Variability among strain variables derived from two-dimensional speckle tracking echocardiography in dogs by use of various software

Giorgia Santarelli 1Department of Animal Medicine and Surgery, Veterinary Faculty, University of Murcia, Campus Espinardo, 30100, Murcia, Spain.
2Cardiopulmonary Service, Hospital for Small Animals, Royal (Dick) School of Veterinary Studies, Easter Bush Campus, EH25 9RG Roslin, Scotland.

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Marco Baron Toaldo 3Department of Veterinary Medical Sciences, Alma Mater Studiorum-University of Bologna, Via Tolara di Sopra 50, 40064 Ozzano Emilia, Italy.
4Division of Cardiology, Clinic for Small Animal Internal Medicine, Vetsuisse Faculty, University of Zurich, CH-8057 Zürich, Switzerland.

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Jonathan Bouvard 2Cardiopulmonary Service, Hospital for Small Animals, Royal (Dick) School of Veterinary Studies, Easter Bush Campus, EH25 9RG Roslin, Scotland.

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Tony M. Glaus 4Division of Cardiology, Clinic for Small Animal Internal Medicine, Vetsuisse Faculty, University of Zurich, CH-8057 Zürich, Switzerland.

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Josefa Fernández del Palacio 1Department of Animal Medicine and Surgery, Veterinary Faculty, University of Murcia, Campus Espinardo, 30100, Murcia, Spain.

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Abstract

OBJECTIVE

To determine variability of global longitudinal strain (GLS) and strain rate (SR) measurements in dogs with and without cardiac disease derived from 2-D speckle tracking echocardiography (STE) by use of various software.

ANIMALS

2 cohorts comprising 44 dogs (23 cardiovascularly healthy and 21 with cardiac disease) and 40 dogs (18 cardiovascularly healthy and 22 with cardiac disease).

PROCEDURES

Transthoracic echocardiographic images in each cohort were analyzed with vendor-independent software and vendor-specific 2-D STE software for each of 2 vendors. Values for GLS and SR obtained from the same left parasternal apical views with various software were compared. Intraobserver and interobserver variability was determined, and agreement among results for the various software was assessed.

RESULTS

Strain analysis was not feasible with vendor-independent software for 20% of images obtained with the ultrasonography system of vendor 1. Intraobserver and interobserver coefficient of variation was < 10% for GLS values, whereas SR measurements had higher variance. There was a significant difference in GLS and SR obtained for each cohort with different software. Evaluation of Bland-Altman plots revealed wide limits of agreement, with variance for GLS of up to 6.3 units in a single dog.

CONCLUSIONS AND CLINICAL RELEVANCE

Results of longitudinal strain analysis were not uniform among software, and GLS was the most reproducible measurement. Significant variability in results among software warrants caution when referring to reference ranges or comparing serial measurements in the same patient because changes of < 6.5% in GLS might be within measurement error for different postprocessing software.

Abstract

OBJECTIVE

To determine variability of global longitudinal strain (GLS) and strain rate (SR) measurements in dogs with and without cardiac disease derived from 2-D speckle tracking echocardiography (STE) by use of various software.

ANIMALS

2 cohorts comprising 44 dogs (23 cardiovascularly healthy and 21 with cardiac disease) and 40 dogs (18 cardiovascularly healthy and 22 with cardiac disease).

PROCEDURES

Transthoracic echocardiographic images in each cohort were analyzed with vendor-independent software and vendor-specific 2-D STE software for each of 2 vendors. Values for GLS and SR obtained from the same left parasternal apical views with various software were compared. Intraobserver and interobserver variability was determined, and agreement among results for the various software was assessed.

RESULTS

Strain analysis was not feasible with vendor-independent software for 20% of images obtained with the ultrasonography system of vendor 1. Intraobserver and interobserver coefficient of variation was < 10% for GLS values, whereas SR measurements had higher variance. There was a significant difference in GLS and SR obtained for each cohort with different software. Evaluation of Bland-Altman plots revealed wide limits of agreement, with variance for GLS of up to 6.3 units in a single dog.

CONCLUSIONS AND CLINICAL RELEVANCE

Results of longitudinal strain analysis were not uniform among software, and GLS was the most reproducible measurement. Significant variability in results among software warrants caution when referring to reference ranges or comparing serial measurements in the same patient because changes of < 6.5% in GLS might be within measurement error for different postprocessing software.

Two-dimensional STE is a cardiac imaging technique that allows noninvasive assessment of LV myocardial function with angle-independent deformation analysis.1 To perform deformation analysis, 2-D STE involves use of a postprocessing computer algorithm that tracks over time the motion of fixed patterns of speckles, which are natural artifacts generated by reflection, refraction, and scattering of ultrasonographic waves.1–6 Deformation variables assessed by use of 2-D STE include the so-called Lagrangian strain, which is defined as the comparison of the length of a myocardial segment at a given point in time, usually end-systole, with a reference length, usually its relaxed length at end-diastole, and SR, which represents the change in length per unit of time.2,4,6

Various vendor-dependent and -independent software systems have been developed for strain analysis by use of 2-D STE.7 Differences in derived results among these systems have been detected in human medicine, with postprocessing procedures an important determinant of variability in results among software plaftorms.8 An EACVI-ASE task force assessed and attempted to standardize GLS results. The task force identified moderate, significant variation in GLS values determined by various systems, with an absolute difference in strain units between systems of up to 3.7% in the same patient, which could be important when performing serial evaluations.8

Speckle tracking echocardiography has been validated against sonomicrometry in dogs,9 and various deformation variables have good correlation with invasive indices of LV function.10 Moreover, adequate feasibility and reproducibility for clinical and research purposes have been reported in healthy awake and sedated dogs.1,11–15 Several indices derived from 2-D STE have also been assessed in canine patients with myxomatous mitral valve disease of varying severity,16–19 dilated cardiomyopathy,1,15 and patent ductus arteriosus.1,11,20–22 Finally, 2-D STE has been used in several models of cardiac disease, including Duchenne muscular dystrophy.23 Published data on veterinary 2-D STE have mainly been for vendor-dependent STE software, including various versions of several plaftorms.1,10–23

To the authors' knowledge, the comparability of strain variables for dogs obtained by use of different software has not been investigated. Therefore, the objective of the study reported here was to compare 2-D STE-derived longitudinal deformation parameters obtained by use of vendor-specific and vendor-independent software for dogs with and without cardiac disease. The hypothesis was that results for the various software systems would have only moderate agreement for longitudinal global variables.

Materials and Methods

Animals

The study population comprised 2 groups of dogs. One cohort was from the Veterinary Teaching Hospital of the University of Murcia; images for this cohort were obtained with the ultrasound unita for vendor 1 (vendor 1 cohort). The other cohort was from the Hospital for Small Animals of the University of Edinburgh; images for this cohort were obtained with the ultrasound unitb for vendor 2 (vendor 2 cohort). For the vendor 1 cohort, cardiovascularly healthy dogs and client-owned dogs with cardiac disease of any age and body weight were prospectively enrolled between January 2015 and June 2017. Dogs with cardiac disease were recruited during cardiology consultations, whereas healthy dogs were owned by staff members and students. For the vendor 2 cohort, cardiovascularly healthy dogs and client-owned dogs with cardiac disease were recruited between October 2017 and January 2018. Client-owned dogs were identified during examination by the cardiopulmonary service.

Dogs were eligible for inclusion when they had undergone complete ECG-gated echocardiography that included good-quality images obtained at an adequate frame rate (between 60 and 100 Hz) to allow reliable longitudinal strain analysis. Dogs were considered cardiovascularly healthy or affected by cardiac disease on the basis of the medical history and results of a physical examination, CBC, serum biochemical analysis, noninvasive blood pressure measurement, ECG, conventional echocardiography, and, if needed, thoracic radiography. Owner consent was obtained for each dog before enrollment in the study. The study protocol was approved by the Ethics Committees of the Universities of Murcia and Edinburgh.

Echocardiographic examination

Transthoracic echocardiography at the University of Murcia was performed by a board-certified veterinary cardiologist (JFP) or a supervised resident in a cardiology training program (GS) with the ultrasound unit of vendor 1, which was equipped with a multifrequency 1- to 5-MHz phased-array sector transducer. Transthoracic echocardiography at the University of Edinburgh was performed by a board-eligible veterinary cardiologist (GS) or a supervised resident in a cardiology training program (JB) with the ultrasound unit of vendor 2, which was equipped with multifrequency 1.5- to 4.6-MHz and 2.4- to 8.0-MHz phased-array sector transducers.

Echocardiography was performed with simultaneous recording of the ECG and in accordance with published veterinary recommendations.24–26 Images recorded at a frame rate between 60 and 100 Hz for at least 4 consecutive cardiac cycles were used for 2-D STE analysis.

2-D STE analysis of echocardiographic images for the vendor 1 system

Echocardiographic examinations obtained with the ultrasound machine of vendor 1 were stored digitally for offline analysis and exported in standard formats to vendor 1-specific softwarec and as DICOM files to vendor-independent software.d Longitudinal strain and SR analyses were performed by a single operator (GS) with vendor 1-specific software and vendor-independent software. Analyses were performed with the same good-quality 2-D cine loop recorded from the left apical 4-chamber view.

For the vendor 1 software, longitudinal strain was obtained by manual placement of 3 reference points (one on each side of the mitral valve annulus and the third at the apical endocardial border). The software then created an ROI with the automatic delineation of endocardial and epicardial borders, and the operator adjusted the ROI to best incorporate the entire myocardial thickness. The LV was divided into 7 segments (basal septal, midseptal, apical septal, apical, apical lateral, midlateral, and basal lateral) by the software. The operator verified that the ROI followed the movements of each segment of the myocardium throughout the cardiac cycle; the operator performed manual adjustments when necessary. The software algorithm performed speckle tracking across the myocardium, and a weighted mean of the myocardial deformation (with the weighting greatest at the endocardium) was used for strain calculation. Derived segmental strain, global strain, and SR curves were generated, with the global waveform being created by use of a weighted mean of the regional strains. Analysis was performed by the vendor 1 software. If curves were not considered adequate and reliable, the dog was excluded from the study. Values for GLS, which were automatically provided by the software, were recorded; the values corresponded to the maximum strain or peak strain of the GLS waveform over the entire cardiac cycle. Peak systolic and diastolic SR values, which were displayed after placing the cursor at the appropriate time point along the global SR curves, were also recorded.

For the vendor-independent software, the endocardial border of the myocardium was manually traced from an end-diastolic frame starting at the septal side of the mitral valve annulus. The software automatically marked the epicardial border, and the operator verified that the ROI followed the movements of each segment of the myocardium throughout the cardiac cycle, making adjustments to the tracings when necessary. The software algorithm performed speckle tracking of 6 segments (basal septal, midseptal, apical septal, apical lateral, midlateral, and basal lateral) at the endocardial and epicardial regions; however, strain values for the epicardial region were excluded from analysis because the values sometimes were outside the echocardiographic window. Time-domain LV strain curves for each segment were created. The GLS indices were calculated as the mean for the peak segmental strain indices. The GLS values provided by the software (which corresponded to global endocardial peak longitudinal strains) and systolic endocardial SR values were recorded by the operator. Diastolic SR values, which were displayed after placing the cursor at the appropriate time point along the global endocardial SR curves, were also recorded.

2-D STE analysis of echocardiography images for the vendor 2 system

Echocardiographic examinations obtained with the ultrasound system for vendor 2 were stored digitally for offline analysis and exported in standard formats to vendor 2-specific softwaree and as DICOM files to the same vendor-independent software used for the vendor 1 cohort. Longitudinal strain and SR analyses were performed by a single operator (GS) with vendor 2-specific software and vendor-independent software. Analyses were performed with the same good-quality 2-D cine loop recorded from the left apical 4-chamber view.

For vendor 2 software, the endocardial border of the myocardium was manually traced from an end-systolic frame starting at the septal side of the mitral valve annulus, after which the software automatically created an ROI that the operator could adjust to best incorporate the entire myocardial thickness. The software algorithm then performed speckle tracking of 6 segments (basal septal, midseptal, apical septal, apical lateral, midlateral, and basal lateral) and assessed tracking quality. Segments marked as having inadequate tracking quality were retraced. If unreliable segments persisted after retracing was performed, the dog was excluded from the study. For segments with adequate tracking quality, segmental strain, GLS, and SR measured at the mid-myocardial layer were quantified by the software, and the global waveform was created by use of the mean of the regional strains. The GLS was automatically calculated by the software and displayed by default; it corresponded to the peak mid-myocardial longitudinal strain value, which was verified by the operator and then recorded. Peak systolic and diastolic SR values were recorded from the appropriate time point along the global SR curves. The GLS value obtained from the endocardial layer of the LV myocardium (which was displayed by the software as requested by the operator) was also recorded.

For the vendor-independent software, the same technique described for evaluation of echocardiographic images for vendor 1 was used.

Measurement reliability

Intraobserver within-day and between-day variability as well as interobserver variability were determined. Six echocardiograms obtained with each ultrasound system were arbitrarily selected, and the cine loops were subjected to repeated analyses by the same operator (GS) 2 times on the same day and on 2 separate days. For interobserver variability, a second investigator (MBT for images obtained with the ultrasound system for vendor 1 and JB for images obtained with the ultrasound system for vendor 2) performed separate repeated analyses on the same cine loops that the first operator used for assessment of intraobserver variability.

Images obtained with the ultrasound system for vendor 1 were assessed with vendor 1-specific software and vendor-independent software, and images obtained with the ultrasound system for vendor 2 were assessed with vendor 2-specific software and vendor-independent software. Observers were unaware of all other results.

Statistical analysis

Statistical analysis was performed with commercially available software.f Distribution of variables was tested for normality by use of the Shapiro-Wilk test (α = 0.05). Mean and SD were calculated for normally distributed variables, and median and range were calculated for nonnormally distributed variables. Study population characteristics were analyzed with descriptive statistics. For all analyses, values were considered significant at P < 0.05.

For the repeatability evaluation, mean and SD values resulting from vendor-dependent and vendor-independent strain analysis of the 6 repeated examinations obtained with either ultrasound system were used to determine interobserver and within-day and between-day intraobserver CVs for each software and were quantified as follows: CV = (SD of the measurements/mean of measurements) × 100. Values < 15% were considered adequate for clinical use.9

A paired t test or Wilcoxon signed rank test was used to make comparisons between the 2-D STE variables obtained with vendor-dependent and vendor-independent software. For each pair of values, the LOA and systematic errors were assessed by calculating the mean difference (bias) and the SD of the differences by use of Bland-Altman analysis.

Results

Study population

Ninety-six dogs were initially enrolled in the study (55 in the vendor 1 cohort and 41 in the vendor 2 cohort). Eleven dogs of the vendor 1 cohort and 1 dog of the vendor 2 cohort were subsequently excluded because of suboptimal tracking with the vendor-independent software.

The final vendor 1 cohort comprised 44 dogs (18 sexually intact males, 12 spayed females, 10 sexually intact females, and 4 castrated males). Age ranged from 1 to 17 years (median, 6.5 years), and body weight ranged from 3 to 48 kg (median, 21 kg). Fourteen dogs were mixed-breed dogs; the other 30 dogs were purebreds of 17 breeds, with the most common being Labrador Retriever (n = 4). Twenty-three dogs were cardiovascularly healthy, and 21 dogs had cardiac disease. Dogs with cardiac disease comprised 14 with myxomatous mitral valve disease (4 at stage B1, 5 at stage B2, and 5 at stage C, as determined on the basis of the American College of Veterinary Internal Medicine classification system27), 2 with preclinical idiopathic dilated cardiomyopathy, 2 with concurrent mild subaortic and pulmonic stenosis, and 1 each with mild subaortic stenosis, hypertensive LV hypertrophy, and right auricular hemangiosarcoma.

The final vendor 2 cohort comprised 40 dogs (17 spayed females, 13 castrated males, 9 sexually intact males, and 1 sexually intact female). Age ranged from 6 months to 14 years (median, 7 years), and body weight ranged from 1.3 to 47.5 kg (median, 21.2 kg). Four dogs were mixed-breed dogs; the other 36 dogs were purebreds of 22 breeds, with the most common being Labrador Retriever (n = 8) and German Shepherd Dog (4). Eighteen dogs were cardiovascularly healthy, and 22 dogs had cardiac disease. Dogs with cardiac disease comprised 8 with myxomatous mitral valve disease (5 at stage B1 and 3 at stage B2), 4 with preclinical idiopathic dilated cardiomyopathy, 3 with left-to-right shunting patent ductus arteriosus, 3 with severe pulmonic stenosis, 2 with tricuspid valve dysplasia, and 1 each with mild subaortic stenosis and pericardial hemangiosarcoma.

2-D STE

Results for 2-D STE variables for each software system (vendor 1, vendor 2, and vendor independent) were determined (Table 1), including GLS, as measured by use of each system (Figures 1 and 2).

Figure 1—
Figure 1—

Representative images for longitudinal strain analysis in a dog with myxomatous mitral valve disease. A left apical 4-chamber view was obtained with the ultrasound system of vendor 1 and analyzed by vendor-independent (A) and vendor 1-specific (C) 2-D STE software, which resulted in a longitudinal time-strain curve for the vendor-independent (B) and vendor 1-specific (D) software. Notice the simultaneously recorded ECG at the bottom of each ultrasonogram. In panel A, the LV endocardium is outlined (green line). In panel C, the LV was divided into 7 segments (basal septal [ISB], midseptal [ISM], apical septal [SApic], apical [Apic], apical lateral [LApic], midlateral [ALM], and basal lateral [ALB]) by the software. In panels B and D, the y-axis is the percentage of longitudinal strain and the x-axis is time, and color-coded longitudinal strain curves are provided for each segment. The GLS curve (black line in panel B and dotted white line in panel D) is displayed.

Citation: American Journal of Veterinary Research 80, 4; 10.2460/ajvr.80.4.347

Figure 2—
Figure 2—

Representative images for longitudinal strain analysis in a dog with tricuspid valve dysplasia. A left apical 4-chamber view was obtained with the ultrasound system of vendor 2 and analyzed by vendor-independent (A) and vendor 2-specific (C) 2-D STE software, which resulted in a longitudinal time-strain curve for the vendor-independent (B) and vendor 2-specific (D) software. Notice the simultaneously recorded ECG at the bottom of each ultrasonogram. In panel A, the LV endocardium is outlined (green line). In panel C, the LV is outlined (red) by the software. In panels B and D, the y-axis is the percentage of longitudinal strain and the x-axis is time, and color-coded longitudinal strain curves are provided for each segment. The GLS curve (black line in panel B and dotted white line in panel D) is displayed. AVC = Aortic valve closure.

Citation: American Journal of Veterinary Research 80, 4; 10.2460/ajvr.80.4.347

Table 1—

Median (range) values for STE-derived longitudinal strain and SR obtained with vendor-dependent and vendor-independent software systems for cardiovascularly healthy dogs and dogs with various cardiac diseases.

 Vendor 1 cohort (n = 44)Vendor 2 cohort (n = 40)
VariableVendor 1IndependentP valueVendor 2IndependentP value
GLS (%)*
  Transmural to endocardial−21.0 (−33.0 to −14.0)−19.5 (−39.0 to −11.0)0.020
  Mid-myocardial to endocardial−17.5 (−27.5 to −9.5)−18.8 (−28.9 to −1.3)0.006
  Endocardial to endocardial−21.7 (−32.3 to −11.4)−18.8 (−28.9 to −1.3)< 0.001
Systolic SR (1/s)−2.2 (−3.2 to −1.4)−1.8 (−4.4 to −0.7)0.005−1.8 (−2.9 to −0.3)−1.6 (−2.9 to −0.4)< 0.001
Early diastolic SR (1/s)2.1 (0.1 to 3.4)2.0 (0.6 to 5.4)0.3571.6 (0.5 to 5)1.2 (0.4 to 4.0)< 0.001
Late diastolic SR (1/s)1.5 (0.7 to 3.5)0.9 (0.3 to 2.4)< 0.0011.0 (0.6 to 2.3)0.7 (0.3 to 2.3)0.003

Values were considered significant at P < 0.05.

Represents GLS values for the specific layer evaluated by use of the vendor-specific and vendor-independent software, respectively.

Weighted mean was used, with the weighting greatest at the endocardium.

— Not applicable; values could not be determined by use of the software.

Reproducibility of GLS values obtained with each of the 3 software systems was good, as indicated by within-day and between-day intraobserver and interobserver CVs < 10% (Tables 2 and 3). However, reproducibility for systolic and diastolic SR was not always satisfactory, especially for late diastolic SR values, for which CVs reached 18.7%.

Table 2—

Intraobserver and interobserver variability of STE-derived longitudinal strain and SR for 6 dogs obtained with vendor 1-dependent software and vendor-independent software.

 Intraobserver variability 
 Within-day CV (%)Between-day CV (%)Interobserver CV (%)
VariableVendor 1IndependentVendor 1IndependentVendor 1Independent
GLS
Transmural* and endocardial3.55.63.19.583.9
Systolic SR7.48.66.29.87.85.7
Early diastolic SR11.35.89.316.98.81 1.1
Late diastolic SR14.213.18.518.714.217.3

Weighted mean was used, with the weighting greatest at the endocardium.

Table 3—

Intraobserver and interobserver variability of STE-derived longitudinal strain and SR for 6 dogs obtained with vendor 2-dependent software and vendor-independent software.

 Intraobserver variability 
 Within-day CV (%)Between-day CV (%)Interobserver CV (%)
VariableVendor 2IndependentVendor 2IndependentVendor 2Independent
GLS
  Mid-myocardial2.85.56.8
  Endocardial2.96.27.43.89.97.1
Systolic SR6.98.98.16.87.615.1
Early diastolic SR4.111.110.84.711.716.2
Late diastolic SR6.010.911.016.917.111.6

See Table 1 for remainder of key.

2-D STE analysis of images for the vendor 1 system

Comparison of most strain indices revealed significant differences among software systems, with absolute numbers for mean GLS values slightly higher for the vendor 1-specific software than for the vendor-independent software (Table 1). Evaluation of Bland-Altman plots revealed a mean difference of −0.9% (95% LOA, −6.5% to 4.7%; Figure 3). For systolic SR, the mean difference was −0.3 1/s (95% LOA, −1.5 to 0.9 1/s). For early diastolic SR, the mean difference was 0.1 1/s (95% LOA, −1.5 to 1.7 1/s). For late diastolic SR, the mean difference was 0.7 1/s (95% LOA, −0.5 to 1.9 1/s).

Figure 3—
Figure 3—

Bland-Altman plots for GLS (A), systolic SR (sSR; B), early diastolic SR (edSR; C), and late diastolic SR (ldSR; D) values derived from images obtained with the ultrasound system of vendor 1. Differences in values obtained with vendor 1-dependent and vendor-independent software are plotted against mean values obtained with both software. Bias (mean difference; solid line) and 95% LOA (mean difference ± [1.96 × SD]; dotted lines) are indicated.

Citation: American Journal of Veterinary Research 80, 4; 10.2460/ajvr.80.4.347

2-D STE analysis of images for the vendor 2 system

Comparison of each strain index revealed significant differences among software systems (Table 1). Absolute numbers for vendor 2-derived mid-myocardial GLS appeared to be slightly lower than results for vendor-independent-derived endocardial GLS. In contrast, mean vendor 2-derived endocardial GLS was higher than mean vendor-independent-derived endocardial GLS.

Evaluation of Bland-Altman plots revealed a mean difference between vendor 2-derived mid-myocardial GLS and vendor-independent-derived endocardial GLS of 1.3% (95% LOA, −4.4% to 7.0%; Figure 4). The mean difference between vendor 2-derived endocardial GLS and vendor-independent-derived endocardial GLS was −2.6% (95% LOA, −8.9% to 3.7%).

Figure 4—
Figure 4—

Bland-Altman plots for mid-myocardial to endocardial GLS (A), endocardial to endocardial GLS (B), systolic SR (sSR; C), early diastolic SR (edSR; D), and late diastolic SR (ldSR; E) values derived from images obtained with the ultrasound system of vendor 2. Differences in values obtained with vendor 2-dependent and vendor-independent software are plotted against mean values obtained with both software. See Figure 3 for remainder of key.

Citation: American Journal of Veterinary Research 80, 4; 10.2460/ajvr.80.4.347

For systolic SR values, evaluation of Bland-Altman plots revealed a mean difference of −0.2 1/s (95% LOA, −1.4 to 1.8 1/s). For early diastolic SR, the mean difference was 0.7 1/s (95% LOA, −1.7 to 3.1 1/s). For late diastolic SR, the mean difference was 0.3 1/s (95% LOA, −0.7 to 1.3 1/s).

Discussion

To the authors' knowledge, the study reported here was the first in which investigators compared results of various 2-D STE software systems for longitudinal strain analysis in dogs with and without cardiac disease. Results indicated that despite use of good-quality images and a high frame rate, reproducibility was not uniform across software systems. When strain analysis was feasible, the reproducibility of GLS measurements was good and superior to that of SR for each software system. Finally, there was significant variability in GLS and SR measurements among the software systems.

In the present study, vendor-independent software could not reliably be used to analyze images attained from 11 of 55 (20%) dogs initially included in the vendor 1 cohort, despite inclusion of a prospective image acquisition protocol for 2-D STE strain analysis. A potential determining factor could have been the use of DICOM files for the vendor-independent system, which had a lower frame rate and image quality than did the raw image files used for the vendor-dependent software.7 A low frame rate can affect reliability of strain analysis by influencing temporal resolution, whereas poor image quality can impede accurate tracking of speckles by affecting spatial resolution.7 For speckle tracking analysis of echocardiographic images of humans, an acquisition frame rate > 30 Hz, and ideally approximately 50 Hz, is recommended.4 In the present study, frame rates > 60 Hz were selected because dogs typically have higher heart rates than humans, and the rate appeared to be appropriate, at a minimum, for raw format image analysis because all images could be analyzed with vendor-specific software. Considering that image quality appeared to be equally good in all the dogs included and that most of the images obtained with the ultrasound system of vendor 2 by use of the predetermined frame rate could be reliably analyzed with the vendor-independent software, another possible determinant could have been differences in technology of the ultrasound machines. In fact, image characteristics may differ among ultrasound machines on the basis of temporal and spatial resolution, filter settings, and other postprocessing effects.8 Heart rate can also affect accuracy of 2-D STE measurements, and high heart rates at conventional frame rates might result in underestimation of GLS.28 In a study29 conducted to assess the effects of heart rate on strain variables, values did not differ significantly when pacing rates between 120 and 180 beats/min were used and frame rate was increased with increasing heart rate. In the present study, frame rate was not adjusted on the basis of heart rate; instead, the frame rate was carefully maintained within the predetermined range; thus, it might be possible that this intervention resulted in inadequate frame rate for some of the images exported to the DICOM format. Reliable analysis can be performed on DICOM data compressed at 30 frames/s, but lower strain values may be observed.30 Although heart rate, frame rate, ultrasound technology, and data compression could all have affected strain assessment, the source of the dissimilar reproducibility of the vendor-independent analysis remains unclear.

When strain analysis was reproducible, intraobserver and interobserver agreement of GLS values was good for each software system, which is similar to results of studies of humans28 and dogs.12,13 Reproducibility of systolic SR also appeared to be acceptable because CVs were generally < 10%, except for interobserver variability in the vendor 2 cohort, for which the maximum value recorded was 15.1%. In contrast, diastolic SR indices had overall poorer reproducibility. Although each software system provided GLS values automatically, in most cases, they displayed SR measurements only after the observer placed a cursor over the global SR waveform at an appropriate time point, which potentially added variability. Different user interactions and semiautomatic user guidance may have a substantial impact on measurement variability among systems, particularly for SR data.6,8 Furthermore, time-dependent variables (eg, SR) are more difficult to assess than is deformation and require a higher frame rate.6 Regardless of the cause, GLS was the most reproducible longitudinal strain index in the present study.

Significant differences in strain variables were found among the 2-D STE software systems, and some results for individual dogs differed considerably. Unfortunately, the source of discordance could not be further defined because a criterion-referenced technique for deformation imaging (ie, sonomicrometry) was not available.7 Discrepancies among manufacturers are mainly attributed to differences in STE algorithms; there is a lack of published information on software validation, and most are not open-source algorithms.7 There have been overall improvements in variation among vendors since the creation of the EACVI-ASE Industry Strain Standardization Task Force,31,32 and it is hoped that these improvements will continue. However, considering that purchasing updated software might incur substantial costs, it is likely that, at least for veterinary settings, older versions of software will be in use for a considerable period during which variability will have to be taken into account. In the study reported here, repeated measurements of GLS obtained for the same dog with 2 software systems could differ by up to 5.6 to 6.3 strain units, which suggested a change of < 6.5% for the GLS could have been within measurement error for the various postprocessing software systems. This may have an important clinical impact because serial echocardiographic examinations to determine GLS could be used to track subclinical changes in LV function over time.31

Software systems can also be used to evaluate the various myocardial layers, which could represent an important source of variability. Each software system used in the present study automatically displayed a value defined as GLS, but this was sometimes derived from a specific layer rather than from the entire myocardium. For example, the vendor-independent software derived GLS from endocardial strain data (values from epicardial layers were excluded because the analysis was not considered reliable), which might have caused overestimation of strain values, compared with a software system that derived GLS from the myocardial midline.7 On the other hand, as stated by the manufacturer's instructions, vendor 1-specific software incorporated a weighted mean of the myocardial deformation across the myocardium in strain calculations, with the weighting greatest at the endocardium. Endocardial, midline, and epicardial strain values and mean values calculated over the entire cardiac wall can all potentially be obtained,28 and the presence of a longitudinal strain gradient from the endocardial layer to the epicardial layer has been established, which possibly is caused by increases in end-diastolic wall stress toward the endocardium and differences in coronary perfusion and metabolism between the layers.33 However, use of strain data obtained at different layers as the only explanation of variability among results for various software systems in the study reported here was not confirmed by comparing the endocardial measurements obtained with vendor 2-specific software and measurements obtained with the vendor-independent software, which also were derived from the endocardial layer. In fact, significant differences were found between vendor 2-derived endocardial GLS and vendor-independent-derived endocardial GLS, and Bland-Altman analysis provided a similarly wide LOA for all longitudinal strain values that were evaluated. Similarly, results of another study8 indicated moderate significant differences between endocardial GLS strain values among systems of 9 vendors. Furthermore, it is expected that endocardial strain values would be higher than transmural strain values,33 as detected for the vendor 2 cohort but in contrast to results detected for the vendor 1 cohort. On the other hand, comparison of vendor-dependent software by use of the entire myocardial wall and vendor-independent software by use of only endocardial strain (as was performed in the present study) resulted in small nonsignificant mean differences in peak strain between STE software systems in another study.7 In addition, comparison of 2 software programs, both of which analyzed full myocardial thickness, led to better agreement between global strain measurements than that found when comparing results for a system that determined mean transmural strain to another system that primarily analyzed endocardial strain.34 Therefore, results of between-layer strain comparability appeared to be conflicting. Because there is currently no evidence about the relative clinical value of strain measurements for various specific layers, endocardial, midline, epicardial, and transmural strain values can all be obtained, provided the layer analyzed is reported,28 and values derived from different ROIs are not used interchangeably without appropriate precautions.

The EACVI-ASE Industry Strain Standardization Task Force also advocates the use of end-systolic strain values as default variables to describe myocardial deformation.6 In the present study, peak strain measurements were chosen because these were provided automatically and therefore considered more likely to be used in clinical veterinary settings in the absence of specific guidelines.

The present study involved dogs with and without myocardial disease to ensure a broad spectrum of strain values. The LV function ranged from enhanced (eg, some dogs with myxomatous mitral valve disease at stage B2) to severely reduced (eg, some dogs with idiopathic dilated cardiomyopathy). However, LV function has not been found to significantly affect longitudinal strain variability,30 and the inclusion of both cardiovascularly healthy dogs and dogs with a variety of cardiac diseases was intended to provide useful results for clinical settings.

The present study had limitations. The number of dogs included was small and not uniform across groups, and there was a dissimilar distribution of dogs within disease category. Because it was a clinical study, it was not possible to control for the status of the animals included; at the same time, a broad range of strain values was desired. Therefore, a similar study conducted with a larger population of dogs or within a narrowly defined disease category could provide different results. A second limitation was that some of the affected dogs received medication, and some of the cardiac treatments that were administered could possibly have affected LV function and therefore strain variables. However, because a paired sample design was used and each dog served as its own control animal, with the same echocardiographic image analyzed for each dog, the inclusion of treated patients was allowed. A third limitation was that dogs underwent echocardiographic examination by use of a single vendor ultrasound system depending on their location, whereas they ideally should have undergone echocardiography by use of both ultrasound systems, which would have permitted us to make direct comparisons between the systems. Furthermore, the present study did not include regional strain analysis; this was intentional because one of the vendor-specific software systems used a segmentation model that differed from those of the other 2 systems.

Another limitation was the use of an early version of the vendor-dependent 2-D STE software, although there can be significant variability of strain measurements obtained by use of various versions of the same 2-D STE system. More specifically, higher GLS values have been obtained in healthy humans by use of one of the versions of the software system used in the present study, compared with values for a more recent version of that system,35 and improved agreement of GLS between the latest versions of the same software from different vendors has been reported.32

Finally, endocardial strain and transmural strain were directly compared, despite the differences known to exist between them that are related to the gradient between endomyocardium and epimyocardium. Ideally, only values obtained from the same ROI should be compared. However, commonly used software systems were used in the study reported here, and comprehensive layer-specific analysis was not always available; thus, the variables examined were primarily those provided by default. Therefore, results of the present study appeared to the authors to be useful because they could aid in assessing, as carefully as possible, strain indices obtained in clinical situations.

In the study reported here, reproducibility of analysis was not uniform across software systems, but when strain assessment could be performed, the reproducibility of GLS measurements was good and superior to that of SR. Because of significant variability in GLS and SR measurements obtained by use of the various software systems, it would appear necessary to be cautious when comparing values obtained for an individual dog to its previous measurements or to reference values that have been derived by use of other ultrasound machines, software, and even other versions of the same software.

Acknowledgments

Supported by an international mobility grant from the International School of Doctoral Studies of the University of Murcia and the Campus of International Excellence Mare Nostrum.

The authors thank Yolanda Martinez-Pereira for resident supervision at the University of Edinburgh, Alisdair Boag for language editing and proofreading, Darren Shaw for assistance with the statistical analysis, and Frane Ivasovic and Sara-Ann Dickson for technical assistance.

ABBREVIATIONS

ASE

American Society of Echocardiography

CV

Coefficient of variation

EACVI

European Association of Cardiovascular Imaging

GLS

Global longitudinal strain

LOA

Limits of agreement

LV

Left ventricle

ROI

Region of interest

SR

Strain rate

STE

Speckle tracking echocardiography

Footnotes

a.

iE33 ultrasound system, Philips Medical Systems, Andover, Mass.

b.

Vivid E9, General Electric Medical Systems, Waukesha, Wis.

c.

QLAB 2D strain software, version 9.0, Philips Medical Systems, Andover, Mass.

d.

TomTec 2D cardiac performance analysis (2D CPA), Image-Arena, version 4.6, TomTec Imaging Systems, Unterschleissheim, Germany.

e.

EchoPach PC, version 113, GE Vingmed Ultrasound AS, Horten, Norway.

f.

IBM SPSS statistics for Windows, version 21.0, IBM Corp, Armonk, NY.

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