Comparative Study
Journal Article
Add like
Add dislike
Add to saved papers

An electrocardiographic algorithm for the prediction of the culprit lesion site in acute anterior myocardial infarction.

Clinical Cardiology 1999 Februrary
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--especially 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 a ST-segment injury pattern in aVL during an anterior myocardial infarction predominantly reflects a proximal LAD lesion and therefore constitutes a high-risk finding.

Full text links

We have located links that may give you full text access.
Can't access the paper?
Try logging in through your university/institutional subscription. For a smoother one-click institutional access experience, please use our mobile app.

Related Resources

For the best experience, use the Read mobile app

Mobile app image

Get seemless 1-tap access through your institution/university

For the best experience, use the Read mobile app

All material on this website is protected by copyright, Copyright © 1994-2024 by WebMD LLC.
This website also contains material copyrighted by 3rd parties.

By using this service, you agree to our terms of use and privacy policy.

Your Privacy Choices Toggle icon

You can now claim free CME credits for this literature searchClaim now

Get seemless 1-tap access through your institution/university

For the best experience, use the Read mobile app