Normal ranges of left ventricular global longitudinal strain: A meta-analysis of 2484 subjects

Transcription

1 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

2 Disclosure No Conflict of Interest

3 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

4 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

5 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

6 A2C GLS = A4C GLS = A3C GLS = Average GLS = -20.7

7 A3C GLS = A4C GLS = -17,6 A2C GLS = Average GLS = Marwick TH, et al. JACC Cardiovas Imaging 2009;2:80-4

8 Vendors and 2D STE Author year N Comparison Results Bansal M VVI Vs EchoPac (AFI) Manovel A EchoPac Vs Toshiba Comparable AFI showed better correlation and agreement with HARP MRI Biaggi P EchoPac Vs VVI Differences in segmental peak strain Koopman TomTec Vs EchoPac Vs QLAB Similar longitudinal/circumferential strain, differences in radial and SR

9 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.

10 Methods - MEDLINE, EMBASE and the Cochrane Library database search - Key terms : strain, speckle tracking, echocardiography and left ventricle - Up to date as August 8, 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.

11 Study Selection Echo Lab Normal Control

12 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

13 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

14 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.

15 Results

16 Patients Characteristics Author year N Control Selection Disease studied Lancellotti Normal control Severe mitral regurgitation Delgado Normal control Coronary artery disease Kang Healthy individuals Untreated hypertension Narayanan Normal control Mild hypertensive heart disease Meluzin Healthy volunteers Idiopathic dilated cardiomyopathy Marwick Healthy volunteers Volunteers without evidence of CV disease Bussadori Healthy volunteers Normal adults and children Saito Healthy volunteers Comparison of 2D and 3D strain Park Healthy control Diastolic dysfunction Ho Healthy control Chemotherapy Dalen Healthy female Healthy individuals Dalen Healthy male Healthy individuals 20

17 Patients Characteristics Author year N Control Selection Disease studied Manovel Healthy subjects with software1 Comparison of different software Manovel Healthy subjects with software2 Comparison of different software Rodriguez Bailon Healthy volunteers Normal subjects Marcus Healthy years Healthy adult and pediatric cohort Marcus Healthy years Healthy adult and pediatric cohort Marcus Healthy years Healthy adult and pediatric cohort Kouzu No CV disease Left ventricular hypertrophy Mizariene Healthy subjects Aortic regurgitation Takamura Normal subjects Acute pulmonary embolism Butz Normal subjects LV hypertrophy Syeda Healthy population Heart transplantation Yip Healthy subjects HFNEF Kusunose Healthy volunteers Previous MI Imbalzano Healthy subjects Hypertension Saleh Healthy individuals Heart transplantation Reckefuss Normal adults Normal probands 20

18 Normal Ranges Study name Statistics for each study Mean and 95% CI Weight Relative Std Mean Standard error Variance p-value weight Residual Manovel Delgado Kusunose Imbalzano Syeda Marwick Kouzu Marcus Park Ho Marcus Marcus Yip Dalen Dalen Reckefuss Mizariene Lancellotti Butz Takamura Narayanan Kang Meluzin Bussadori RodriguezBailon Saleh Manovel Saito (95%CI to -19.1)

19 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.

20 Publication bias - Funnel plot

21 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 (-0.14 to 0.12) 0.85 Male gender (%) (-0.02 to 0.01) 0.33 BMI (-1.56 to 0.44) 0.17 SBP 0.43 ( ) 0.01 Frame rate (-0.15 to 0.07) 0.37 Vendor (Echopac) (-6.26 to 0.62) 0.08

22 Conclusion Normal strain is -19.7% Vendor does not appear to account for variation between studies. Systolic blood pressure is an important source of variation between studies.

23 Thank You For Your Attention

24 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 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.

25 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

26 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.