Download Normal ranges of left ventricular global longitudinal strain: a meta

Normal ranges of left ventricular
global longitudinal strain:
A meta-analysis of 2484 subjects
Teerapat Yingchoncharoen MD.
Shikar Agarwal MD. MPH.
Thomas H. Marwick MBBS. Ph.D. MPH.
Cleveland Clinic Foundation
Disclosure
No Conflict of Interest
ESC Aug 2012
Clinical use of myocardial strain
- Accurate and
reproducible
measurement of LV
systolic function
- Global or regional
- Strain and strain rate
- Facilitate echo
interpretation by lessexpert readers
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Clinical Application of GLS
• Assessment of subclinical LV dysfunction
•
•
•
•
•
Chemotherapy
Valvular heart diseases
Cardiomyopathies
Hypertensive heart disease
Heart transplant rejection
• Prognostic factors
• Heart failure
The routine application of myocardial strain in clinical
practice requires the definition of a normal range
Feigenbaum H, et al. Circ J 2012;76:1550
Potential influences on GLS
• Patient factors
•
•
•
•
Age
Gender
Ethnicity
Anthropometric
• Technical factors
• Hemodynamic factors
• Heart rate
• Blood pressure
• Cardiac factors
• LV size
• Wall thickness
• Equipment
• Signal processing
• Smoothing
Marwick TH, et al. JACC Cardiovas Imaging 2009;2:80-4
A2C GLS = -21.8
A4C GLS = -18.9
A3C GLS = -21.4
Average GLS = -20.7
A3C GLS = -19.1
A4C GLS = -17,6
A2C GLS = -18.0
Average GLS = -18.23
Marwick TH, et al. JACC Cardiovas Imaging 2009;2:80-4
Vendors and 2D STE
Author
year
N
Comparison
Results
Bansal M.
2008
30 VVI Vs EchoPac
(AFI)
AFI showed better correlation and
agreement with HARP MRI
Manovel A.
2010
28 EchoPac Vs Toshiba
Comparable
Biaggi P
2011
47 EchoPac Vs VVI
Differences in segmental peak strain
Koopman
2011
49 TomTec Vs EchoPac
Vs QLAB
Similar longitudinal/circumferential
strain, differences in radial and SR
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Study objectives
To perform a systematic review of the entire literature to
obtain a large dataset to :
1. Document the normal range of myocardial strain
2. Identify the role of vendor as a cause of variability
3. Identify the role of vendor relative to other causes of
variation.
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Methods
-
MEDLINE, EMBASE and the Cochrane Library database search
-
Key terms : strain, speckle tracking, echocardiography and left
ventricle
-
Up to date as August 8, 2011
-
Abstracts with no full-text, review articles, editorial comments and
letters to editor were excluded
-
The search strategy, study selection and analysis adhered to
QUOROM guidelines for meta-analysis.
Limited to adult human studies published in English
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Study Selection
Echo Lab
Normal Control
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Data Collection
• Clinical, echocardiographic and outcome data were extracted from
individual studies by one author (TY), verified by a second (TM) and
entered into an electronic database
• Data included group numbers, demographic, clinical and
echocardiographic data
• In situations where we believed that multiple articles were published
from a single dataset, the largest study was assessed
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Outcome Measures
• Mean global longitudinal strain was extracted from the text, tables or
graphs.
• Meta-regression analysis was performed by random-effect model
weighed by inverse variance in order to delineate the source of bias
of the data
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Statistical Analysis
• The primary outcomes comprised GLS and mean and 95%
confidence intervals (CI) were computed using random-effects
models.
• Between-studies heterogeneity was assessed using the Cochran Q
test (based on 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)
• General linear model was used to assess the associations of clinical
characteristics with normal strain measurements.
• Statistical analysis was performed using standard software
packages (STATA 10.0, College Station, TX and Comprehensive
meta-analysis (Biostat, Englewood, NJ) with two-tailed P-values and
P<0.05 considered significant.
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Results
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Patients Characteristics
Author
year
N
Control Selection
Disease studied
Lancellotti
2008
23
Normal control
Severe mitral regurgitation
Delgado
2008
20
Normal control
Coronary artery disease
Kang
2008
20
Healthy individuals
Untreated hypertension
Narayanan
2009
52
Normal control
Mild hypertensive heart disease
Meluzin
2009
14
Healthy volunteers
Idiopathic dilated cardiomyopathy
Marwick
2009
242
Healthy volunteers
Volunteers without evidence of CV disease
Bussadori
2009
30
Healthy volunteers
Normal adults and children
Saito
2009
46
Healthy volunteers
Comparison of 2D and 3D strain
Park
2010
38
Healthy control
Diastolic dysfunction
Ho
2010
50
Healthy control
Chemotherapy
Dalen1
2010
673
Healthy female
Healthy individuals
Dalen2
2010
623
Healthy male
Healthy individuals
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Patients Characteristics
Author
year
N
Control Selection
Disease studied
Manovel1
2010
28
Healthy subjects with software1
Comparison of different software
Manovel2
2010
28
Healthy subjects with software2
Comparison of different software
Rodriguez Bailon
2010
105
Healthy volunteers
Normal subjects
Marcus1
2011
25
Healthy 20-24 years
Healthy adult and pediatric cohort
Marcus2
2011
13
Healthy 25-29 years
Healthy adult and pediatric cohort
Marcus3
2011
13
Healthy 30-40 years
Healthy adult and pediatric cohort
Kouzu
2011
55
No CV disease
Left ventricular hypertrophy
Mizariene
2011
47
Healthy subjects
Aortic regurgitation
Takamura
2011
25
Normal subjects
Acute pulmonary embolism
Butz
2011
18
Normal subjects
LV hypertrophy
Syeda
2011
42
Healthy population
Heart transplantation
Yip
2011
60
Healthy subjects
HFNEF
Kusunose
2011
58
Healthy volunteers
Previous MI
Imbalzano
2011
51
Healthy subjects
Hypertension
Saleh
2011
82
Healthy individuals
Heart transplantation
Reckefuss
2011
144
Normal adults
Normal probands
ESC Aug 201220
Normal Ranges
Study name
Manovel1
Delgado
Kusunose
Imbalzano
Syeda
Marwick
Kouzu
Marcus 3
Park
Ho
Marcus 2
Marcus 1
Yip
Dalen2
Dalen1
Reckefuss
Mizariene
Lancellotti
Butz
Takamura
Narayanan
Kang
Meluzin
Bussadori
RodriguezBailon
Saleh
Manovel2
Saito
Statistics for each study
Mean
21.95
18.30
19.50
20.40
17.40
18.60
18.90
18.90
19.50
19.60
20.60
20.90
20.90
15.90
17.40
20.60
20.30
21.90
16.00
20.00
22.00
22.10
20.21
19.05
19.84
17.28
22.28
19.90
Mean and 95% CI
Weight
Relative
Std
weight
Residual
Standard Variance p-Value
error
0.31
0.10
0.00
0.38
0.14
0.00
0.76
0.58
0.00
0.35
0.12
0.00
0.89
0.80
0.00
0.01
0.00
0.00
0.44
0.20
0.00
0.28
0.08
0.00
0.47
0.22
0.00
0.25
0.06
0.00
0.33
0.11
0.00
0.26
0.07
0.00
0.32
0.10
0.00
0.09
0.01
0.00
0.09
0.01
0.00
0.22
0.05
0.00
0.34
0.11
0.00
0.35
0.13
0.00
0.66
0.44
0.00
0.60
0.36
0.00
0.42
0.17
0.00
0.49
0.24
0.00
0.17
0.03
0.00
0.56
0.31
0.00
0.45
0.20
0.00
0.25
0.06
0.00
0.37
0.13
0.00
0.78
0.61
0.00
0.04
0.03
0.01
0.03
0.01
98.12
0.02
0.05
0.02
0.06
0.04
0.06
0.04
0.48
0.52
0.09
0.04
0.03
0.01
0.01
0.02
0.02
0.14
0.01
0.02
0.06
0.03
0.01
-19.7
0.00
12.50
10.71
-0.77
1.19
5.16
-1.33
6.29
0.69
1.10
1.93
3.95
6.03
8.87
7.14
-29.31
-13.51
9.26
5.08
9.33
-3.93
2.34
8.19
7.13
9.45
0.82
2.78
-5.18
10.08
1.67
(95%CI -20.2 to -19.1)
25.00
ESC Aug 2012
Heterogeneity and Consistency
• Between-studies heterogeneity was evidenced by a Cochran Q test
of 1935 (p<0.0001), and inconsistency by an I2 value of 99.
• The GLS values showed between-studies heterogeneity and
inconsistency.
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Publication bias - Funnel plot
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Causes of variability
• Meta-regression analysis from 10 studies of factors affecting
systematic bias of GLS
• Age, male gender, BMI, frame rate and vendors were not
significantly associated with the variance of mean GLS
• Systolic blood pressure is an important source of variation between
studies.
Beta (95%CI)
p
Age
-0.01 (-0.14 to 0.12)
0.85
Male gender (%)
-0.01 (-0.02 to 0.01)
0.33
BMI
-0.56 (-1.56 to 0.44)
0.17
SBP
0.43 (0.18-0.69)
0.01
Frame rate
-0.04 (-0.15 to 0.07)
0.37
Vendor (Echopac) -2.82 (-6.26 to 0.62)
0.08
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Conclusion
• Normal strain is -19.7% + 0.28
• Vendor does not appear to account for variation between studies.
• Systolic blood pressure is an important source of variation between
studies.
ESC Aug 2012
Thank You For Your Attention
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Discussion
• This is the first meta-analysis that synthesized the controversial
data on systemic bias on variation of normal range of GLS (the
published value of GLS varied considerably from -15.9 to -22.1)
• Several previous studies reported the value of GLS as an individual
report with different patient characteristics and software.
• We pooled patient sample included in a large number of patients
with a wide variety of ages, most recruited consecutive patients
representing the spectrum of normal healthy individuals seen in
everyday practice.
• By consolidating data from numerous diverse studies, the metaanalysis provided estimates of normal GLS than those possible with
individual studies.
ESC Aug 2012
Discussion
• The meta-regression from our study showed that the effect of age,
gender, BMI, frame rate and vendors were not significantly affected
the mean value of GLS.
• This data may be contradicted to the previous knowledge that
ageing results in a decrease in global longitudinal strain.
• Gender was shown to affect the mean GLS as shown in one study
that the healthy female had a lower GLS compared to the healthy
male.
• The information about the vendors be limited when applied clinically
due to the small variation of vendors used in the meta-regression as
well as small numbers of studies that had the complete data and
eligible to be included for the meta-regression
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Limitation
• One study reported the analysis separately from the different
software and we chose to consider these substudies as individual
data sources.
• Although this strategy means that patients were represented twice,
it allows the variety of 2D speckle tracking method to be included.
• As the aim of the meta-analysis is to evaluate the normal value of
GLS nor patient characteristics, it was more important to include all
the different 2D speckle tracking analyses. However, we excluded
this study when studied the meta-regression analysis.
ESC Aug 2012