Department of Cardiology University of California San Diego CA USA
2. Department of Physics University of California Santa Barbara CA USA
3. Department of Physics University of California San Diego CA USA
Funder
National Institutes of Health
Publisher
Wiley
Subject
Cardiology and Cardiovascular Medicine
Link
https://onlinelibrary.wiley.com/doi/pdf/10.1002/ejhf.2155
Reference10 articles.
1. 2017 ACC/AHA/HFSA Focused Update of the 2013 ACCF/AHA Guideline for the Management of Heart Failure
2. 2016 ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure: the Task Force for the diagnosis and treatment of acute and chronic heart failure of the European Society of Cardiology (ESC). Developed with the special contribution of the Heart Failure Association (HFA) of the ESC;Ponikowski P;Eur J Heart Fail,2016
3. Heart Failure With Preserved, Borderline, and Reduced Ejection Fraction
4. The survival of patients with heart failure with preserved or reduced left ventricular ejection fraction: an individual patient data meta-analysis
5. Predicting survival in heart failure: a risk score based on 39 372 patients from 30 studies
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