Identification of first acute Q wave and non-Q wave myocardial infarction by multivariate analysis of body surface potential maps.

BACKGROUND

Patients with acute non-Q wave myocardial infarction (NQMI) appear to have more jeopardized residual myocardium at high risk for subsequent angina, reinfarction, or malignant arrhythmias than patients with acute Q wave myocardial infarction (QMI). Unfortunately, conventional electrocardiographic (ECG) criteria have limited utility in recognizing NQMI.

METHODS AND RESULTS

The present study combines the increased information content of body surface potential maps (BSPM) over the 12-lead ECG with the power of multivariate statistical procedures to identify a practical subset of leads that would allow improved diagnosis of NQMI. Discriminant analysis was performed on 120-lead data recorded simultaneously in 159 normal subjects and 308 patients with various types of myocardial infarction (MI) by using instantaneous voltages on time-normalized P, PR, QRS, and ST-T waveforms as well as the duration of these waveforms as features. Leads and features for optimal separation of 159 normals from 183 patients with recent or old QMI (group A) were selected. A total of six features from six torso sites accounted for a specificity of 96% and a sensitivity of 94%. All lead positions were outside the conventional electrode sites and selected features were voltages at mid-P, early and mid-QRS, and before and after the peak of the T wave. The discriminant function was then tested on 57 patients with acute NQMI (group B) and 68 patients with acute QMI (group C): Rates of correct classification were 91% and 93%, respectively. Because of the possible deterioration of the results caused by ST-T abnormalities also present in other clinical entities, a second classification model including an independent group of 116 patients with left ventricular hypertrophy (LVH) but without MI was developed. Two additional measurements were required, namely, P wave duration and a mid-QRS voltage on a lead located 10 cm below V1. Testing the model on both acute MI groups produced correct classification rates of 88% for acute NQMI and 93% for acute QMI. Group mean BSPM were plotted for the three MI groups at successive instants throughout the PQRST waveform. Typical patterns for each MI group were identified during PQRST by removing the corresponding normal variability at each electrode site from sequential MI maps. These standardized maps or discriminant maps provided information on the capability of each measurement at each electrode site and at each instant to separate each class of MI from the normal group (N). Striking similarities were observed between the three MI groups, particularly at mid-QRS and throughout ST-T. The closest resemblance was between acute NQMI and old QMI. Discriminant analysis was also performed on the 12-lead ECG: The first classification model (N versus MI) produced correct classification rates of 85% for acute QMI and 70% for NQMI. With the second model (MI versus N or LVH), correct rates were 81% and 65%, respectively.

CONCLUSIONS

Diagnosis of acute NQMI and QMI (also in the presence of LVH) can be improved substantially by appropriate selection of ECG leads and features. Comparison of discriminant maps from groups A, B, and C does not support the concept of acute NQMI as a distinct ECG entity but rather as a group with infarcts of smaller size. However, pathophysiological and clinical differences between acute NQMI and acute QMI influence long-term risks and may define different therapeutic approaches.