The Seattle Heart Failure Model: Prediction of Survival in Heart Failure
By W. Levy, D. Mozaffarian, D. Linker, S. Sutradhar, S. Anker, A. Cropp, I. Anand, A. Maggioni, P. Burton, Michael J. Sullivan and 4 more. Circulation, 2006
2,020 citations recorded by the source corpus.
Subfields: Cardiology and Cardiovascular Medicine
View Paper: https://www.semanticscholar.org/paper/211c0b7f30930ad92da6650d45d1e3c7f3c5f0c9
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In Cardiology and Cardiovascular Medicine
Citations in the corpus
- 2013 ACCF/AHA guideline for the management of heart failure: a report of the American College of Cardiology Foundation/American Heart Association Task Force on Practice Guidelines. (2013, background): “Seattle Heart Failure Model (203) http://SeattleHeartFailureModel.”
- Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone (2020, background): “Although scientists have identified a broad set of predictors and indicators, there is no shared consensus on their relative impact on survival prediction [38].”
- Red cell distribution width as a novel prognostic marker in heart failure: data from the CHARM Program and the Duke Databank. (2007, background): “To date, a wide variety of clinical variables nd biological markers have been used to create predictive odels for survival in patients with chronic heart ailure (1,2).”
- Forecasting the Impact of Heart Failure in the United States (2013, background): “Although the median survival for patients with symptomatic HF is ≈5 years, the clinical course for an individual patient is typically nonlinear and relatively unpredictable, with acute episodes of decompensation often separated by relative periods of stability.(64,65) The relative uncertainty in pro”
- 2013 ACCF/AHA guideline for the management of heart failure: executive summary: a report of the American College of Cardiology Foundation/American Heart Association Task Force on practice guidelines. (2013, neutral): “Seattle Heart Failure Model (56) http://Seat”