Usefulness of serial electrocardiograms for diagnosis of acute myocardial infarction

The purpose of this study was to determine whether the automated detection of acute myocardial infarction (AMI) by utilizing artificial neural network s was improved by using a previous electrocardiogram (ECG) in addition to t he current ECG. A total of 4,691 ECGs were recorded from patients admitted to an emergency department due to suspected AMI. Of these, 902 ECGs, in whi ch diagnoses of AMI were later confirmed, formed the study group, whereas t he remaining 3,789 ECGS comprised the control group. For each ECG recorded, a previous ECG of the same patient was selected from the clinical electroc ardiographic database. Artificial neural networks were then programed to de tect AMI based on either the current ECG only or on the combination of the previous and the current ECGs. On this basis, 3 assessors-a neural network, an experienced cardiologist, and an intern-separately classified the ECGs of the test group, with and without access to the previous ECG. The detecti on performance, as measured by the area under the receiver operating charac teristic curve, showed an increase for all assessors with access to previou s ECGs. The neural network improved from 0.85 to 0.88 (p = 0.02), the cardi ologist from 0.79 to 0.81 (p = 0.36), and the intern from 0.71 to 0.78 (p < 0.001). Thus, the performance of a neural network, detecting AMI in an ECG, is improved when a previous ECG is used as an additional input. (C) 2001 b y Excerpta Medica, Inc.