Left atrial strain reproducibility using vendor-dependent and vendor-independent software

Adult patients receiving echocardiography at the clinics of the Adult Echocardiography Lab of Guangdong Provincial People’s Hospital from December 2016 to September 2017 were recruited for the study. This study was approved by Research Ethics Committee of Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences. Inclusion criteria were: (1) older than 18 years; (2) sinus rhythm at examination; (3) agreed to participate in the study and provided written informed consent. Exclusion criteria were: (1) onset of atrial fibrillation during the examination; (2) valvular heart disease (moderate or severe heart valve stenosis or valve replacement); (3) implantation of a pacemaker or defibrillator; (4) poor image quality; (5) did not provide informed consent. Clinical data including a history of hypertension, diabetes mellitus, dyslipidemia, and smoking were collected trained research staff at the time of the first hospital admission.

We identified 99 participants. Of these, 35 subjects were excluded from analysis for not providing consent (n = 5), sinus rhythm turning into atrial fibrillation (n = 7) or inadequate imaging quality due to acquisition with unclear LA endocardium (n = 12), LA foreshortening (n = 11). The final study population consisted of 64 individuals (male/female, 32/32; mean age = 44.1 ± 16.1 years).

Echocardiographic studies were performed using a GE Vivid 9 (GE Vingmed Ultrasound, Horten, Norway) echocardiograph system. Examinations were performed with subjects in the left lateral recumbent position. Apical 4- and 2-chamber views were obtained using conventional 2D gray scale echocardiography with an M5S probe (2 ~ 4 MHz), using a frame-rate of 40–71 frames/s, in accordance with current American Society of Echocardiography recommendations [20]. Both apical views used should be optimized in terms of orientation, depth, and gain to avoid LA foreshortening and to visualize the entire LA throughout the cardiac cycle. Five cardiac cycles of each plane were stored in cine loop format in order to subsequently select the images of better quality for off-line speckle-tracking analysis.

Offline analysis of images was performed using EchoPAC version 201 (GE Vingmed Ultrasound) (VSS_LV) software, and Image Arena 2D Cardiac Performance Analysis version 4.6 (TomTec Imaging Systems, Unterschleissheim, Germany) (VIS_LA and VIS_LV) software, yielding 3 strain analysis sets for each examination (Fig. 1).

From the parasternal long-axis view, LV end-diastolic diameter and LV end-systolic diameter were obtained at the level of the mitral valve tips by M-mode Doppler ultrasonography. Left ventricle ejection fraction (LVEF) was calculated automatically by the GE Vivid 9 system. From the apical 4- and 2-chamber positions, maximum LA volume (LAV_max,; measured on the 2D frame just before mitral valve opening), LA pre-atrial contraction volume (LAV_preA; measured on the frame just before the onset of atrial emptying), and LA minimum volume (LAV_min,; measured on the frame at end-diastole with the smallest LA volume) were computed separately following American Society of Echocardiography guidelines, and using the biplane modified Simpson’s method of discs. The indices and formulas calculated from the volumes were as follows. Total LA stroke volume = LAV_max − LAV_min. Active LA stroke volume = LAV_preA − LAV_min. Passive LA stroke volume = LAV_max − LAV_preA. Active LA emptying fraction = (active LA stroke volume / LAV_preA) × 100%. Passive LA emptying fraction = (passive LA stroke volume / LAV_max) × 100%. The LA expansion index = (total LA stroke volume / LAV_min) × 100%. Pulsed wave Doppler at the apical position was used to obtain mitral inflow velocity between the tips of the mitral leaflets. E/e′ was calculated as early mitral inflow velocity (E) divided by the average of septal and lateral mitral annular peak early diastolic velocity (e′) obtained by pulsed wave tissue Doppler imaging (TDI).

Speckle-tracking analysis was performed by the software: VSS_LV, VIS_LA, and VIS_LV. VSS_LV analyzed the image derived from raw data, while VIS_LV and VIS_LA analyzed the image from compressed data.

According to the consensus document of the EACVI/ASE/Industry Task Force recommendation [21], the LA was traced as followings: starting tracing the LA endocardial border at the endocardial border of the mitral annulus, up to the opposite mitral annulus side, while carefully excluding the pulmonary veins and LA appendage orifices. The apical two-chamber view was also analysed to obtain a biplane calculation of the LA strain.

The endocardial border was traced manually or was user defined. The user could review the tracking path and manually adjusted it after running. The adjustable numbers of images were 1/2 of the frame frequency with VIS_LA and VIS_LV, and 1 frame with VSS_LV. With VSS_LV and VIS_LV, the tracking location marker was placed at the end of QRS wave. With VIS_LA, the marker was placed at the beginning of the P wave. With VIS_LV and VIS_LA, the amplitude of the ECG cannot be modulated.

A region of interest (ROI) was selected in the endocardial mode of VSS_LV and VIS_LV, while it was defaulted to include the endocardium with VIS_LA. VSS_LV uses full thickness ROI including endocardium and epicardium, the position and size of which can be adjusted. And the ROI tracing line of VIS is single, which can not be adjusted.

The software algorithms automatically performed speckle-tracking on a frame-to-frame basis.

Exclusion of the individual studies was done upon visual assessment, when abnormal curves were believed to be artefactual. Strain values were obtained from the apical 4- and apical 2-chamber views.

Intra-observer and inter-observer reproducibility of LA strain values were analyzed with repeated measurements by the same observer at 2 different time points, and by a second independent different observer. All observers were blinded to the results of the other software package and previous strain results when assessing reproducibility.

Statistical analysis was performed using SPSS version 20 (IBM, Armonk, New York) and Empower (R) (www.​empowerstats.​com, X&Y Solutions, Inc., Boston MA) and R (http://​www.​R-project.​org). All measurements were tested for distribution normality with the Kolmogorov–Smirnov test. Continuous variables were expressed as mean ± standard deviation (SD). Categorical variables were reported as percentages. Variability values were expressed by the coefficient of variation (CV), defined as CV = S/X × 100%, where S is the standard deviation and X is the mean value. Differences between groups were analyzed for statistical significance with the unpaired t-test or Mann-Whitney U test, as appropriate. Agreement between the 3 speckle-tracking methods was assessed by Bland–Altman analysis. The bias (mean difference) and the 95% limits of agreement (2 SDs around the mean difference) between the measurements derived from each system were calculated. The reliability for inter-software, intra-observer, and inter-observer measurements was evaluated by intra-class correlation coefficient (ICC) [22]. An ICC of ≥0.90 was considered excellent reliability, an ICC of ≥0.70 - < 0.90 was considered good reliability, an ICC of ≥0.50 - < 0.70 was considered moderate reliability, an ICC of ≥0.30 - < 0.50 was considered poor reliability, and an ICC of > 0.30 was considered very poor reliability. Pearson’s correlation coefficient and the point-biserial correlation coefficient were used to assess the correlation between strain value and baseline clinical characterizes. The Z-test (after transformation) was used to test the difference of correlation coefficient among the 3 software. All statistical tests are 2-sided, and a p-value < 0.05 is considered statistically significant.

Strain analysis was obtained in all 64 subjects. Representative examples of strain measured by the 3 systems are shown in Table 2. When using VSS_LV, there were significant differences in intra-observer measurement of Sct and Sr, and inter-observer measurement of Sct (p < 0.05). Comparing VIS_LV and VIS_LA, there were no significant differences in intra-observer or inter-observer measurements (p > 0.05).

Strain measurements between VSS_LV and VIS (VIS_LV and VIS_LA) demonstrated considerable variability in Sct, Scd and Sr as assessed by their CoV, as shown in Table 3, the CoV for measurements between VSS_LV and VIS (VIS_LA and VIS_LV) ranged from 0.04 to 31.16% (Sct), − 1.94 to 38.21%(Scd) and − 1.29 to 36.40%(Sr).

The comparability of strain measurements showed good agreement within VIS (VIS_LA and VIS_LV), with a smaller mean differences, which were 0.02 (Sct), 0.76 (Scd), and − 0.50 (Sr), and a narrower limits of agreement ranging from − 5.42 to 7.05. Between VSS_LV and VIS_LV, mean differences were 3.51 (Sct), 6.41 (Scd), and − 10.20(Sr), with limits of agreement ranging from 16.99 to − 21.30. Between VSS_LV and VIS_LA, the mean differences were 3.53 (Sct), 7.17 (Scd), and − 10.70 (Sr), with limits of agreement ranging from 17.69 to − 23.03. Figure 2 shows the Bland-Altman Scatter plots.

In Table 4, the intra-class correlation coefficients revealed excellent reliability for VIS, with the correlation coefficients generally > 0.9.

Overall, there were fewer differences between strain values from VIS compared with strain values from VSS derived from same images.

As shown in Table 5, the inter-observer and intra-observer variability (mean and range) of the measurements derived from VSS_LV were 2.35% (1.52–3.30%) and 2.67% (1.95–3.49%), respectively. The differences of inter-observer measurement of Sct and Sr were statistically significant (p < 0.05), and the intra-observer difference of measurement of Sct was significant (p < 0.05). The inter-observer and intra-observer variability (mean and range) of VIS_LV were 1.39% (0.71–2.31%) and 3.87% (3.67–4.10%), respectively. Both intra-observer and inter-observer variability were not significantly different (p = 0.01).

For VIS_LA, the inter-observer and intra-observer variability (mean and range) were 0.55% (0.05–1.18%) and 1.62% (0.85–2.68%), respectively, and there were no significant differences (p > 0.05).

The results of the analysis of bias and limits of agreement for the same software are shown in Table 6. The bias for value measurement by VIS_LA was smaller than that by VSS_LV and VIS_LV.

The inter-observer and intra-observer evaluation results of the same software for intra-class correlation coefficient are shown in Table 7. The inter-observer ICC values were VIS_LA > VSS_LV = VIS_LV; for intra-observer ICC, VSS_LV > VIS_LA > VIS_LV.

The study was not designed to assess the accuracy of LA strain measurements as there was no comparison to gold standard. This study was focused on determining the reproducibility of LA strain measurements among different vendors.

We used small numbers of subjects to assess consistency among 3 vendors. However there are no data showing a variability between different ultrasound software packages regarding LA analyses, and the use of a larger number of subjects may help clarify this issue.

Although we included patients with AF, they were in sinus rhythm during analyzing and further studies are needed to assess use of LA strain in patients who are not in sinus rhythm.

We excluded the patients with poor image quality. However it is important for STE analyzing, prospective image acquisition with a focus on LA optimisation would benefit further LA strain study.