Background: Although the 12-lead electrocardiogram (ECG) has been found useful in identifying the left anterior descending (LAD) coronary artery as the infarct-related artery in acute myocardial infarction (MI), it has traditionally been felt to be incapable of localizing the culprit lesion within the LAD itself. Such a capability would be important, because anterior MI due to proximal LAD lesions carry a much worse prognosis than those due to more distal or branch vessel lesions. Hypothesis: This study investigated whether certain ECG variables-specially an ST-segment injury pattern in leads aVL and/or V1-would correlate with culprit lesion site, and an ECG algorithm was developed to predict culprit lesion site. Methods: The initial ECGs of 55 patients who had undergone cardiac catheterization after an anterior or lateral MI were reviewed to identify the leads with an ST-segment injury pattern; the corresponding catheterization films were then reviewed to identify the location of the culprit lesion; and these separate findings were then compared. Results: The sensitivity and specificity of an ST-injury pattern in aVL in predicting a culprit lesion before the first diagonal branch were 91 and 90%, respectively; the same values in predicting a lesion prior to the first septal branch were 85 and 78%. ST-segment elevation in V1, on the other hand, was a much less sensitive and specific predictor of a preseptal lesion. Overall, our algorithm correctly identified the culprit lesion location in 82% of our patients. Conclusion: Based on our findings, we conclude that an ST-segment injury pattern in aVL during an anterior myocardial infarction predominantly reflects a proximal LAD lesion and therefore constitutes a high-risk finding.
CITATION STYLE
Kim, T. Y., Alturk, N., Shaikh, N., Kelen, G., Salazar, M., & Grodman, R. (1999). An electrocardiographic algorithm for the prediction of the culprit lesion site in acute anterior myocardial infarction. Clinical Cardiology, 22(2), 77–84. https://doi.org/10.1002/clc.4960220205
Mendeley helps you to discover research relevant for your work.