A Clinical Model for Predicting Survival Following Acute Myocardial Infarction in Patients Without Cardiogenic Shock: A Multivariate (Cox) Analysis

Abstract

A multivariate predictive model for early (six-month) survival based on Cox's proportional-hazards regression model was developed using data collected prospectively from 317 consecutive patients admitted with acute myocardial infarction to a coronary care unit (CCU). Of these, 63 (19.8%) died within the follow-up period. Patients with cardiogenic shock were excluded from the study. Variables associated with survival were sought from clinical, historical, electrocardiographic and radiographic variables recorded at the time of admission. On multivariate analysis, a stepwise selection procedure identified four variables which described the probability of survival for the six-month follow-up. These were: age, upper lung crepitations, marginal and also definite radiographic cardiomegaly on an anteroposterior radiograph. With this combination of clinical variables alone, using a survival probability partition value of 80%, the model had a sensitivity of 67% and a specificity of 75%. However, the model's predictive accuracy for death was 40%, compared to a predictive accuracy for survival of 90%. This clinical model is most useful for early discrimination of those patients at low risk of death within six months of CCU admission. Other predictive tests for premature death would need to exceed these discriminatory criteria to justify their cost and risks.