BACKGROUND: Three-dimensional echocardiographic (3DE) analysis provides better measurements of left ventricular (LV) volumes, ejection fraction, myocardial deformation, and dyssynchrony. Many studies have shown that this technique has high intrainstitutional reproducibility. However, interinstitutional reproducibility is low, limiting its adoption. The aim of this study was to determine if standardization of training could reduce the interinstitutional variability in 3DE data analysis. METHODS: In total, 50 full-volume, transthoracic 3DE data sets of the left ventricle were analyzed by two readers. Measurements obtained included LV volumes, ejection fraction, global longitudinal strain, and two dyssynchrony indices. The cases represented a wide spectrum of ejection fraction. After initial analysis of 21 studies, readers formally met to standardize their analytic approach on six additional cases. Five months after the intervention, 23 new cases were analyzed. Paired t tests were performed to identify systematic institutional differences in measurements. Interinstitutional variability was quantified using intraclass correlation coefficients and variability. RESULTS: Before the intervention, there was a systematic bias in LV volumes, which was eliminated after intervention. Intraclass correlation coefficients showed that the intervention improved agreement in measurements of LV volumes, strain, and dyssynchrony between the two centers and decreased variability. CONCLUSIONS: A simple intervention to standardize analysis can reduce interinstitutional variability of measurements obtained from 3DE analysis. This intervention is needed before the use of 3DE measurement in multicenter trials and to increase the reproducibility of such measurements in routine clinical practice.
BACKGROUND: Three-dimensional echocardiographic (3DE) analysis provides better measurements of left ventricular (LV) volumes, ejection fraction, myocardial deformation, and dyssynchrony. Many studies have shown that this technique has high intrainstitutional reproducibility. However, interinstitutional reproducibility is low, limiting its adoption. The aim of this study was to determine if standardization of training could reduce the interinstitutional variability in 3DE data analysis. METHODS: In total, 50 full-volume, transthoracic 3DE data sets of the left ventricle were analyzed by two readers. Measurements obtained included LV volumes, ejection fraction, global longitudinal strain, and two dyssynchrony indices. The cases represented a wide spectrum of ejection fraction. After initial analysis of 21 studies, readers formally met to standardize their analytic approach on six additional cases. Five months after the intervention, 23 new cases were analyzed. Paired t tests were performed to identify systematic institutional differences in measurements. Interinstitutional variability was quantified using intraclass correlation coefficients and variability. RESULTS: Before the intervention, there was a systematic bias in LV volumes, which was eliminated after intervention. Intraclass correlation coefficients showed that the intervention improved agreement in measurements of LV volumes, strain, and dyssynchrony between the two centers and decreased variability. CONCLUSIONS: A simple intervention to standardize analysis can reduce interinstitutional variability of measurements obtained from 3DE analysis. This intervention is needed before the use of 3DE measurement in multicenter trials and to increase the reproducibility of such measurements in routine clinical practice.
Authors: Juan Carlos Plana; Maurizio Galderisi; Ana Barac; Michael S Ewer; Bonnie Ky; Marielle Scherrer-Crosbie; Javier Ganame; Igal A Sebag; Deborah A Agler; Luigi P Badano; Jose Banchs; Daniela Cardinale; Joseph Carver; Manuel Cerqueira; Jeanne M DeCara; Thor Edvardsen; Scott D Flamm; Thomas Force; Brian P Griffin; Guy Jerusalem; Jennifer E Liu; Andreia Magalhães; Thomas Marwick; Liza Y Sanchez; Rosa Sicari; Hector R Villarraga; Patrizio Lancellotti Journal: Eur Heart J Cardiovasc Imaging Date: 2014-10 Impact factor: 6.875
Authors: M M P Driessen; E Kort; M J M Cramer; P A Doevendans; M J Angevaare; T Leiner; F J Meijboom; S A J Chamuleau; G Tj Sieswerda Journal: Neth Heart J Date: 2014-09 Impact factor: 2.380
Authors: Lamia Al Saikhan; Muath Alobaida; Anish Bhuva; Nish Chaturvedi; John Heasman; Alun D Hughes; Siana Jones; Sophie Eastwood; Charlotte Manisty; Katherine March; Arjun K Ghosh; Jamil Mayet; Ayodipupo Oguntade; Therese Tillin; Suzanne Williams; Andrew Wright; Chloe Park Journal: Front Cardiovasc Med Date: 2020-11-13