LEFT VENTRICULAR FUNCTION
Normal Ranges of Left Ventricular Strain: A Meta-Analysis Teerapat Yingchoncharoen, MD, Shikhar Agarwal, MD, MPH, Zoran B. Popovic, MD, PhD, and Thomas H. Marwick, MD, PhD, MPH, Cleveland, Ohio
Background: The definition of normal values of left ventricular global longitudinal strain (GLS), global circumferential strain, and global radial strain is of critical importance to the clinical application of this modality. The investigators performed a meta-analysis of normal ranges and sought to identify factors that contribute to reported variations. Methods: MEDLINE, Embase, and the Cochrane Library database were searched through August 2011 using the key terms ‘‘strain,’’ ‘‘speckle tracking,’’ ‘‘left ventricle,’’ and ‘‘echocardiography’’ and related phrases. Studies were included if the articles reported left ventricular strain using two-dimensional speckle-tracking echocardiography in healthy normal subjects, either in the control group or as a primary objective of the study. Data were combined using a random-effects model, and effects of demographic, hemodynamic, and equipment variables were sought in a meta-regression. Results: The search identified 2,597 subjects from 24 studies. Reported normal values of GLS varied from 15.9% to 22.1% (mean, 19.7%; 95% CI, 20.4% to 18.9%). Normal global circumferential strain varied from 20.9% to 27.8% (mean, 23.3%; 95% CI, 24.6% to 22.1%). Global radial strain ranged from 35.1% to 59.0% (mean, 47.3%; 95% CI, 43.6% to 51.0%). There was significant between-study heterogeneity and inconsistency. The source of variation was sought between studies using meta-regression. Blood pressure, but not age, gender, frame rate, or equipment, was associated with variation in normal GLS values. Conclusions: The narrowest confidence intervals from this meta-analysis were for GLS and global circumferential strain, but individual studies have shown a broad range of strain in apparently normal subjects. Variations between different normal ranges seem to be associated with differences in systolic blood pressure, emphasizing that this should be considered in the interpretation of strain. (J Am Soc Echocardiogr 2013;26:185-91.) Keywords: Strain, Meta-analysis, Normal range, Echocardiography
Recent developments in speckle-tracking echocardiography have enabled the quantitative assessment of myocardial function via image-based analysis of myocardial dynamics.1 Important applications of this technique include global assessment of left ventricular (LV) function using global longitudinal strain (GLS), global radial strain (GRS), and global circumferential strain (GCS)2,3 and regional assessment including measurement of the transmural distribution of strain,4 assessment of radial synchrony,5 and tissue characterization.6 Speckle-tracking echocardiographic measurement of these parameters has been validated against sonomicrometry7 and magnetic resonance imaging.8 The routine application of myocardial strain in clinical practice requires the definition of a normal range and an understanding of its reliability; each aspect is specific to the application as a marker of
global or regional function, in each image plane (longitudinal, circumferential, and radial). A variety of parameters might potentially influence the measurement of strain, including features specific to patients (age, gender, race, ethnicity, anthropometric variables), hemodynamic factors (heart rate, blood pressure), and cardiac factors (LV size, wall thickness).9 One cause for concern is the variation in recorded measurements among different vendors due to proprietary differences in the software used to calculate deformation.10 Because GLS is the simplest deformation parameter, and probably the closest to routine clinical application, we sought to define its normal range by providing a synthesis of all studies that reported normal or control patients. We also sought to evaluate the role of the vendor as a contributor to variation among reported normal ranges, particularly in relation to other sources of variation.
From the Cleveland Clinic, Cleveland, Ohio.
METHODS
Reprint requests: Thomas H. Marwick, MD, PhD, MPH, Cleveland Clinic, Cardiovascular Medicine J1-5, 9500 Euclid Avenue, Cleveland, OH 44195 (E-mail:
[email protected]). 0894-7317/$36.00 Copyright 2013 by the American Society of Echocardiography. http://dx.doi.org/10.1016/j.echo.2012.10.008
Search Strategy We searched MEDLINE, Embase, and the Cochrane Library database using the key terms ‘‘strain,’’ ‘‘speckle tracking,’’ ‘‘echocardiography,’’ and ‘‘left ventricle,’’ completing this search on August 8, 2011. To ensure the identification of all relevant trials, the reference lists of 185
186 Yingchoncharoen et al
Abbreviations
GCS = Global circumferential strain
GLS = Global longitudinal strain
GRS = Global radial strain LV = Left ventricular ROI = Region of interest
Journal of the American Society of Echocardiography February 2013
these articles were scrutinized to further identify studies pertinent to the topic. The search was limited to adult human studies published in English; abstracts without full text, review articles, editorial comments, and letters to the editor were excluded. The search strategy, study selection, and analysis adhered to Quality of Reporting of MetaAnalyses guidelines.11
Study Selection From these lists, studies were included if the articles reported LV strain using two-dimensional speckle-tracking echocardiography in healthy normal subjects. This review incorporates both observational studies that used control groups with normal results on echocardiography (which may have been obtained for patients referred to the echocardiography laboratory who therefore could have had subclinical dysfunction), as well as studies explicitly describing the recruitment of normal subjects from the community. Data Collation Clinical, echocardiographic, and strain data were extracted from individual studies by one author (T.Y.), verified by a second (T.H.M.), and entered into an electronic database. Where available, these data included group numbers and demographic, clinical, and echocardiographic data. Mean GLS, GRS, and GCS were extracted from the text, tables, or graphs. In situations in which we believed that multiple articles were published from a single data set, the largest study was assessed. Statistical Analysis The means and 95% CIs of GLS, GCS, and GRS were computed using random-effects models weighted by inverse variance.12 Betweenstudy heterogeneity was assessed using Cochran’s Q test (on the basis of the pooled RR by Mantel-Haenszel), as well as by measuring inconsistency (I2, the percentage of total variance across studies attributable to heterogeneity rather than chance).13 Because a number of important variables that influence strain differed among studies, we performed a regression using a general linear model to assess their influence on the variation in normal strain measurements. Statistical analysis was performed using standard software packages (Stata version 10.0, StataCorp LP, College Station, TX; and Comprehensive Meta-Analysis, Biostat, Englewood, NJ), with two-tailed P values < .05 considered significant.
RESULTS Study Selection In total, 201 titles were screened for relevance, of which there were 28 valid studies of GLS in a total of 2,597 subjects, from which 24 articles were considered eligible (Figure 1). From 24 articles, 13 articles (14 studies) with a total of 599 patients were eligible for the meta-analysis of GCS and 12 articles with 568 patients for GRS. The patient characteristics of the included studies are listed in Table 1.14-36
Potentially relevant papers from search (n=201)
36 articles not in humans 116 articles unrelated to study objectives 4 articles not in English
Potentially appropriate papers to be included in the meta-analysis (n = 45) 15 articles did not have data on normal subjects 30 eligible articles including 34 valid datasets
6 articles with incomplete data
24 eligible articles including 28 valid datasets used for meta-analysis
Figure 1 Study design. This flow chart illustrates the selection process for published reports. Normal Ranges Reported normal values of GLS (Figure 2) varied from 15.9% to 22.1% (mean, 19.7%; 95% CI, 20.4% to 18.9%). Betweenstudy heterogeneity was evidenced by a Cochran’s Q statistic of 1,935 (P < .0001) and inconsistency by an I2 value of 99. Normal GCS (Figure 3) varied from 20.9% to 27.8% (mean, 23.3%; 95% CI, 24.6% to 22.1%). GRS (Figure 4) ranged from 35.1% to 59.0% (mean, 47.3%; 95% CI, 43.6% to 51.0%). Both GCS and GRS showed between-study heterogeneity and inconsistency, similar to that of GLS. The funnel plot of all selected 25 articles showed no publication bias (Figure 5). Causes of Variability Age (47 6 11 years), male gender (51 6 24% men), body mass index (24.3 6 1.6 kg/m2), systolic blood pressure (124 6 5 mm Hg), frame rate (66 6 13 frames/sec), and equipment vendor were considered the variables most likely to influence GLS (Table 2). In a general linear model, only mean blood pressure was independently associated with higher values of strain. Vendor was not significantly associated with mean absolute GLS, and GLS in normal patients, assessed in 23 data sets using EchoPAC software (GE Healthcare, Milwaukee, WI), was no different from the measurement in five data sets using nonEchoPAC software ( 19.65 6 1.78% vs 19.67 6 1.80%, P = .98). DISCUSSION This is the first synthesis of the literature on the normal range of global strain. Although it emphasizes the association of strain with systolic blood pressure, differences in vendor and other variables shown to
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Table 1 Patient characteristics and measured strain types (longitudinal, circumferential, and radial) Year
n
Age (y)
Marwick et al.9
2009
242
51 6 12
44
130 6 16
L
Healthy volunteers
Volunteers without evidence of CVD
Kang et al.14 Lancellotti et al.17 Delgado et al.18 Narayanan et al.19
2008 2008 2008 2009
20 23 20 52
46 6 6 58 6 11 65 6 10 49 6 13
60 43 77 27
128 6 8 129 6 11
L, C, R L L L, C, R
Healthy individuals Normal control Normal control Normal control
Untreated hypertension Severe mitral regurgitation Coronary artery disease Mild hypertensive heart disease
Meluzin et al.20
2009
14
44 6 3
79
L, C, R
Healthy volunteers
Bussadori et al.21 Saito et al.22
2009 2009
30 46
37 6 6 29 6 7
63 87
L, C L, C, R
Healthy volunteers Healthy volunteers
Park et al.23 Ho et al.24 Dalen et al.15 Dalen et al.15 Manovel et al.16
2010 2010 2010 2010 2010
38 50 673 623 28
52 6 10 52 6 5 48 6 14 51 6 14 38 6 12
53 0 0 100 64
L, C, R L, R L L L, C
Healthy control Healthy control Healthy female Healthy male Healthy subjects
Idiopathic dilated cardiomyopathy Normal adults and children Comparison of 2D and 3D strain Diastolic dysfunction Chemotherapy Healthy individuals Healthy individuals Comparison of different software
Manovel et al.16
2010
28
38 6 12
64
L, C
Healthy subjects
Rodriguez-Bailon et al.25 Marcus et al.26
2010
105
39 6 10
45
24.7 6 3.3
117 6 12
L, C
Healthy volunteers
2011
25
22 6 1
64
22.1 6 2.3
118 6 12
L
Healthy, 20–24 y
Marcus et al.26
2011
13
27 6 1
62
23.1 6 2.2
121 6 11
L
Healthy, 25–29 y
Marcus et al.26
2011
13
36 6 3
46
24.9 6 2.9
123 6 12
L
Healthy, 30–40 y
Kouzu et al.27 Mizariene et al.28 Takamura et al.29 Butz et al.30
2011 2011 2011 2011
55 47 25 18
59 6 10 44 6 13 57 6 25 48 6 16
15 74 32 56
22.5 6 2.6
121 6 13 127 6 10 117 6 12 127 6 14
L, C, R L, C, R L, C, R L
No CVD Healthy subjects Normal subjects Normal subjects
Syeda et al.31 Yip et al.32 Kusunose et al.33
2011 2011 2011
42 60 58
60 6 3 53 6 10 67 6 11
38 52
119 6 13 133 6 20
L L, C, R L
Healthy population Healthy subjects Healthy volunteers
Imbalzano et al.34 Saleh et al.35 Reckefuss et al.36
2011 2011 2011
51 82 144
52 6 13 53 6 17 42 6 14
63 30 49
L, C, R L L, R
Healthy subjects Healthy individuals Normal adults
Study
Men (%)
BMI (kg/m2)
26.5 6 5
26 6 4 25.8 6 4.1 26.5 6 3.4
22 6 3
23 6 3 24.2 6 3
SBP (mm Hg)
118 6 13
114 6 13 126 6 12 127 6 17 133 6 14
122 6 7 24.2 6 4.2
Strain
Control selection
Disease studied
Comparison of different software Normal subjects Healthy adult and pediatric cohort Healthy adult and pediatric cohort Healthy adult and pediatric cohort LV hypertrophy Aortic regurgitation Acute pulmonary embolism Diagnosis of LV hypertrophy Heart transplantation Heart failure with normal EF Previous myocardial infarction Hypertension Transplanted heart Normal probands
BMI, body mass index; C, circumferential; CVD, cardiovascular disease; EF, ejection fraction; L, longitudinal; R, radial; SBP, systolic blood pressure; 3D, three-dimensional; 2D, two-dimensional.
be important in individual studies were not an explanation of between-study differences. There is no current consensus on normal values of GCS and GRS, and our study is the first to define normal values of these parameters on the basis of a meta-analysis. These findings show that confidence intervals for GCS are similar to those for GLS, but inferior results were obtained for GRS. Directional Components of Strain The development of speckle-tracking echocardiography more than a decade ago permitted for the first time the assessment of the directional nature of myocardial contraction.1 Most of the subsequent literature has focused on longitudinal strain, which is a sensitive marker of subclinical dysfunction that correlates with a variety of
biochemical markers of disease.1,37 However, the differential contributions of different diseases to endocardial and epicardial dysfunction might be measured from radial strain, and this may be particularly important in the assessment of myocardial ischemia.4 Despite the attraction of measuring radial strain from a physiologic or clinical standpoint, there are important technical challenges. The identification of greater variability of normal values of radial strain in this study is concordant with previous observations of inferior interobserver variability of radial strain compared with longitudinal strain.37 The differences in the reliability of each strain component are likely to be technical rather than biologic, and at least four factors are potentially important. First, the superiority of longitudinal strain may reflect the fact that measurements in the axial plane are more reliable than those that depend on azimuthal (or elevational) resolution.
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Journal of the American Society of Echocardiography February 2013
Study name
Statistics for each study
Mean and 95% CI
Standard Lower Upper Mean error Variance limit limit Z-Value P-Value Manovel (16) Delgado (18) Kusunose (33) Imbalzano (34) Syeda (31) Marwick (9) Kouzu (27) Marcus (26) Park (23) Ho (24) Marcus (26) Marcus (26) Yip (32) Dalen (15) Dalen (15) Reckefuss (36) Mizariene (28) Lancellotti (17) Butz (30) Takamura (29) Narayanan (19) Kang (14) Meluzin (20) Bussadori (21) RodriguezBailon (25) Saleh (35) Manovel (16) Saito (22)
21.950 18.300 1 9 .5 0 0 20.400 17.400 18.600 18.900 18.900 19.500 1 9 .6 0 0 20.600 20.900 20.900 15.900 17.400 20.600 20.300 21.900 16.000 20.000 22.000 22.100 20.210 1 9 . 05 0 19.840 17.280 22.280 19.900 19.662
0.313 0.380 0.762 0. 3 5 0 0.895 0.096 0.445 0.277 0.470 0.255 0.333 0.260 0.323 0.092 0 .0 8 9 0.217 0.335 0.354 0.660 0.600 0.416 0.492 0.171 0.557 0.448 0.254 0.366 0.781 0.377
0.098 0.145 0.580 0.123 0.801 0.009 0.198 0.077 0.221 0.065 0.111 0.068 0.104 0.008 0.008 0.047 0.113 0.126 0.436 0.360 0.173 0.242 0.029 0.310 0.201 0.065 0.134 0.611 0.142
21.336 17.555 18.007 19.714 15.646 18.411 18.028 18.356 18.578 19.101 19.948 20.390 20.267 15.719 17.226 20.175 19.642 21.205 14.706 18.824 21.185 21.136 19.875 17.959 18.962 16 . 7 82 21.564 18.368 18.923
22.564 70.052 19.045 48.141 20.993 25.605 21.086 58.274 19.154 19.442 1 8 . 78 9 1 9 2 . 8 9 9 19.772 42.475 19.444 68.145 20.422 41.450 20.099 76.996 21.252 61.895 21.410 80.385 21.533 64.756 16.081 172.549 17.574 196.259 21.0 2 5 9 5 .0 77 20.958 60.509 22.595 61.782 1 7 . 29 4 2 4 . 2 4 4 21.176 33.333 22.815 52.881 23.064 44.925 20.545 118.155 20.141 34.210 2 0 . 7 1 8 4 4. 2 9 2 17.778 68.034 22.996 60.947 21.432 25.466 20.402 52.121
Weight (Random) Relative Std weight Residual
.000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000
3.65 3.61 3.25 3.63 3.09 3.74 3.56 3.67 3.54 3.68 3.64 3.68 3.65 3.74 3.74 3.70 3.64 3.63 3.36 3.42 3. 58 3.52 3. 72 3.46 3.56 3.68 3.62 3.23 12.50
1.18 -0.70 -0.08 0.38 -1.07 -0.55 -0.39 -0.39 -0.08 -0.03 0.48 0.64 0.64 -1.97 -1.18 0.49 0.33 1.15 -1.81 0.17 1.19 1.23 0.29 -0.31 0.09 -1.23 1.34 0.12
25.00
Figure 2 Normal value of GLS. The forest plot of point estimates and confidence intervals also includes results for variance, used in the inverse variance correction. Study name
Mean and 95% CI
Statistics for each study Mean
Standard Lower Upper error Variance limit limit Z-Value P -Value
Weight Relative weight
Std Residual
Manovel (16)
23.18
0.57
0.33
22.05 24.31
40.35
.00
4.08
2.35
Imbalzano (34)
20.00
0.39
0.15
19.23 20.77
51.01
.00
8.76
-4.97 -0.63
Kouzu (27)
21.40
0.74
0.55
19.95 22.85
28.86
.00
2.45
Park (23)
21.20
0.58
0.34
20.06 22.34
36.30
.00
3.95
-1.15
Yip (32)
26.40
0.45
0.20
25 . 5 1 27 . 2 9
58.43
.00
6.59
10.40 -2.20
Mizariene (28)
20.90
0.45
0.20
20.01 21.79
46.22
.00
6.58
Takamura (29)
24.00
0.80
0.64
22.43 25.57
30.00
.00
2.10
2.70
Narayanan (19)
22.00
0.42
0.17
2 1 .1 8 2 2 . 8 2
52.88
.00
7.78
0.35
Kang (14)
21.80
0.94
0.88
19.96 23.64
23.21
.00
1.53
-0.06
Meluzin (20)
21.05
0.17
0.03
20.71 21.39 123.07
.00
46.01
-6.44
Bussadori (21)
24 . 9 3
1.34
1.80
22.30 27.56
18.58
.00
0.75
2.30
RodriguezBailon (25)
22.20
0.47
0.22
21.28 23.12
47.29
.00
6.11
0.75
Manovel (16)
27.17
0.82
0.67
25.57 28.77
33.21
.00
2.01
6.56
Saito (22)
27.80
1.02
1.04
25.81 29.79
27.33
.00
1.30
5.88
21.86
0.12
0.01
21.63 22.09 188.40
.00 0.00
15.00
30.00
Figure 3 Normal value of GCS. The forest plot of point estimates and confidence intervals also includes results for variance, used in the inverse variance correction. Second, the variability of GRS may reflect the limited amount of tissue to track in the short-axis view of the nonhypertrophied heart. Third, placement of the region of interest (ROI), which is user defined and may vary from one test to the next, may affect the strain amplitude. The impact of ROI size may have a more important role for GRS than GLS or GCS because the thickening gradient is most significant in the radial plane. Minor differences (of only 1 mm) in manual tracing of the ROI lead to capture of different contributions of endocardial and epicardial strain and yield different radial strain values. Finally, it is quite possible that this variation is vendor dependent, based on the use of different approaches to speckle-tracking calculations of strain by different vendors. For example, the use of fixed concentric ROI circles in the short-axis view may contribute to variation related to either underestimation or overestimation of the septal contribution to GRS if the septum is thicker or thinner than the free wall segments. This source of variation may be avoided by software that provides adjustable ROIs. Other intervendor differences for
strain calculations that may influence GRS include weighting of the endocardial interface versus myocardial tissue speckle patterns. Normal Range of GLS Previous studies have reported normal values of GLS from relatively small populations; most recruited patients with ‘‘normal’’ echocardiographic findings (with the risk of including some patients with occult pathology), and a few represented completely healthy individuals from the community (Table 1). Although 18.6 6 0.1% was proposed as an average value of GLS in one large study of healthy volunteers,9 the published values of GLS vary considerably from 15.9% to 22.1%.14,15 By consolidating data from diverse studies, this meta-analysis provides more representative estimates of normal GLS than are possible with individual studies. Although the determinants of strain have been sought on the basis of variations in patient characteristics, each of these studies was performed with a single type of software. We pooled these patient samples to
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Journal of the American Society of Echocardiography Volume 26 Number 2
Study name
Statistics for each study
Weight
Mean and 95% CI
Standard Lower Upper Mean error Variance limit limit Z-Value P -Value
Relative weight
Std Residual
Imbalzano (34)
54.50
2.24
5 . 02
50.11
58.89
24 . 3 3
.00
3.87
5.00
Kouzu (27)
40.30
2.04
4.15
3 6 . 31
44.29
19.79
.00
4.69
-1.62
Park (23)
34.50
1.33
1.77
31.89
37.11
25.94
.00
10.99
-7.19
Ho (24)
44.70
2.55
6.48
39.71
49.69
17.56
.00
3.00
0.47
Yip (32)
44.50
1.32
1.73
41.92
47.08
33.79
.00
11.22
0.79
Reckefuss (36)
48.20
1.15
1.32
45.95
50.45
41.91
.00
14.71
4.41 2.23
Mizariene (28)
47.00
1.62
2.62
43.83
50.17
29.03
.00
7.42
Takamura (29)
59.00
2.80
7.84
53.51
64.49
21.07
.00
2.48
5.60
Narayanan (19) 44.00
1.53
2.33
41.01
46.99
28.84
.00
8.36
0.33
Kang (14)
59.00
3.13
9.80
5 2. 8 6
65.14
18.85
.00
1.98
5.00
Meluzin (20)
4 1.24
0.88
0.78
39.51
42.97
46.62
.00
24.85
-2.97
6.43
-5.00
Saito (22)
35.10
1.74
3.03
31.69
3 8. 5 1
20.17
.00
43.52
0.44
0.19
42.65
44.38
98.68
.00 0.00
35.00
70.00
Figure 4 Normal value of GRS. The forest plot of point estimates and confidence intervals also includes results for variance, used in the inverse variance correction.
Table 2 Meta-regression results b (95% CI)
Age Male gender Body mass index Systolic blood pressure Frame rate Vendor (non-GE)
Figure 5 Publication bias. Funnel plot for studies of GLS. include a large number of patients from a variety of clinical settings. By consolidating these data, we sought to define the main sources of between-study variation of normal GLS. These results show systolic blood pressure to be an important determinant of strain. Although intervendor differences have been highlighted by several investigators, this appears to be less with longitudinal than radial strain and more with three-dimensional imaging, segmental evaluation, and strain rate.38-40 Intervendor variation of GLS has not been a uniform finding.16 Sources of Bias of GLS The meta-regression in our study showed that the effects of age, gender, body mass index, frame rate, and vendor were not significant determinants of variations among normal ranges of GLS. This should not be misconstrued to mean that these features do not influence strain; previous work has shown age41 and female gender15 to be associated with lower GLS. However, it does enable us to put the impact of vendor in the context of other common influences. Study Limitations This study had a number of limitations. First, meta-analysis has inherent limitations, not the least of which is that all studies are assumed to
0.01 ( 0.14 to 0.12) 0.01 ( 0.02 to 0.01) 0.56 ( 1.56 to 0.44) 0.43 (0.18 to 0.69) 0.04 ( 0.15 to 0.07) 2.82 ( 6.26 to 0.62)
P
.85 .33 .17 .01 .37 .08
have equal value. This may not necessarily be the case if there are different levels of expertise among individuals who have actually measured the strain. The heterogeneity of studies in meta-analyses is considered by some to be a limitation, although it is through this that the performance (and variability) of a test can be appreciated in different patient groups and sources of variation identified. For some parameters (e.g., body mass index), data were incomplete and variations were small, which may have prevented recognition of anticipated associations with strain. Although most of these studies of normal subjects did not report the absence of dysglycemia and raised brain natriuretic peptide (leading to the possibility of pathology contributing to variation), the balance of reported normal strain values appears evenly distributed above and below the mean, and we consider this to be a minor contributor to the normal range. It could nonetheless account for some variability. The strain curve contains both amplitude and timing information, and we have focused on strain magnitude as a potential marker of LV function. The use of strain to measure timing has been applied to dyssynchrony analysis,5 and radial strain may be superior to other strain vectors for this purpose. On the other hand, timing measurements were usually not mentioned in the reports used in this analysis of strain amplitude, and the rationale of the main clinical application (the prediction of response to biventricular pacing) remains controversial.42 For these reasons, we considered strain timing to be a separate topic. One study reported separate analyses from different software.16 In considering these substudies as individual data sources, patients were represented twice, but at the benefit of increasing the variety of 2D speckle-tracking methods. However, we excluded this study from the regression analysis. The contribution of equipment to variation in GLS may be viewed as controversial; after all, the American Society of Echocardiography
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and the European Association of Echocardiography have a joint task force to deal with equipment-based variation. Because the majority of studies (n = 23) used machines from one manufacturer, it is possible that there was insufficient heterogeneity to reflect the impact of this finding. Nonetheless, equipment-based variation in 2D strain is perhaps more subtle than is often portrayed; a recent review of this literature emphasized variation in regional strain and GRS but noted that no such variation was documented in three of five studies of GLS.43 Conclusions Normal GLS is 19.7% (95%CI, 20.4% to 18.9%), normal GCS is 23.3% (95% CI, 24.6% to 22.1%), and normal GRS is 47.3% (95% CI, 43.6% to 51.0%). Variations among different normal ranges seem likely to be associated with differences in systolic blood pressure in different patient groups, and at least in this analysis, this seemed to be a more important source of variation than vendor differences. In patients in whom follow-up scans are being performed, use of the same equipment is prudent, but the clinical application of strain also requires that careful attention be paid to blood pressure at the time of the study.
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