Evaluation of Prognostic Clinical and ECG Parameters in Patients after Myocardial Infarction By Applying Logistic Regression Method

Noninvasive risk stratification of patients who have suffered myocardial infarction (MI) is one of the greatest challenges in today's cardiology. No single test has sufficient predictive ability. Therefore, a combination of the tests must be applied for better post-MI risk stratification. The purpose of this study was to assess noninvasive predictors of 2 years cardiac mortality in post-MI patients and create a stratification model for identification of high-risk patients. Clinical, electrocardiographic, and echocardiographic parameters were evaluated before hospital discharge in 180 survivors of acute MI (mean age 57.0 +/- 9.9, male 82.2%), followed up for 2 years. A multivariate logistic regression analysis was used to determine informative predictors of cardiac mortality. A clinical score was constructed using the regression coefficient from the multivariate model. During follow-up, 16 deaths (8.8%) occurred. Multivariate analysis identified a combination of six variables that showed the strongest association with cardiac mortality. Based on the coefficients of the logistic regression, six variables were used to create a scoring system: filtered QRS duration (QRSd) >114 ms, coefficient of variation (Cv) or=445 ms, left ventricular ejection fraction (LVEF) <or=30%, the absence of use of beta-blockers (BB), and absence of treatment by primary percutaneous coronary intervention (PCI). The cutoff value for the score of 10 or higher, defined the "high-risk" patients with a sensitivity of 75% and a specificity of 70%. The mortality rate for the low- and high-risk groups were 12.5% and 87.5%, respectively. The receiver operating characteristic (ROC) analysis yielded area under curve of 0.88. The proposed scoring system could be valuable in predicting 2 years risk of cardiac mortality in post-MI population and allows the stratification of patients into low- and high-risk groups.