Literature DB >> 9458421

Vectorcardiographic monitoring of patients with acute myocardial infarction and chronic bundle branch block.

P Eriksson1, K Andersen, K Swedberg, M Dellborg.   

Abstract

AIMS: This study was set up to describe vectorcardiographic patterns in patients with bundle-branch block and acute myocardial infarction. METHODS AND
RESULTS: Sixty-five patients admitted to the coronary care unit with bundle-branch block and suspected acute myocardial infarction were monitored by dynamic vectorcardiography with trend analysis. In 28 patients, a clinical diagnosis of acute myocardial infarction was made. In patients with left bundle-branch block and acute myocardial infarction, the pattern of QRS vector-difference evolution was similar to that in patients with the narrow QRS complex, while ST vector-magnitude changes increased over time. Using a cut-off value for QRS vector-difference at 12 h of more than 20 microVs and a specific trend curve pattern, acute myocardial infarction in the presence of left bundle-branch block could be diagnosed with an accuracy of 71%. For patients with right bundle branch block, using a maximum ST vector-magnitude of > 200 microV during the first 4 h, acute myocardial infarction could be diagnosed with a 78% accuracy.
CONCLUSION: Our results indicate that dynamic vectorcardiography is a valuable tool in diagnosing and monitoring acute myocardial infarction in patients with bundle branch block.

Entities:  

Mesh:

Year:  1997        PMID: 9458421     DOI: 10.1093/oxfordjournals.eurheartj.a015440

Source DB:  PubMed          Journal:  Eur Heart J        ISSN: 0195-668X            Impact factor:   29.983


  5 in total

1.  Continuous ST-segment monitoring of patients with right bundle branch block and suspicion of acute myocardial Infarction.

Authors:  Gunnar Gunnarsson; Peter Eriksson; Mikael Dellborg
Journal:  Ann Noninvasive Electrocardiol       Date:  2005-04       Impact factor: 1.468

2.  Frontal plane vectorcardiograms: theory and graphics visualization of cardiac health status.

Authors:  Dhanjoo N Ghista; U Rajendra Acharya; T Nagenthiran
Journal:  J Med Syst       Date:  2009-02-21       Impact factor: 4.460

3.  A new way of quantifying diagnostic information from multilead electrocardiogram for cardiac disease classification.

Authors:  R K Tripathy; L N Sharma; S Dandapat
Journal:  Healthc Technol Lett       Date:  2014-11-06

4.  Detection and classification of cardiac ischemia using vectorcardiogram signal via neural network.

Authors:  Ali Reza Mehri Dehnavi; Iman Farahabadi; Hossain Rabbani; Amin Farahabadi; Mohamad Parsa Mahjoob; Nasser Rajabi Dehnavi
Journal:  J Res Med Sci       Date:  2011-02       Impact factor: 1.852

5.  Discriminant analysis between myocardial infarction patients and healthy subjects using wavelet transformed signal averaged electrocardiogram and probabilistic neural network.

Authors:  Ahmad Keshtkar; Hadi Seyedarabi; Peyman Sheikhzadeh; Seyed Hossein Rasta
Journal:  J Med Signals Sens       Date:  2013-10
  5 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.