Noninvasive prediction of sudden death and sustained ventricular tachycardia after acute myocardial infarction using a neural network algorithm.

BACKGROUND

The early and accurate noninvasive identification of postinfarction patients at risk of sudden death and sustained ventricular tachycardia (arrhythmic events) still remains an unsolved problem. The aim of the present study was to identify the combination of clinical and laboratory noninvasive variables, easy to obtain in most patients, that best predicts the occurrence of arrhythmic events after an acute myocardial infarction.

METHODS

Four hundred and four consecutive patients with acute myocardial infarction were enrolled and followed for a median period of 21.4 months. In each patient, 61 clinical and laboratory noninvasive variables were collected before hospital discharge and used for the prediction of arrhythmic events using an artificial neural network.

RESULTS

During follow-up, 13 (3.2%) patients died suddenly and 11(2.5%) had sustained ventricular tachycardia. The neural network showed that the combination best predicting arrhythmic events included: left ventricular failure during coronary care stay, ventricular dyskinesis, late potentials, number of ventricular premature depolarizations/hour, nonsustained ventricular tachycardia, left ventricular ejection fraction, bundle branch block and digoxin therapy at discharge. The neural network algorithm allowed identification of a small high-risk patient subgroup (12% of the study population) with an arrhythmic event rate of 46%. The sensitivity and specificity of the test were 96 and 93% respectively.

CONCLUSIONS

These results suggest that, in postinfarction patients, it is possible to predict early and accurately arrhythmic events by noninvasive variables easily obtainable in most patients. Patients identified as being at risk are candidates for prophylactic antiarrhythmic therapy.