Literature DB >> 21906244

Multivariate risk scores and patient outcomes in advanced heart failure.

Eric S Ketchum1, Wayne C Levy.   

Abstract

Significant improvements in survival have occurred for patients with advanced heart failure, with an increasing array of therapeutic options sharing quite varied properties of cost, invasiveness, and impact on life expectancy. Risk models allow patients and providers to achieve a better understanding of prognosis than is possible through unstructured holistic assessment. This article reviews scoring systems for heart failure prognostication in the general sense and in the setting of providing answers to specific clinical queries. Topics addressed include outpatient survival, risk of inpatient and post-discharge mortality, potential changes to clinician decision-making through better understanding of prognosis, and mortality after having a left ventricular assist device placed or receiving an implantable cardiac-defibrillator.
© 2011 Wiley Periodicals, Inc.

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Year:  2011        PMID: 21906244     DOI: 10.1111/j.1751-7133.2011.00241.x

Source DB:  PubMed          Journal:  Congest Heart Fail        ISSN: 1527-5299


  7 in total

1.  The utility of biomarker risk prediction score in patients with chronic heart failure.

Authors:  Alexander E Berezin; Alexander A Kremzer; Yulia V Martovitskaya; Tatyana A Berezina; Tatyana A Samura
Journal:  Clin Hypertens       Date:  2016-03-11

Review 2.  Predicting mortality in patients with acute heart failure: Role of risk scores.

Authors:  Andrea Passantino; Francesco Monitillo; Massimo Iacoviello; Domenico Scrutinio
Journal:  World J Cardiol       Date:  2015-12-26

3.  The utility of biomarker risk prediction score in patients with chronic heart failure.

Authors:  Alexander E Berezin; Alexander A Kremzer; Yulia V Martovitskaya; Tatyana A Samura; Tatyana A Berezina; Anthony Zulli; Jan Klimas; Peter Kruzliak
Journal:  Int J Clin Exp Med       Date:  2015-10-15

4.  Clinical factors associated with early readmission among acutely decompensated heart failure patients.

Authors:  Bredy Pierre-Louis; Shareen Rodriques; Vanessa Gorospe; Achuta K Guddati; Wilbert S Aronow; Chul Ahn; Maurice Wright
Journal:  Arch Med Sci       Date:  2016-05-18       Impact factor: 3.318

Review 5.  Heart Failure: Diagnosis, Severity Estimation and Prediction of Adverse Events Through Machine Learning Techniques.

Authors:  Evanthia E Tripoliti; Theofilos G Papadopoulos; Georgia S Karanasiou; Katerina K Naka; Dimitrios I Fotiadis
Journal:  Comput Struct Biotechnol J       Date:  2016-11-17       Impact factor: 7.271

6.  Validation of the MAGGIC (Meta-Analysis Global Group in Chronic Heart Failure) heart failure risk score and the effect of adding natriuretic peptide for predicting mortality after discharge in hospitalized patients with heart failure.

Authors:  Sayma Sabrina Khanam; Eunhee Choi; Jung-Woo Son; Jun-Won Lee; Young Jin Youn; Junghan Yoon; Seung-Hwan Lee; Jang-Young Kim; Sung Gyun Ahn; Min-Soo Ahn; Seok-Min Kang; Sang Hong Baek; Eun-Seok Jeon; Jae-Joong Kim; Myeong-Chan Cho; Shung Chull Chae; Byung-Hee Oh; Dong-Ju Choi; Byung-Su Yoo
Journal:  PLoS One       Date:  2018-11-28       Impact factor: 3.240

7.  Meta-Analysis Global Group in Chronic (MAGGIC) Heart Failure Risk Score: Validation of a Simple Tool for the Prediction of Morbidity and Mortality in Heart Failure With Preserved Ejection Fraction.

Authors:  Jonathan D Rich; Jacob Burns; Benjamin H Freed; Mathew S Maurer; Daniel Burkhoff; Sanjiv J Shah
Journal:  J Am Heart Assoc       Date:  2018-10-16       Impact factor: 5.501

  7 in total

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