Optimization of the method of measuring left ventricular end-diastolic diameter in cardiac magnetic resonance as a predictor of left ventricular enlargement

SCIENTIFIC REPORTS(2022)

Cited 0|Views2
No score
Abstract
The objective of the study was to optimize the method of measuring left ventricular end-diastolic diameter (LVEDD) in cardiac magnetic resonance (CMR) as a predictor of left ventricular end-diastolic volume (LVEDV). The study group consisted of 78 patients (age 55.28 ± 17.18) who underwent 1.5 T CMR examination. LVEDD measurements in the short axis, in the long axis in the 2-chamber, 3-chamber and 4-chamber views were made by 2 radiologists. The repeatability of LVEDD measurements was assessed. The sensitivity and specificity of various methods of measuring LVEDD as a predictor of left ventricular enlargement (diagnosed based on LVEDV) were assessed. The correlation coefficients between LVEDD measurements made by researcher A and B were 0.98 for the long axis measurements in the 2-chamber and 3-chamber view, and 0.99 for measurements made in the short axis and in the long axis in the 4-chamber view. The lowest LVEDD measurements variability was recorded for the short axis measurements (RD 0.02, CV 1.38%), and the highest for the long axis measurements in the 3-chamber view (RD 0.04, CV 2.53%). In the male subgroup, the highest accuracy in predicting left ventricular enlargement was characterized by the criterion “LVEDD measured in the long axis in the 2-chamber view > 68.0 mm” (accuracy 94.1%). In the female subgroup, the highest accuracy in predicting left ventricular enlargement was achieved by the criterion “LVEDD measured in the short axis > 63.5 mm” (96.3%). In summary, the measurement made in the short axis should be considered the optimal method to LVEDD measure in CMR, considering the repeatability of measurements and the accuracy of left ventricular enlargement prediction.
More
Translated text
Key words
Anatomy,Biomarkers,Cardiology,Diseases,Medical research,Pathogenesis,Signs and symptoms,Science,Humanities and Social Sciences,multidisciplinary
AI Read Science
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Chat Paper
Summary is being generated by the instructions you defined