The added diagnostic value of automated QT-dispersion measurements and automated ST-segment deviations in the electrocardiographic diagnosis of acute cardiac ischemia.

The purpose of this study was to determine the added value of automated QT dispersion and ST-segment measurements to physician interpretation of 12-lead electrocardiograms (ECGs) in patients with chest pain. To date, poor reproducibility of manual measurements and lack of shown added value have limited the clinical use of QT dispersion. Twelve-lead ECGs (n = 1,161) from the Milwaukee Prehospital Chest Pain Database were independently classified by 2 physicians into 3 groups (acute myocardial infarction (AMI), acute cardiac ischemia (ACI), or nonischemic), and their consensus was obtained. QT-end and QT-peak dispersions were measured by a computerized system. The computer also identified ST-segment deviations. Sensitivity, specificity, and positive predictive values (PPVs) and negative predictive values (NPV) for AMI and ACI were evaluated independently and in combinations. For AMI, physicians' consensus classification was remarkably good (sensitivity, 48%, specificity, 99%). Independent classification by QT-end and QT-peak dispersions or ST deviations was not superior to the physicians' consensus. Optimal classification occurred by combining automated QT-end dispersion and ST deviations with physicians' consensus. This combination increased sensitivity for the diagnoses of AMI by 35% (65% vs 48%, P < .001) and ACI by 55% (62% vs 40%, P < .001) compared with physicians' consensus, while maintaining comparable specificity. This study supports a potential clinical role for automated QT dispersion when combined with other diagnostic methods for detecting AMI and ACI.