An improved automated ECG algorithm for detecting acute and prior myocardial infarction.
Commercial electrocardiographic (ECG) algorithms for detection of prior myocardial infarction (MI) predominantly rely on QRS criteria and on established qualitative ST and T changes. The qualitative approach to ST-T changes in Cardiovise 2.5 is improved upon in version 3.0 with two distinct new approaches for quantifying ST and T changes to assist with the detection of prior MI. The first method uses the mean axes of vectorcardiographic T-loops taken from the inverse Dower transform of the 12-lead ECG to indicate ischemic regions of the left ventricular wall. The second method establishes regional scores for residual ST elevation supportive of ischemia or infarction. These 2 ST-T measures qualify borderline QRS infarct criteria, resulting in composite criteria having higher sensitivities and specificities than QRS criteria alone. Comparative results were created by studying an MI positive group of 360 patients with biochemical marker evidence for infarction and an MI negative group of 515 patients negative for risk factors, biochemical markers and/or coronary disease or wall motion abnormality. Three automated ECG methods that rely predominantly on QRS markers for infarction are compared to Cardiovise (CV) prior MI algorithm version 3.0: Cardiovise 3.0 also introduces criteria for detection of acute myocardial infarction (AMI). Two automated ECG methods are compared to Cardiovise for the detection and labeling of ST elevation (STE) AMI, and for detection of non-STE AMI.
Andresen
Alan
A
Inovise Medical, Inc, Newberg, OR 97132, USA.
Gasperina
Marco Dalla
MD
Myers
Richard
R
Wagner
Galen S
GS
Warner
Robert A
RA
Selvester
Ronald H S
RH
D000465:Algorithms; D004562:Electrocardiography / Q000379:methods*; D006801:Humans; D009203:Myocardial Infarction / Q000175:diagnosis*; D012680:Sensitivity and Specificity