Literature DB >> 25194289

Heart failure risk prediction models: what have we learned?

Wayne C Levy1, Inder S Anand2.   

Abstract

Entities:  

Keywords:  heart failure; mortality; prognosis; risk prediction

Mesh：

Year:  2014        PMID： 25194289     DOI： 10.1016/j.jchf.2014.05.006

Source DB:  PubMed          Journal:  JACC Heart Fail        ISSN： 2213-1779            Impact factor:   12.035


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  5 in total

1.  Analysis of Machine Learning Techniques for Heart Failure Readmissions.

Authors:  Bobak J Mortazavi; Nicholas S Downing; Emily M Bucholz; Kumar Dharmarajan; Ajay Manhapra; Shu-Xia Li; Sahand N Negahban; Harlan M Krumholz
Journal:  Circ Cardiovasc Qual Outcomes       Date:  2016-11-08

2.  Decongestion Models and Metrics in Acute Heart Failure: ESCAPE Data in the Age of the Implantable Cardiac Pressure Monitor.

Authors:  David Paniagua; Glenn N Levine; Lorraine D Cornwell; Ernesto Jimenez; Biswajit Kar; Hani Jneid; Ali E Denktas; Tony S Ma
Journal:  Tex Heart Inst J       Date:  2022-07-01

3.  The prognosis of patients hospitalized with a first episode of heart failure, validation of two scores: PREDICE and AHEAD.

Authors:  Francisco Ruiz-Ruiz; Miguel Menéndez-Orenga; Francisco J Medrano; Enrique J Calderón; David Lora-Pablos; Maria Asunción Navarro-Puerto; Patricia Rodríguez-Torres; Agustín Gómez de la Cámara
Journal:  Clin Epidemiol       Date:  2019-07-22       Impact factor: 4.790

Review 4.  Left ventricular hypertrophy and sudden cardiac death.

Authors:  Grigorios Giamouzis; Apostolos Dimos; Andrew Xanthopoulos; John Skoularigis; Filippos Triposkiadis
Journal:  Heart Fail Rev       Date:  2021-06-28       Impact factor: 4.214

5.  Compelling Benefit of Soluble Suppression of Tumorigenicity-2 in Post-Myocardial Infarction Estimation of Risk: The Time Is Right for Its Routine Use in the Clinic.

Authors:  Javier Díez; Antoni Bayes-Genis
Journal:  J Am Heart Assoc       Date:  2017-10-20       Impact factor: 5.501
  5 in total

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