[Multivariate analysis of prognostically significant parameters in acute transmural myocardial infarct]

The clinical data of 722 patients admitted for acute myocardial infarction to the coronary care unit of the Hannover Medical School were retrospectively analyzed. Six hundred patients survived through the fifth day of their hospital stay. We evaluated 142 variables from each patient, i.e., previous cardiac manifestations, drug-history, acute complications, laboratory data, intensive care treatment and the 1-year outcome. One-hundred-sixty-nine patients underwent cardiac catheterization before being discharged from the hospital. Thirty-two variables showed to be predictive of 1-year survival in the univariate analysis, although performance of logistic regression analysis revealed only seven parameters to be independent predictors: age (p < 0.0001), glycoside intake before infarction (p = 0.0317), acute heart failure (p = 0.0005), late (occurring after 48 h) ventricular tachycardia or fibrillation (p = 0.0003), maximum of serum creatine phosphokinase (p = 0.0129), new onset of atrial fibrillation (p = 0.0116), and use of dobutamine during intensive care stay (p = 0.0014). With this combination of clinical variables alone, using a survival probability partition value of 50%, the model had a sensitivity of 39% and a specificity of 96%, respectively, 84% overall correct classification. Predictive accuracy for death was 71%, compared to a predictive accuracy for survival of 85%. Diagnostic procedures performed after infarction were highly predictive in the individual case, but they could not improve accuracy of the statistical model. These data emphasize the importance of multivariate methods to find suitable predictors for outcome after acute myocardial infarction.