title: A validated prediction model for all forms of acute coronary syndrome: estimating the risk of 6-month postdischarge death in an international registry.
abstract: BACKGROUND
  Accurate estimation of risk for untoward outcomes after patients have been hospitalized for an acute coronary syndrome (ACS) may help clinicians guide the type and intensity of therapy.
  
  
  OBJECTIVE
  To develop a simple decision tool for bedside risk estimation of 6-month mortality in patients surviving admission for an ACS.
  
  
  METHODS
  A multinational registry, involving 94 hospitals in 14 countries, that used data from the Global Registry of Acute Coronary Events (GRACE) to develop and validate a multivariable stepwise regression model for death during 6 months postdischarge. From 17,142 patients presenting with an ACS from April 1, 1999, to March 31, 2002, and discharged alive, 15,007 (87.5%) had complete 6-month follow-up and represented the development cohort for a model that was subsequently tested on a validation cohort of 7638 patients admitted from April 1, 2002, to December 31, 2003.
  
  
  METHODS
  All-cause mortality during 6 months postdischarge after admission for an ACS.
  
  
  RESULTS
  The 6-month mortality rates were similar in the development (n = 717; 4.8%) and validation cohorts (n = 331; 4.7%). The risk-prediction tool for all forms of ACS identified 9 variables predictive of 6-month mortality: older age, history of myocardial infarction, history of heart failure, increased pulse rate at presentation, lower systolic blood pressure at presentation, elevated initial serum creatinine level, elevated initial serum cardiac biomarker levels, ST-segment depression on presenting electrocardiogram, and not having a percutaneous coronary intervention performed in hospital. The c statistics for the development and validation cohorts were 0.81 and 0.75, respectively.
  
  
  CONCLUSIONS
  The GRACE 6-month postdischarge prediction model is a simple, robust tool for predicting mortality in patients with ACS. Clinicians may find it simple to use and applicable to clinical practice.
authors.affiliation: Department of Internal Medicine, University of Michigan Health System, Ann Arbor, USA. keagle@umich.edu
authors.forename: Kim A
authors.initials: KA
authors.lastname: Eagle
authors.forename: Michael J
authors.initials: MJ
authors.lastname: Lim
authors.forename: Omar H
authors.initials: OH
authors.lastname: Dabbous
authors.forename: Karen S
authors.initials: KS
authors.lastname: Pieper
authors.forename: Robert J
authors.initials: RJ
authors.lastname: Goldberg
authors.forename: Frans
authors.initials: F
authors.lastname: Van de Werf
authors.forename: Shaun G
authors.initials: SG
authors.lastname: Goodman
authors.forename: Christopher B
authors.initials: CB
authors.lastname: Granger
authors.forename: P Gabriel
authors.initials: PG
authors.lastname: Steg
authors.forename: Joel M
authors.initials: JM
authors.lastname: Gore
authors.forename: Andrzej
authors.initials: A
authors.lastname: Budaj
authors.forename: Alvaro
authors.initials: A
authors.lastname: Avezum
authors.forename: Marcus D
authors.initials: MD
authors.lastname: Flather
authors.forename: Keith A A
authors.initials: KA
authors.lastname: Fox
mesh_terms: D000368:Aged; D000789:Angina, Unstable / Q000401:mortality*; D002423:Cause of Death; D003661:Decision Support Techniques; D005260:Female; D006760:Hospitalization; D006801:Humans; D008297:Male; D008875:Middle Aged; D017202:Myocardial Ischemia / Q000401:mortality*; D012042:Registries; D018570:Risk Assessment