The logistic regression model for identification patients at risk of arrhythmic events after acute myocardial infarction

Introduction: Identification of patients at risk of arrhythmic events (AE) after acute myocardial infarction (MI) in the era of modern therapy is difficult. The purpose of the present study was to develop apredictive model, based on logistic regression analysis, allowing successful identification of patients with previous MI at risk for AE during 24-month follow-up. Material and methods: Study population consisted of 248 consecutive survivors of acute MI. In enrolled patients, at 7-10 day after MI, 12 - lead ECG, signal averaged ECG, 24 - hour ambulatory ECG and 2 - dimensional echocardiography were performed to determine parameters of ventricular depolarization and repolarization, electrical instability, autonomic imbalance, left ventricular volume and function. Patients were followed for 24 months and all episodes of AE defined as occurrence of sudden death, sustained ventriculartachycardia or resuscitated ventricular fibrillation were recorded. The relation between single and various combinations of parameters presented as continuous variables and incidence of AE during 2-year follow-up was analysed using univariate and stepwise multivariate logistic regression analysis. Results: In univariate analysis the following continuous variables were statistically significant predictors of arrhythmic events: QRSd, LAS40, HR mean, SDNNi, rMSSD, LVEDV, LVESV and LVEF%. In multivariate analysis independent variables significantly associated with arrhythmic events were EDV and LAS40. Conclusions: The equation of logistic regression model with two parameters: LVEDV and LAS40 was found to be the most fittedto analysed group of patients after a cute myocardial infarction with 67% prediction capability of arrhythmic events during 24 months of follow-up. (Clin. Exp. Med. Lett. 2006; 47(4):241-246)

Keywords: arrhythmic events, Risk Factors