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 m yocardial infarction (AMI). The majority of leads for optimal classificatio n of AMI probably lie outside the area covered by the 6 precordial leads. T hus, body-surface mapping (BSM) may be more helpful, as a larger thoracic a rea is sampled. We recorded 64-lead anterior BSMs in 635 patients with ches t pain suggestive of AMI and abnormal electrocardiograms (ECGs), and 125 co ntrols without chest pain. Of the 635 patients, 325 had AMI according to Wo rld Health Organization (WHO) criteria (203 presenting with ST segment elev ation, and 122 with nondiagnostic EGG), and 310 had an "abnormal ECG but no t AMI." QRS and ST-T isointegrals and variables describing map shape were d erived. Subjects were randomly allocated to a training set (63 controls, 32 1 patients) and a validation set (62 controls, 314 patients). Multiple logi stic regression was used in the training set to identify which variables ga ve best discrimination between groups. A model with these variables was the n tested prospectively in the validation set. In stage 1 (all subjects), co ntrols were compared with patients. In the training set, a model containing 21 variables classified 58/63 controls (specificity 92%) and 316/321 patie nts (sensitivity 98%). In the validation set, the model classified 48/62 co ntrols (specificity 77.4%) and 302/314 patients (sensitivity 96%). In stage 2 (studying patients only), patients with AMT were compared with patients who had an abnormal EGG-not AMI. In the training set, a model containing 28 variables classified 132/165 patients (sensitivity 80%) with AMI and 134/1 56 patients (specificity 86%) with an abnormal EGG-not AMI. In the validati on set, the model classified 123/160 patients (sensitivity 77%) with AMI an d 131/154 patients (specificity 85%) with an abnormal EGG-not AMI. Combinin g results of both stages in a two-step algorithm gave an overall classifica tion in the training set of controls 92%, abnormal EGG-not AMI 84%, AMI 80% , and in the validation set of controls 77%, abnormal EGG-not AMI 82%, AMI 74%. Thus, in conclusion, when compared with the 12-lead EGG, BSM models re sults in higher sensitivity and specificity for detection of AMI, particula rly in patients presenting with chest pain and nondiagnostic ECG changes. T he use of BSM models in such patients, may lead to the earlier detection of AMI and appropriate administration of fibrinolytic therapy and/or anti-pla telet agents.