4.7 Article

A new method to quantify left ventricular mass by 2D echocardiography

期刊

SCIENTIFIC REPORTS
卷 12, 期 1, 页码 -

出版社

NATURE PORTFOLIO
DOI: 10.1038/s41598-022-13677-1

关键词

-

资金

  1. Augustinus Foundation [16-3012]
  2. Heart Center at Rigshospitalet, Denmark

向作者/读者索取更多资源

Increased left ventricular mass is a strong predictor for adverse cardiovascular events. Conventional echocardiographic methods have limited reproducibility and accuracy, while our novel method shows better reproducibility and accuracy.
Increased left ventricular mass (LVM) is a strong independent predictor for adverse cardiovascular events, but conventional echocardiographic methods are limited by poor reproducibility and accuracy. We developed a novel method based on adding the mean wall thickness from the parasternal short axis view, to the left ventricular end-diastolic volume acquired using the biplane model of discs. The participants (n = 85) had various left ventricular geometries and were assessed using echocardiography followed immediately by cardiac magnetic resonance, as reference. We compared our novel two-dimensional (2D) method to various conventional one-dimensional (1D) and other 2D methods as well as the three-dimensional (3D) method. Our novel method had better reproducibility in intra-examiner [coefficients of variation (CV) 9% vs. 11-14%] and inter-examiner analysis (CV 9% vs. 10-20%). Accuracy was similar to the 3D method (mean difference +/- 95% limits of agreement, CV): Novel: 2 +/- 50 g, 15% vs. 3D: 2 +/- 51 g, 16%; and better than the linear 1D method by Devereux (7 +/- 76 g, 23%). Our novel method is simple, has considerable better reproducibility and accuracy than conventional linear 1D methods, and similar accuracy as the 3D-method. As the biplane model forms part of the standard echocardiographic protocol, it does not require specific training and provides a supplement to the modern echocardiographic report.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.7
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据