Body-surface map models for early diagnosis of acute myocardial infarction

The standard 12-lead ECG is only 50% sensitive for the detection of acute myocardial infarction (AMI). The majority of leads for optimal classification of AMI probably lie outside the area covered by the 6 precordial leads. Thus, body-surface mapping (BSM) may be more helpful, as a larger thoracic area is sampled. We recorded 64-lead anterior BSMs in 635 patients with chest pain suggestive of AMI and abnormal electrocardiograms (ECGs), and 125 controls without chest pain. Of the 635 patients, 325 had AMI according to World Health Organization (WHO) criteria (203 presenting with ST segment elevation, and 122 with nondiagnostic ECG), and 310 had an "abnormal ECG but not AMI." QRS and ST-T isointegrals and variables describing map shape were derived. Subjects were randomly allocated to a training set (63 controls, 321 patients) and a validation set (62 controls, 314 patients). Multiple logistic regression was used in the training set to identify which variables gave best discrimination between groups. A model with these variables was then tested prospectively in the validation set. In stage 1 (all subjects), controls were compared with patients. In the training set, a model containing 21 variables classified 58/63 controls (specificity 92%) and 316/321 patients (sensitivity 98%). In the validation set, the model classified 48/62 controls (specificity 77.4%) and 302/314 patients (sensitivity 96%). In stage 2 (studying patients only), patients with AMI were compared with patients who had an abnormal ECG-not AMI. In the training set, a model containing 28 variables classified 132/165 patients (sensitivity 80%) with AMI and 134/156 patients (specificity 86%) with an abnormal ECG-not AMI. In the validation set, the model classified 123/160 patients (sensitivity 77%) with AMI and 131/154 patients (specificity 85%) with an abnormal ECG-not AMI. Combining results of both stages in a two-step algorithm gave an overall classification in the training set of controls 92%, abnormal ECG-not AMI 84%, AMI 80%, and in the validation set of controls 77%, abnormal ECG-not AMI 82%, AMI 74%. Thus, in conclusion, when compared with the 12-lead ECG, BSM models results in higher sensitivity and specificity for detection of AMI, particularly in patients presenting with chest pain and nondiagnostic ECG changes. The use of BSM models in such patients, may lead to the earlier detection of AMI and appropriate administration of fibrinolytic therapy and/or anti-platelet agents.