Derivation and validation of a combined in-hospital mortality and bleeding risk model in acute myocardial infarction

Background: In the potent new antiplatelet era, it is important issue how to balance the ischemic risk and the bleeding risk. However, previous risk models have been developed separately for in-hospital mortality and major bleeding risk. Therefore, we aimed to develop and validate a novel combined model to predict the combined risk of in-hospital mortality and major bleeding at the same time for initial decision making in patients with acute myocardial infarction (AMI). Methods: Variables from the Korean Acute Myocardial Infarction Registry (KAMIR) – National Institute of Health (NIH) database were used to derive (n = 8955) and validate (n = 3838) a multivariate logistic regression model. Major adverse cardiovascular events (MACEs) were defined as in-hospital death and major bleeding. Results: Seven factors were associated with MACE in the model: age, Killip class, systolic blood pressure, heart rate, serum glucose, glomerular filtration rate, and initial diagnosis. The risk model discriminated well in the derivation (c-static = 0.80) and validation (c-static = 0.80) cohorts. The KAMIR-NIH risk score was developed from the model and corresponded well with observed MACEs: very low risk (0.9%), low risk (1.7%), moderate risk (4.2%), high risk (8.6%), and very high risk (23.3%). In patients with MACEs, a KAMIR-NIH risk score ≤ 10 was associated with high bleeding risk, whereas a KAMIR-NIH risk score > 10 was associated with high in-hospital mortality. Conclusion: The KAMIR-NIH in-hospital MACEs model using baseline variables stratifies comprehensive risk for in-hospital mortality and major bleeding, and is useful for guiding initial decision making.