Abstract

The long-term event-free survival (EFS) after acute myocardial infarction (AMI) is largely uninvestigated. We analyzed noninvasive clinical variables in association with long-term EFS after AMI. The present prospective study included 504 consecutive patients with AMI at 3 hospitals from 1995 to 1998 (Adria, Bassano, Conegliano, and Padova Hospitals [ABC] study). Thirty-seven variables were examined, including demographics, cardiovascular risk factors, in-hospital characteristics, and blood components. The end point was 10-year EFS. Logistic and Cox regression models were used to identify the predictive factors. We compared 3 predictive models according to the goodness of fit and C-statistic analyses. At enrollment, the median age was 67 years (interquartile range 58 to 75), 29% were women, 38% had Killip class >1, and the median left ventricular ejection fraction was 51% (interquartile range 43% to 60%). The 10-year EFS rate was 19%. Both logistic and Cox analyses identified independent predictors, including young age (hazard ratio 1.2, 95% confidence interval 1.1 to 1.3, p = 0.0006), no history of angina (hazard ratio 1.4, 95% confidence interval 1.1 to 1.8, p = 0.009), no previous myocardial infarction (hazard ratio 1.4, 95% confidence interval 1.1 to 1.7, p = 0.01), high estimated glomerular filtration rate (hazard ratio 0.8, 95% confidence interval 0.7 to 0.9, p = 0.001), low albumin/creatinine excretion ratio (hazard ratio 1.2, 95% confidence interval 1.1 to 1.3, p <0.0001), and high left ventricular ejection fraction (hazard ratio 0.8, 95% confidence interval 0.7 to 0.9, p = 0.006). These variables had greater predictive power and improved the predictive power of 2 other models, including Framingham cardiovascular risk factors and the recognized predictors of acute heart damage. In conclusion, 10-year EFS was strongly associated with 4 factors (ABC model) typically neglected in studies of AMI survival, including estimated glomerular filtration rate, albumin/creatinine excretion ratio, a history of angina, and previous myocardial infarction. This model had greater predictive power and improved the power of 2 other models using traditional cardiovascular risk factors and indicators of heart damage during AMI.

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