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Predictors of hospital mortality in the global registry of acute coronary events
Quantitative Health Sciences Publications and Presentations
  • Christopher B. Granger, Duke University
  • Robert J. Goldberg, University of Massachusetts Medical School
  • Omar H. Dabbous, University of Massachusetts Medical School
  • Karen S. Pieper, Duke University
  • Kim A. Eagle, University of Michigan
  • Christopher P. Cannon
  • Frans Van de Werf, Universitair Ziekenhuis Gasthuisberg
  • Alvaro Avezum, Dante Pazzanese Institute of Cardiology
  • Shaun G. Goodman, University of Toronto
  • Marcus Flather
  • Keith A. A. Fox, University of Edinburgh
UMMS Affiliation
Department of Medicine, Division of Cardiovascular Medicine
Publication Date
Document Type
Aged; Female; *Hospital Mortality; Humans; Logistic Models; Male; Middle Aged; Multivariate Analysis; Myocardial Ischemia; Prognosis; Risk Assessment; Risk Factors

BACKGROUND: Management of acute coronary syndromes (ACS) should be guided by an estimate of patient risk.

OBJECTIVE: To develop a simple model to assess the risk for in-hospital mortality for the entire spectrum of ACS treated in general clinical practice.

METHODS: A multivariable logistic regression model was developed using 11 389 patients (including 509 in-hospital deaths) with ACS with and without ST-segment elevation enrolled in the Global Registry of Acute Coronary Events (GRACE) from April 1, 1999, through March 31, 2001. Validation data sets included a subsequent cohort of 3972 patients enrolled in GRACE and 12 142 in the Global Use of Strategies to Open Occluded Coronary Arteries IIb (GUSTO-IIb) trial.

RESULTS: The following 8 independent risk factors accounted for 89.9% of the prognostic information: age (odds ratio [OR], 1.7 per 10 years), Killip class (OR, 2.0 per class), systolic blood pressure (OR, 1.4 per 20-mm Hg decrease), ST-segment deviation (OR, 2.4), cardiac arrest during presentation (OR, 4.3), serum creatinine level (OR, 1.2 per 1-mg/dL [88.4- micro mol/L] increase), positive initial cardiac enzyme findings (OR, 1.6), and heart rate (OR, 1.3 per 30-beat/min increase). The discrimination ability of the simplified model was excellent with c statistics of 0.83 in the derived database, 0.84 in the confirmation GRACE data set, and 0.79 in the GUSTO-IIb database.

CONCLUSIONS: Across the entire spectrum of ACS and in general clinical practice, this model provides excellent ability to assess the risk for death and can be used as a simple nomogram to estimate risk in individual patients.

DOI of Published Version
Arch Intern Med. 2003 Oct 27;163(19):2345-53. Link to article on publisher's site
PubMed ID
Related Resources
Link to Article in PubMed
Citation Information
Christopher B. Granger, Robert J. Goldberg, Omar H. Dabbous, Karen S. Pieper, et al.. "Predictors of hospital mortality in the global registry of acute coronary events" Vol. 163 Iss. 19 (2003) ISSN: 0003-9926 (Linking)
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