A validated prediction model for all forms of acute coronary syndrome: estimating the risk of 6-month postdischarge death in an international registry.

By K. Eagle, M. Lim, O. Dabbous, K. Pieper, R. Goldberg, F. Werf, S. Goodman, C. Granger, P. Steg, J. Gore and 5 more. Journal of the American Medical Association (JAMA), 2004

853 citations recorded by the source corpus.

Subfields: Cardiology and Cardiovascular Medicine, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/02d06fc6420bd2ad5aa32a6db89e8af684e2c3fd

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cardiology and Cardiovascular Medicine

In Radiology, Nuclear Medicine and Imaging

M. Creager

Citations in the corpus

2014 AHA/ACC guideline for the management of patients with non-ST-elevation acute coronary syndromes: executive summary: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines. (2014, background): “005 MACE reduction by intensive LLT 15% (11-18) <0.”
2007 focused update of the ACC/AHA/SCAI 2005 guideline update for percutaneous coronary intervention: a report of the American College of Cardiology/American Heart Association Task Force on Practice guidelines. (2008, background): “The GRACE (Global Registry of Acute Coronary vents) study risk model, which predicts in-hospital morality (and death or MI), can be useful to clinicians to guide reatment type and intensity (20,21).”
Prediction of adverse cardiac events in emergency department patients with chest pain using machine learning for variable selection (2014, methodology): “Various systems exist for stratifying the risk of acute coronary syndromes (ACS) [4-6].”
Estimating Individualized Treatment Rules Using Outcome Weighted Learning (2012, methodology): “…for developing individual-adaptive interventions is to classify subjects into different risk levels estimated by a parametric or semiparametric regression model using prognostic factors, and then to assign therapy according to risk level (Eagle et al. 2004; Marlowe et al. 2007; Cai et al. 2010).”