Prediction of Survival after Myocardial Infarction Using Killip Class

Background: Short and long term predictions of mortality and survival after a myocardial infarction (MI) are important in order to assist physicians in their decision about optimal treatment. We considered the utility of Killip class and other risk factors in the prediction of cardiac death after a MI. Methods: One hundred and eighty two patients with myocardial infarctions were studied over a one year period. Variables include historical factors, physical examination and noninvasive factors measured during hospitalization. All patients were selected in the Imam Khomeini hospital in Ilam City in Iran. Discriminant function and Logistic regression were used to analyze data. The percent of correct classification was compute using the Jack knife method. Results: The one month, 6 months, and one year mortality rate after MI was 25.8, 29.7, and 32.8 percent, respectively. The rate of mortality for women was 1.78 times higher than of the men (RR = 1.78, P-value = 0.02). The mean age was 62.45 year. Our results show that the mortality at 1 month and 6 months after MI had a significant relation with Killip class (P-value < 0.01). Discriminant function analysis shows that with knowledge of Killip class mortality and patient survival could be predicted with 88.1 percent accuracy up to 6 months. By adding age, cholesterol, and sex it could be increased to 91 percent. The results of logistic regression analysis revealed that there was a significant relation between mortality after MI and variables such a age, sex, cholesterol and the maximum blood urea nitrogen (BUN) level. Conclusion: Death and patient survival of up to one year after MI is predictable using an initial Killip class and other patient characteristics.