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
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
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 Equipment Signal processing Smoothing Hemodynamic factors Heart rate Blood pressure Cardiac factors LV size Wall thickness 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) Manovel A. 2010 28 EchoPac Vs Toshiba Comparable AFI showed better correlation and agreement with HARP MRI 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
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.
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 - Limited to adult human studies published in English - 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.
Study Selection Echo Lab Normal Control
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
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
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 (I 2 ; 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.
Results
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 20
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 20
Normal Ranges Study name Statistics for each study Mean and 95% CI Weight Relative Std Mean Standard error Variance p-value weight Residual Manovel1 21.95 0.31 0.10 0.00 0.04 10.71 Delgado 18.30 0.38 0.14 0.00 0.03-0.77 Kusunose 19.50 0.76 0.58 0.00 0.01 1.19 Imbalzano 20.40 0.35 0.12 0.00 0.03 5.16 Syeda 17.40 0.89 0.80 0.00 0.01-1.33 Marwick 18.60 0.01 0.00 0.00 98.12 6.29 Kouzu 18.90 0.44 0.20 0.00 0.02 0.69 Marcus 3 18.90 0.28 0.08 0.00 0.05 1.10 Park 19.50 0.47 0.22 0.00 0.02 1.93 Ho 19.60 0.25 0.06 0.00 0.06 3.95 Marcus 2 20.60 0.33 0.11 0.00 0.04 6.03 Marcus 1 20.90 0.26 0.07 0.00 0.06 8.87 Yip 20.90 0.32 0.10 0.00 0.04 7.14 Dalen2 15.90 0.09 0.01 0.00 0.48-29.31 Dalen1 17.40 0.09 0.01 0.00 0.52-13.51 Reckefuss 20.60 0.22 0.05 0.00 0.09 9.26 Mizariene 20.30 0.34 0.11 0.00 0.04 5.08 Lancellotti 21.90 0.35 0.13 0.00 0.03 9.33 Butz 16.00 0.66 0.44 0.00 0.01-3.93 Takamura 20.00 0.60 0.36 0.00 0.01 2.34 Narayanan 22.00 0.42 0.17 0.00 0.02 8.19 Kang 22.10 0.49 0.24 0.00 0.02 7.13 Meluzin 20.21 0.17 0.03 0.00 0.14 9.45 Bussadori 19.05 0.56 0.31 0.00 0.01 0.82 RodriguezBailon 19.84 0.45 0.20 0.00 0.02 2.78 Saleh 17.28 0.25 0.06 0.00 0.06-5.18 Manovel2 22.28 0.37 0.13 0.00 0.03 10.08 Saito 19.90 0.78 0.61 0.00 0.01 1.67-19.7 (95%CI -20.2 to -19.1) 0.00 12.50 25.00
Heterogeneity and Consistency Between-studies heterogeneity was evidenced by a Cochran Q test of 1935 (p<0.0001), and inconsistency by an I 2 value of 99. The GLS values showed between-studies heterogeneity and inconsistency.
Publication bias - Funnel plot
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
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.
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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.
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
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.