GRACE score; gender impact; race impact; acute coronary syndrome; hospital mortality; health status disparities; acute coronary syndrome; hospital mortality; heart diseases; Global Registry of Acute Coronary Events


Cardiovascular Diseases



Acute coronary syndrome (ACS) causes significant global morbidity and mortality and requires early risk stratification. The global registry of acute coronary events (GRACE) score is a well-known, validated risk stratification system that does not include race and gender. We aimed to assess whether the addition of gender and race could add to the predictability of the GRACE score model.


We performed a retrospective cohort study of 46 764 ACS patients from the files of a national healthcare system. We compared the predictability of the GRACE score in conjunction with gender and race versus the original GRACE score. Different possible associations of predictability were investigated and statistically calculated. The accuracy of the prediction models was assessed using the receiver operating characteristic curve and its respective area under the curve (AUC). We compared the AUC of the 2 models, with the significance set at a P value of less than .05.


Our comparison favored the original GRACE score over the modified prediction model with gender and race added (AUC = 0.838 and 0.839 respectively, P = .008). Although the P value comparing the AUC shows that the original GRACE was superior, due to our large dataset, the actual numbers are similar and may not be clinically significant.

Gender and race were significantly associated with in-hospital mortality (P < .001, P = .002, respectively). However, this relationship disappeared in the multivariate analysis. Gender significantly predicted in-hospital mortality, with females 1.167 times more likely to die (P < .001). Non-white racial groups had lower in-hospital mortality than whites (OR: 0.823, P = .03).


The GRACE score was valid in its original form and its ability to predict mortality was not substantially improved by including gender and race.