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Heart. 2009 Mar;95(3):221-7. doi: 10.1136/hrt.2008.144022. Epub 2008 May 8.

Evaluation of risk scores for risk stratification of acute coronary syndromes in the Myocardial Infarction National Audit Project (MINAP) database.

Author information

  • 1Division of Cardiovascular and Diabetes Research, Leeds Institute of Genetics Health and Therapeutics, University of Leeds, Clarendon Way, West Yorkshire, Leeds LS2 9JT, UK. c.p.gale@leeds.ac.uk

Abstract

OBJECTIVE:

To compare the discriminative performance of the PURSUIT, GUSTO-1, GRACE, SRI and EMMACE risk models, assess their performance among risk supergroups and evaluate the EMMACE risk model over the wider spectrum of acute coronary syndrome (ACS).

DESIGN:

Observational study of a national registry.

SETTING:

All acute hospitals in England and Wales.

PATIENTS:

100 686 cases of ACS between 2003 and 2005.

MAIN OUTCOME MEASURES:

Model performance (C-index) in predicting the likelihood of death over the time period for which they were designed. The C-index, or area under the receiver-operating curve, range 0-1, is a measure of the discriminative performance of a model.

RESULTS:

The C-indexes were: PURSUIT C-index 0.79 (95% confidence interval 0.78 to 0.80); GUSTO-1 0.80 (0.79 to 0.81); GRACE in-hospital 0.80 (0.80 to 0.81); GRACE 6-month 0.80 (0.79 to 0.80); SRI 0.79 (0.78 to 0.80); and EMMACE 0.78 (0.77 to 0.78). EMMACE maintained its ability to discriminate 30-day mortality throughout different ACS diagnoses. Recalibration of the model offered no notable improvement in performance over the original risk equation. For all models the discriminative performance was reduced in patients with diabetes, chronic renal failure or angina.

CONCLUSION:

The five ACS risk models maintained their discriminative performance in a large unselected English and Welsh ACS population, but performed less well in higher-risk supergroups. Simpler risk models had comparable performance to more complex risk models. The EMMACE risk score performed well across the wider spectrum of ACS diagnoses.

Comment in

PMID:
18467355
[PubMed - indexed for MEDLINE]
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