Selection of dichotomy limits for multifactorial prediction of arrhythmic events and mortality in survivors of acute myocardial infarction.

AIMS To evaluate the predictive value and optimum dichotomy limits for different combinations of prognostic indicators for the prediction of arrhythmic events and cardiac mortality in post-infarction patients. BACKGROUND Studies of new interventions based on risk stratification after myocardial infarction have often used a single variable as a predictor of risk. However, whether the dichotomy limits of these single variables, derived from univariate analyses, should be altered when such variables are combined for the prediction of risk after myocardial infarction has not been examined. METHODS Left ventricular ejection fraction, signal-averaged electrocardiography, heart rate variability index, mean heart rate and ventricular extrasystole frequency were recorded pre-discharge in 439 survivors of their first myocardial infarction. Arrhythmic events and cardiac mortality were recorded during 1 year (range 1-6 years) follow-up. RESULTS During follow-up for at least 1 year, there were 25 cardiac deaths and 23 arrhythmic events. Different optimum dichotomy limits were obtained for the prediction of cardiac mortality vs arrhythmic events, for different combinations of variables, for different selected levels of sensitivity and for different numbers of variables abnormal before identification of those at risk. The dichotomy limit of the heart rate variability index for the prediction of events appeared to be the least affected by the inclusion of other variables. For example, when predicting arrhythmic events using combinations of left ventricular ejection fraction and/or heart rate variability, the optimum dichotomy limits when each variable was used alone was 32% and 18 units respectively; 43% and 18 units when either left ventricular ejection fraction or heart rate variability are required to be abnormal, and 52% and 19 units when both are required to be abnormal before identification of those at risk of arrhythmic events. CONCLUSIONS Dichotomy limits derived from univariate analyses do not optimally predict events when used in the multivariate setting. Risk stratification can be improved by using several variables in combination and is further improved by using dichotomy limits of these variables which are different from those used in or derived from univariate analyses.