AIMS: We investigated predictors of 90-day risk among patients surviving the early period after an acute coronary syndrome (ACS). METHODS AND RESULTS: The study population included 15 904 stabilized ST-segment elevation or non-ST-segment elevation ACS patients randomized in SYMPHONY and 2nd SYMPHONY. We developed risk models for death, death or myocardial infarction (MI), and death, MI, or severe recurrent ischaemia (SRI) using Cox proportional-hazards techniques. Demographic, history, and pre-randomization clinical and medication variables were tested. Validation techniques included development of individual trial models, backward elimination and bootstrapping. Of 118 variables, 17 independently predicted mortality. The strongest associations included greater age (chi(2)=31.1), higher randomization heart rate (chi(2)=27.4), and heart failure (HF) variables (HF between qualifying event and randomization, chi(2)=21.8; history of HF, chi(2)=12.2). Higher creatinine clearance (chi(2)=17.7) and percutaneous coronary intervention between qualifying event and randomization (chi(2)=11.1) most strongly predicted lower risk. Similar characteristics entered the double and triple composite models, but HF variables and age less strongly predicted these end-points. CONCLUSIONS: In patients stabilized after ACS, those at highest risk over the next 90 days can be identified. Typical clinical markers are better at identifying risk of death than non-fatal MI or SRI. Novel risk markers are needed for these outcomes.
RCT Entities:
AIMS: We investigated predictors of 90-day risk among patients surviving the early period after an acute coronary syndrome (ACS). METHODS AND RESULTS: The study population included 15 904 stabilized ST-segment elevation or non-ST-segment elevation ACS patients randomized in SYMPHONY and 2nd SYMPHONY. We developed risk models for death, death or myocardial infarction (MI), and death, MI, or severe recurrent ischaemia (SRI) using Cox proportional-hazards techniques. Demographic, history, and pre-randomization clinical and medication variables were tested. Validation techniques included development of individual trial models, backward elimination and bootstrapping. Of 118 variables, 17 independently predicted mortality. The strongest associations included greater age (chi(2)=31.1), higher randomization heart rate (chi(2)=27.4), and heart failure (HF) variables (HF between qualifying event and randomization, chi(2)=21.8; history of HF, chi(2)=12.2). Higher creatinine clearance (chi(2)=17.7) and percutaneous coronary intervention between qualifying event and randomization (chi(2)=11.1) most strongly predicted lower risk. Similar characteristics entered the double and triple composite models, but HF variables and age less strongly predicted these end-points. CONCLUSIONS: In patients stabilized after ACS, those at highest risk over the next 90 days can be identified. Typical clinical markers are better at identifying risk of death than non-fatal MI or SRI. Novel risk markers are needed for these outcomes.
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