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rdf:type
lifeskim:mentions
pubmed:dateCreated
2007-10-23
pubmed:abstractText
We have developed a new method of detecting coronary artery stenoses that uses Doppler ultrasound to measure minute local vibrations in the cardiac wall associated with post-stenotic flow turbulence. In this paper, we present the results of a preliminary clinical study to evaluate the efficacy of this method for detecting coronary artery disease (CAD) using coronary angiography as the gold standard. The study population consisted of 34 patients clinically-indicated for coronary angiography. Based on the catheterization reports, the patients were divided into three categories: severe (obstructive CAD, typically with >70% diameter reduction), moderate (non-obstructive CAD, typically with <50% diameter reduction or diffuse atherosclerosis), and normal (no angiographic evidence of CAD). A diastolic myocardial vibration index (DMVI) was calculated as the ratio of the normalized periodogram spectral energy in the 100~800-Hz frequency band of the instantaneous wall velocity in early diastole to a baseline value during diastasis. The DMVI was significantly higher in severe CAD patients (21.2 +/- 3.2 dB) compared to moderate CAD (17.5 +/- 3.5 dB) and normal (11.2 +/- 4.8 dB). The differences between each of the categories were statistically significant (p<0.05). Severe CAD patients could be distinguished from normal with a sensitivity of 91.7% and specificity of 83.3%. We believe that this method could potentially be developed into a low-cost and accurate test for diagnosis and screening for coronary artery stenosis.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:issn
1557-170X
pubmed:author
pubmed:issnType
Print
pubmed:volume
1
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
863-6
pubmed:meshHeading
pubmed:year
2006
pubmed:articleTitle
Ultrasonic Doppler vibrometry: measurement of left ventricular wall vibrations associated with coronary artery disease.
pubmed:affiliation
Department of Bioengineering, University of Washington, Seattle, WA 98195, USA. ssikdar@u.washington.edu
pubmed:publicationType
Journal Article