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.
To develop a simple decision tool for bedside risk estimation of 6-month
mortality in patients surviving admission for an ACS.
Design, Setting, and Patients
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.
Main Outcome Measure
All-cause mortality during 6 months postdischarge after admission for
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
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.