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Despite advances in their treatment, acute coronary syndromes (ACS) continue to carry significant mortality and morbidity.1 Identifying higher-risk patients, and hence selecting those who would most benefit from more aggressive treatments, forms an integral approach to ACS management and outcome.1 2 Such stratification of patients on admission (near-point testing) is based on patient-specific variables which have been shown in randomised controlled trials (RCTs) and observational databases to be associated with mortality. The logistic regression of these variables on clinical outcomes to generate scores of risk has sought to help doctors identify patients for whom tailored treatment would offer a reduction in mortality risk.
ACS RISK SCORES
The past few years have seen a palpable increase in the number of ACS risk scores.3–10 This is predominantly because of the wealth of data recorded by RCT and observational datasets, but also because of the relative ease by which simple risk modelling can now be undertaken using statistical software packages. Furthermore, stratification of ACS risk is recommended by practice guidelines with tools assisting this perceived to be beneficial.1 3 Thrombolysis in Myocardial Infarction (TIMI),4 5 Platelet glycoprotein IIb/IIIa in Unstable angina: Receptor Suppression Using Integrillin Therapy (PURSUIT),6 simple risk index (SRI),7 Global Registry of Acute Coronary Events (GRACE)8 and the Evaluation of the Methods and Management of Acute Coronary Events (EMMACE)9 are the probably the most familiar and commonly used ACS risk scores.
The TIMI risk score was first derived for patients with ST elevation myocardial infarction eligible for thrombolysis in the InTime II trial.5 The model offered good discriminative …
Funding: We gratefully acknowledge funding from the British Heart Foundation.
Competing interests: None.