Predicting adverse outcomes after myocardial infarction among patients with diabetes mellitus

Background - Although patients with diabetes mellitus experience high rates of adverse events after acute myocardial infarction (AMI), including death and recurrent ischemia, some diabetic patients are likely at low risk, whereas others are at high risk. We sought to develop prediction models to stratify risk after AMI in patients with diabetes mellitus. Methods and Results - We developed prediction models for long-term mortality and angina among 1613 patients with diabetes mellitus discharged alive after AMI from 24 US hospitals and then validated the models in a separate, multicenter registry of 786 patients with diabetes mellitus. Event rates in the derivation cohort were 27% for 5-year mortality and 27% for 1-year angina. Parsimonious prediction models demonstrated good discrimination (c-indices=0.78 and 0.69, respectively) and excellent calibration. Within the context of the predictors we estimated, the strongest predictors for mortality were higher creatinine, not working at the time of the AMI, older age, lower hemoglobin, left ventricular dysfunction, and chronic heart failure. The strongest predictors for angina were angina burden in the 4 weeks before the AMI, younger age, history of prior coronary bypass graft surgery, and non-white race. The lowest and highest deciles of predicted risk ranged from 4% to 80% for mortality and 12% to 59% for angina. The models also performed well in external validation (c-indices=0.78 and 0.73, respectively). Conclusions - We found a wide range of risk for adverse outcomes after AMI in diabetic patients. Predictive models can identify patients with diabetes mellitus for whom closer follow-up and aggressive secondary prevention strategies should be considered.