医学
再现性
超声学家
核医学
超声波
射血分数
斑点图案
心脏病学
统计
放射科
人工智能
数学
计算机科学
心力衰竭
作者
Konstantinos Farsalinos,Ana Maria Daraban,Serkan Ünlü,James D. Thomas,Luigi P. Badano,Jens‐Uwe Voigt
标识
DOI:10.1016/j.echo.2015.06.011
摘要
This study was planned by the EACVI/ASE/Industry Task Force to Standardize Deformation Imaging to (1) test the variability of speckle-tracking global longitudinal strain (GLS) measurements among different vendors and (2) compare GLS measurement variability with conventional echocardiographic parameters.Sixty-two volunteers were studied using ultrasound systems from seven manufacturers. Each volunteer was examined by the same sonographer on all machines. Inter- and intraobserver variability was determined in a true test-retest setting. Conventional echocardiographic parameters were acquired for comparison. Using the software packages of the respective manufacturer and of two software-only vendors, endocardial GLS was measured because it was the only GLS parameter that could be provided by all manufactures. We compared GLSAV (the average from the three apical views) and GLS4CH (measured in the four-chamber view) measurements among vendors and with the conventional echocardiographic parameters.Absolute values of GLSAV ranged from 18.0% to 21.5%, while GLS4CH ranged from 17.9% to 21.4%. The absolute difference between vendors for GLSAV was up to 3.7% strain units (P < .001). The interobserver relative mean errors were 5.4% to 8.6% for GLSAV and 6.2% to 11.0% for GLS4CH, while the intraobserver relative mean errors were 4.9% to 7.3% and 7.2% to 11.3%, respectively. These errors were lower than for left ventricular ejection fraction and most other conventional echocardiographic parameters.Reproducibility of GLS measurements was good and in many cases superior to conventional echocardiographic measurements. The small but statistically significant variation among vendors should be considered in performing serial studies and reflects a reference point for ongoing standardization efforts.
科研通智能强力驱动
Strongly Powered by AbleSci AI