age-, body size- and gender-specific reference values for...

Age-, Body Size- and Gender-specific Reference Values for Right

Ventricular Volumes and Ejection Fraction by Three-dimensional

Echocardiography: A Multicenter Echocardiographic Study in 507 Healthy

Volunteers

Maffessanti et al: RV Reference Values by 3D Echocardiography

Francesco Maffessanti, PhD1*; Denisa Muraru, MD2*; Roberta Esposito, MD3; Paola Gripari,

MD1; Davide Ermacora, MD2; Ciro Santoro, MD3; Gloria Tamborini, MD1; Maurizio

Galderisi, MD3; Mauro Pepi, MD1; Luigi P. Badano, MD2

*Francesco Maffessanti and Denisa Muraru contributed equally to the study

1Centro Cardiologico Monzino IRCCS, Milan, Italy

2Department of Cardiac, Thoracic and Vascular Sciences, University of Padua, Padua, Italy

3Cardioangiology with CCU, Department of Translational Medical Sciences, Federico II

University Hospital, Naples, Italy

Correspondence to Francesco Maffessanti, PhD Centro Cardiologico Monzino, IRCCS Via Parea, 4 20138 Milan, Italy Phone: +39 02 5800 2011 Fax: +39 02 5800 2287 Email: [email protected] DOI: 10.1161/CIRCIMAGING.113.000706

Journal Subject Code: [31] Echocardiography

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2

List of Abbreviations

2D = Two-dimensional

3D = Three-dimensional

3DE = Three-dimensional echocardiography

BMI = Body mass index

BSA = Body surface area

CMR = Cardiac magnetic resonance

EF = Ejection fraction

EDV = End-diastolic volume

ESV = End-systolic volume

FAC = Fractional area change

LV = Left ventricular

RV = Right ventricular

SV = Stroke volume

TAPSE = Tricuspid annular plane systolic excursion

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Abstract

Background—Right ventricular (RV) volumes and ejection fraction (EF) vary significantly

with demographic and anthropometric factors, and are associated with poor prognosis in

several cardiovascular diseases. This multicenter study was designed to: a) establish the

reference values for RV volumes and EF using transthoracic 3D echocardiography (3DE); b)

investigate the influence of age, gender and body-size on RV anatomy; c) develop normative

equations.

Methods and Results—RV volumes (end-diastolic, EDV; end-systolic, ESV), stroke volume

(SV) and EF were measured by 3DE in 540 healthy adult volunteers, prospectively enrolled,

evenly distributed across age and gender. The relation of age, gender and body size

parameters were investigated using bivariate and multiple linear regression. Analysis was

feasible in 507 (94%) subjects (260 women, age 45±16 years, range 18-90). Age, gender,

height and weight significantly influenced RV volumes and EF. Gender effect was significant

(p<0.01), with RV volumes larger and EF smaller in men than in women. Older age was

associated with lower volumes (EDV, -5ml/decade; ESV, -3ml/decade; EF, -2ml/decade),

and higher EF (+1%/decade). Inclusion of body size parameters in the statistical models

resulted in improved overall explained variance for volumes (EDV, R2=0.43; ESV, R2=0.35;

SV, R2=0.30), while EF was unaffected. Ratiometric and allometric indexing for age, gender

and body size resulted in no significant residual correlation between RV measures and height

or weight.

Conclusions—The presented normative ranges and equations could help standardize the 3DE

assessment of RV volumes and function in clinical practice, considering the effects of age,

gender and body size.

Key Words: three-dimensional echocardiography; reference values; allometric equations;

right ventricular volumes; right ventricular ejection fraction

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Right ventricular (RV) volumes and ejection fraction (EF) are important determinants of

survival in patients with myocardial infarction,1 systolic heart failure,2-4 congenital heart

disease,5 and pulmonary arterial hypertension.6

Cardiac magnetic resonance (CMR) is the current gold standard for quantitation of RV

geometry and function, but its widespread use is limited by costs, time-consumption and

contraindications, resulting unsuitable for patient screening or monitoring on large scale.

One of the major breakthroughs of transthoracic three-dimensional echocardiography

(3DE) is the ability to measure RV volumes and EF, otherwise not feasible by standard 2D

echocardiography.7 However, recent recommendations for RV quantitation8 indicated that

limited normative data is currently available for 3DE and, despite significant differences by

age and gender for RV geometry and function were identified by CMR,9 no reliable age- or

gender-specific reference values can be recommended at present for 3DE. Paralleling the

findings obtained with CMR,9 we hypothesized that RV volumes and EF measured by 3DE

would vary according to age, gender and body size in healthy subjects. Accordingly, this

study was designed to:

1) identify 3DE reference values for RV volumes and EF from a large population-based

sample of healthy volunteers with a wide age range, prospectively studied in 3 centers, using

the same study protocol and different 3DE scanners;

2) analyze the relationship of RV parameters with age, gender and body size;

3) study intra-observer and inter-center reproducibility of RV volumes and EF;

4) develop normative equations for RV volumes and EF and validate them using similar

normative equations obtained by CMR.9

Methods

Healthy volunteers were prospectively enrolled in 3 Italian tertiary centers (C1, C2, C3)

having a large expertise in 3DE for RV quantification (>400 studies/year/center for both

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clinical and research purposes). Participating centers were asked to provide samples with a

fairly uniform distribution among genders and age (from 18 to 90 years). Subjects from one

participating center have been included in a previous publication.10

Population

Subjects were prospectively recruited among hospital employees, fellows in training, their

relatives and people screened for driving or working license. Criteria for recruitment

included: age 18 years, no history or symptoms of cardiovascular or lung disease, no

cardiovascular risk factors (i.e. arterial systemic hypertension, smoking, diabetes,

dyslipidemia), no ongoing or previous cardio- or vaso-active treatment, normal ECG and

physical examination. Risk factors were assessed from the medical files, when available

(lipid and glucose levels, history of diabetes and/or dyslipidemia), from subject self-reporting

(smoking habit, history of blood pressure levels and metabolic risk factors when otherwise

not available) and from physical assessment immediately preceding the echocardiographic

examination (blood pressure, weight and height). Exclusion criteria included: tricuspid

regurgitation more than mild, poor apical acoustic window, professional sport activity,

pregnancy, obesity (body mass index, BMI 30 kg/m2), no knowledge of serum lipid or

glucose levels at any time. Written consent was obtained from each participant. The study

protocol was approved by the institutional review board of each center.

Anthropometric measurements

Height and weight were measured before the echocardiographic study using calibrated

stadiometer and scale. Body surface area (BSA) was calculated using the DuBois formula,11

while BMI as the individual's body weight divided by the square of height.

Two-dimensional echocardiography

A complete standard M-mode, 2D and Doppler examination was performed to rule out any

silent abnormality. Peak velocity of tricuspid regurgitation signal and right atrial pressure,

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estimated from the dimensions and respiratory changes of inferior vena cava, were used to

calculate systolic pulmonary artery pressure.8 RV size was assessed by measuring end-

diastolic and end-systolic RV areas, and RV function was assessed by fractional area change

(FAC) and M-mode derived tricuspid annular plane systolic excursion (TAPSE).8 Left

ventricular (LV) volumes and EF were calculated using the biplane disc summation

method.12

Three-dimensional echocardiography

Acquisition. At the end of the 2D examination, an apical 3D dataset of the RV was obtained

using two commercially-available ultrasound systems: Vivid E9 with 4V probe (BT 11, GE

Vingmed, Horten, N), and Philips IE33 with X3-1 probe (Philips Medical Systems, Andover,

MA). RV full-volume datasets were obtained from 4 or 7 consecutive beats for an adequate

temporal resolution (26-40 volumes/second). Acquisitions were performed using second-

harmonic imaging, with adjustments in frequency, gain, contrast, depth, sector size and

respiratory maneuvers to optimize the visualization of the RV, particularly at the level of

anterior wall and outflow tract (Figure 1). Each acquisition was verified to rule out stitching

artifacts or incomplete RV visualization, and additional recordings were performed when

necessary.

Analysis. Offline analyses of 3D datasets were carried out by a single experienced

investigator in each center using a commercially-available 3DE software package (TomTec

4D-RV Analysis, Unterschleissheim, D) according to a standardized method (Supplemental

Methods). This vendor-independent software has been validated against CMR and its

workflow has been extensively described elsewhere.13-15 RV volumes were semi-

automatically computed throughout the entire cardiac cycle, from which end-diastolic (EDV)

and end-systolic (ESV) volumes were obtained, and stroke-volume (SV) and EF were

calculated (Figure 1).

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Statistical analysis

Normality distribution of continuous variables was assessed using the Kolmogorov-Smirnov

test. Data are presented as mean ± standard deviation, evaluated across age decades. Values

obtained in women and men were compared using unpaired t-test (Aim #1).

The relationship of RV measurements with age and anthropometric data was characterized

using bivariate and multivariate linear regressions (Aim #2). Multivariate analysis was

performed introducing different sets of predictors: model AGr included age and gender;

model AGBr, age, gender and BSA; model AGHWr considered age, gender, height and

weight. The latter model, considering height and weight separately, avoided the assumptions

proper of BSA, while allowing to account for differences in body size.9

The intra-observer and inter-center reproducibility were evaluated using Pearson’s

correlation coefficient, Bland-Altman analysis and coefficient of variation (Aim #3). To

assess intra-observer reproducibility, the main investigator of each center repeated the

analysis in a randomly chosen subset of 15 RV datasets, at least 1 month after the first

analysis. To assess the inter-center reproducibility, 30 RV datasets from one center have been

delivered to the other two centers and analyzed independently by the same three

investigators. Preliminary results revealed significant inter-center variability due to

systematic biases in RV volume measurements. Inter-center variability was significantly

reduced when a standardized method defined by joint consensus was used to re-analyze the

same 30 RV datasets (Supplemental Methods). As a consequence, all the datasets were

analyzed applying the jointly agreed standardized method and the obtained results have been

used for further analyses.

Normative equations for RV parameters were obtained using linear regression, after log-

transforming dependent and independent variables, to allow allometric scaling of RV

volumes and function (Aim #4).16 Gender was included as a dummy variable in the model,

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resulting in different intercepts for women and men. To validate the 3DE normative

equations, a split and sample approach was used (Supplemental Methods). A comparison

between the derived normative equations and those obtained in a similar population using

CMR,9 has been carried out applying the two sets of equations to our population. The

difference between the two predicted values was investigated as a function of the mean

expected value.

Bivariate linear regression was applied to study the residual correlation between indexed

RV volumes and body size parameters. RV indexes were defined as each RV volume divided

by BMI, BSA or by the patient-specific expected value when applying the multivariate

models. Indexes were obtained both via ratiometric and allometric approaches. Ratiometric

scaling approaches divide the variable of interest by a linear combination of body size

predictors (models AGr, AGBr, and AGHWr). Allometric indexing was obtained by dividing

each RV measurement by age raised to the power of the coefficient of log-transformed age,

by BSA raised to the power of log-transformed BSA, and by the exponential of the gender-

specific intercept (model AGBa).

Statistical analysis was performed using SPSS 17.0 (SPSS, Inc, Chicago, IL).

Results

A total of 585 volunteers fulfilled the inclusion criteria of this study. Of these, 45 (8%)

individuals were excluded from 3DE analysis because of poor apical acoustic window during

2D examination. Therefore, 540 subjects (240 enrolled at C1, 87 at C2, and 213 at C3) were

enrolled. Analysis of 3D RV volumes and EF was feasible in 507 out of the 540 enrolled

healthy volunteers. The overall feasibility of 3DE was similar among centers: 94% for C1,

93% for C2, and 93% for C3.

Clinical, echocardiographic and demographic characteristics of the study population are

summarized in Table 1. All parameters were normally distributed. The age of the study

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cohort ranged between 18 and 90 years. Women were slightly prevalent (51%). At least 37

subjects per age decade were included in the study (mean 85±27 subjects/age decade),

similarly distributed among decades (p=0.13). All anthropometric measurements were greater

in men than in women. Male gender was associated with larger LV volumes and RV areas,

and lower LV EF and RV FAC, while TAPSE, heart rate and RV systolic pressure were

similar in men and women (Table 1).

3D RV Volumes and Function. Reference values of 3D RV measurements, for the whole

study population and separately for each gender and age decade group, are shown in Table 2.

As expected, gender effect was significant in all age groups, with RV volumes larger in men

than in women. Overall, RV EF was lower in men than in women, even if this difference was

not significant in all age groups. Figure 2 depicts the values of RV EDV and ESV indexed to

BSA, showing that the significant differences between men and women persist even after

adjusting for body size.

Relationship with age, gender and body size. Results of the bivariate correlations between

RV measurements, age, and body size parameters (i.e. height, weight, BMI and BSA) are

listed in Table 3. RV volumes showed a progressive reduction with age, and positive

correlation with body size. Conversely, RV EF increased with age, and was inversely related

to body size. Among body size measurements, BMI showed the weakest correlation with RV

volumes, and no correlation with EF. For this reason, BSA was preferred to BMI in

multivariate analysis. Heart rate was inversely correlated with RV volumes (EDV, r=-0.16;

ESV, r=-0.17) and SV (r=-0.09), whereas it was directly correlated with EF (r=0.13).

Age and gender were independently associated with RV volumes and function (Table 4,

Model AGr). Specifically, there was an expected age-related decrement of 5 ml/decade for

EDV, 3 ml/decade for ESV and 2 ml/decade for SV Moreover, aging was associated with a

small ( 1%/decade), but significant increase in RV EF.

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The inclusion of BSA (Table 4, Model AGBr) improved the overall variance in RV

volumes explained by the model (up to 40% for the EDV). BSA was associated with EDV,

ESV and SV, with an expected increase of 5 ml, 2 ml and 3 ml respectively per each 0.1

m2 of BSA increase. The effects of age and gender persisted in Model AGBr, with

coefficients similar to those derived in Model AGr. Conversely, no significant improvement

was observed when including BSA in the regression model for RV EF.

Including height and weight separately in the model (Table 4, Model AGHWr) instead of

combining them in BSA (Table 4, Model AGBr), did not lead to significant improvements of

model R2. AGHWr and AGBr models resulted in similar values in terms of constant and age

and gender coefficients. For this reason, and also to avoid the possible issue of collinearity,

multivariate regressions on log-log transformed variables were performed using BSA, and not

height and weight separately.

Normative equations for 3DE RV volume and EF. Derived normative equations for 3DE

RV volumes, SV and EF are reported in Table 4. Contrary to general assumptions about

scaling effects, Table 5 shows that 3D RV volumes, SV and EF maintain significant residual

correlations with both height and weight, even after indexing to BSA or BMI. Ratiometric

scaling for RV parameters adjusted for age and BSA (AGBr), or age, height and weight

(AGHWr) resulted in RV volumes and EF values independent of body size. Similarly,

allometric scaling for normative equation (Table 4, Model ABa) resulted in negligible

residual correlations with height and weight.

Comparison with CMR-derived normative equations. Comparison with normative

equations derived from CMR9 applied to our study population, showed lower predicted RV

volumes and EF calculated using 3DE vs. CMR normative equations (Figure 3). The

differences between the CMR- and 3DE-predicted volumes showed a strong positive

correlation with their average. For every 10 mL increase of average 3DE and CMR RV

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volumes, there was an increase in the inter-modality difference of approximately 6 ml, 4 ml

and 5 ml, for EDV, ESV and SV, respectively (Figure 4). Conversely, the inter-modality

difference of expected EF showed no correlation with the average expected value, resulting in

a bias of 6.7% at Bland-Altman analysis (Figure 4, bottom panels).

Reproducibility. Intra-observer and inter-center reproducibility for RV volumes values are

summarized in Table 6. Intra-observer analysis showed good to excellent reproducibility (R2

ranges from 0.76 to 0.94, coefficient of variation between 2.0 and 8.6%), with negligible bias

and narrow limits of agreement (4.3 ml for ESV at C1, 11.5 for EDV at C3). Inter-center

measurements showed higher variability than intra-observer (R2 between 0.43 and 0.87,

coefficient of variation between 6.8 and 15.2%), with no significant biases (absolute values

between 2 and 9 ml) and acceptable limits of agreement between repeated measurements,

ranging between 13 and 33 ml.

Discussion

This is the first prospective multi-center study providing reference values for 3D RV volumes

and EF, separately for age and gender. To foster the clinical application of 3DE in individual

patients, a set of normative equations for RV volumes and EF was derived from this large

cohort of healthy adults, taking into account demographic and anthropometric parameters.

The main findings of this study can be summarized as follows: 1) the normal ranges of

absolute and BSA-indexed values of RV volumes and EF measured using 3DE are now

available separately for gender and age decades; 2) demographic and anthropometric

parameters were strong independent predictors of 3DE RV volumes at multivariate analysis;

3) RV EF showed a weak correlation with age and gender, and no correlation with body size;

4) normative allometric equations for 3D RV volumes and EF have been developed,

effectively indexing RV parameters for body size; 5) a strong agreement between 3DE and

CMR predicted normative values was found; 6) individual reference values for RV size and

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function parameters calculated using 3DE normative equations were systematically lower

than those calculated using CMR normative equations.

Quantitation of RV size and function with conventional echocardiography is challenging,

due to the anterior position of the RV in the chest, its complex asymmetric geometry and

highly trabeculated endocardial border, impossibility to simultaneously visualize both inflow

and outflow tracts and lack of realistic geometric models for volume calculation.17 3DE has

been demonstrated to have a good accuracy in measuring RV volumes compared to CMR.18

This, in conjunction with the availability of specifically designed software not requiring a

priori modeling or geometric assumptions,19 made 3DE a feasible, fast and accurate technique

for RV assessment in different clinical settings.13,20-22

Several biological processes and anatomic structures, including cardiovascular structural

and functional variables, scale with anthropometric and demographic data.23 However, while

scaling is commonly performed in pediatric medicine, allowing to eliminate the possible

confounding effect of the rapid somatic growth process, the practice of indexing is less used

in adult clinical cardiology.16 The goal of scaling cardiac structure is to eliminate, or at least

substantially decrease, the overlap between normal and abnormal ranges that could be

influenced by body size. In the present study, besides providing normative values of RV

volumes and ejection fraction stratified for gender and age, we also derived different models

and normative equations to index RV values according to demographic and anthropometric

parameters. Independently of the adopted model, aging was associated with a decrease of 5

ml/decade for EDV, 3ml/decade for ESV, and 2 ml/decade for SV. Conversely, EF increased

by 1%/decade. Body size was also significantly related with RV volumes. For every 0.1 m2

increase in BSA, there was an increase of 6 ml, 2 ml and 3 ml in RV EDV, ESV and SV,

respectively. Height and weight were also positively associated with RV volumes. However,

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their inclusion in the statistical model separately, and not merged into BSA, did not increase

the predictive power of the model.

We also demonstrated the importance of gender in all RV parameters: RV volumes were

larger and EF lower in men than in women. Albeit these dissimilarities can be partially

explained by different biometric characteristics between women and men, gender was a

significant determinant of RV size even after scaling for anthropometric variables.

From an empirical point of view, adequate model fitting should result in negligible

residual association between the values indexed by body size (e.g. BSA) and the independent

variables, specifically height and weight.24 Our results showed a residual correlation with

height and weight after ratiometric indexing (Table 5) suggesting that the assumption of

linearity between 3DE RV volumes and BSA is not completely satisfied, even after adjusting

for age and gender. Furthermore, indexing RV ESV to Model AGBr overcompensated the

association with anthropometric measures. Conversely, applying the allometric scaling led to

the calculation of RV volumes which were independent of age and body size. These findings

imply that simply indexing by BSA may be suboptimal, and the use of an exponential

allometric model could be more appropriate to scale RV volumes and SV. Alternatively,

ratiometric scaling for the Model AGHWr, considering height and weight separately and not

in fixed proportion as in BSA calculated by Du Bois and Du Bois formula,11 resulted in RV

volumes independent of body size. In addition, the appropriateness of normalization achieved

via scaling ratiometrically to BSA may be questionable from a theoretical point of view.

Indeed, the theory of similarity states that relative geometries determine in part the

relationships between body size variables. As RV volume is proportional to a length measure

raised to the third power, and BSA is proportional to a length raised to the second power, the

scaling relationship is dimensionally not consistent. Thus, indexing to BSA, despite widely

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used in clinical practice in all cardiac imaging modalities, may be suboptimal in certain

circumstances.16

The observed association between RV size, demographic and anthropometric parameters

has been previously reported in other studies, both using echocardiography and other imaging

modalities. In a large series of subjects with normal echocardiographic findings, D’Oronzio et

al.25 concluded that gender and BSA are important determinants of 2D echocardiographic RV

dimensions, and proposed to use gender-specific RV measures indexed to BSA for the

assessment of RV in the clinical routine. Of note, D’Oronzio et al reported only a weak

inverse association between aging and RV area, which may be due to the lower accuracy of

2D echocardiography in comparison with 3DE in assessing RV size.26

Our study corroborate and extend the results of Tamborini et al.10 to a larger population,

investigating the demographic and anthropometric determinants of RV morphology and

function in a multi-center setting. This is an important step towards the definition of clinically

applicable reference ranges. Indeed, systematic biases among laboratories have been reported

for left ventricular assessment by 3DE,24,27,28 and similar biases affecting RV measurements

could not be excluded a priori. In this view, a multi-center design is crucial for identifying

reliable normative values.

Nowadays, CMR is considered the gold standard for the assessment of RV volumes and

function, and different studies have reported RV reference values for this modality.29,30 Our

findings confirm previous studies reporting a slight systematic underestimation of RV

volumes by 3DE in comparison with CMR.26,31. The potential source of bias has been

documented in both in vivo and in vitro studies.18,32 Despite the bias in absolute values, the

role of aging, gender and anthropometric variables as determinants of RV size and function

demonstrated by Maceira et al.29 using steady-state free precession CMR are in complete

agreement with our findings. The comparison of normative non-linear equations obtained in

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e demographic and anthropometric determinants of RV mor

lti-center setting. This is an important step towards the definiti n

nce ran s. Indeed, s tematic biases amo laboratories have b

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our study with those presented by Kawut et al.9 obtained in a similar number of subjects,

represents an even more interesting parallelism between 3DE and CMR in the setting of RV

evaluation. Indeed (as depicted in Figure 4) normative surfaces of the present study obtained

applying the derived normative equations show similar behaviors, with exponential positive

influence of both BSA and age on RV EDV, ESV and SV. Also, from the comparison of

these two models we demonstrated that the bias between the predicted normal values by 3DE

and CMR is not constant, but has a rather strong positive linear correlation with the average

expected RV volume. Normative surfaces of RV EF were very similar, with a significant and

constant underestimation of 3DE versus CMR (approximately 7%), with narrow limits of

agreement.

From a clinical standpoint, the reliability of a quantitative method is more important that

its accuracy in comparison with the reference value obtained using a universally-accepted

“gold-standard”. Accordingly, for patient follow-up most clinicians would prefer to use a

precise method with a reproducible bias, despite less accurate, instead of using a highly

accurate method affected by large random error.33 For the first time, we evaluated the inter-

center reproducibility of 3DE RV volumes using different echocardiographic equipment and

the same post-processing software. Inter-institutional variability constitutes a serious threat

for measurement reproducibility and it is potentially the most serious source of bias in a

multicenter study. In agreement with previous study,34 we demonstrated that the definition of

a standardized method by joint consensus significantly improved the inter-center agreement.

We demonstrated that RV 3DE measurements are fairly robust and reproducible. However,

the limits of agreements of measurements among different centers were still relatively high

and, apart the described standardization of the image acquisition and post-processing steps,

further technological advances towards a fully-automated quantitation of cardiac chamber

volumes would be desirable to further improve the robustness of 3DE.

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Clinical implications

Currently, echocardiographic assessment of RV size and systolic function is performed using

different methods, including qualitative eye-balling, M-mode, 2D and Doppler-derived

measurements.8 3DE is currently gaining popularity, as a more accurate and reproducible

technique for RV evaluation in various conditions. The availability of reference values

obtained in a large population of healthy subjects is an important step towards the inclusion

of 3DE in everyday clinical practice. In addition, this study confirmed the relationship of RV

volumes and function with demographic and anthropometric parameters, as it was described

by CMR,9 increasing the confidence in 3DE. The use of normative equations for indexing RV

absolute measurements would allow a better discrimination of abnormal RV size, reducing

the confounding effect of age and biometric factors. This may have a considerable impact on

an early clinical diagnosis and on research studies in diseases associated with subtle RV

abnormalities.

The multi-center design of this study highlighted the importance of applying a

standardized analysis protocol for 3DE RV assessment, with obvious benefits on serial

follow-up evaluation and on inter-center communicability. In addition, the inclusion of multi-

vendor 3D acquisitions analyzed with the only software commercially available for RV 3DE

analysis ensures a widespread applicability of the proposed normative equations.

Limitations

The generalizability of our results may be limited by the homogeneity of our study cohort

with respect to race and ethnicity.35 Furthermore, reference values in our elderly cohort ( 70

years) should be considered cautiously, given the small size of this age group.

The use of newest 3DE transducers (X5, Philips Medical Systems, Andover, MA) with

better penetration and image resolution and those with single-beat full-volume acquisition

capability might have increased the feasibility of the 3DE analyses. However, higher

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diagnosis and on research studies in diseases associated w t

nter design of this stud highlighted the im rtance of

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penetration and image quality are more important when scanning real patients than

volunteers. In addition, single-beat full-volume acquisitions have either low temporal and

limited spatial resolution that may impair the accuracy of volume and ejection fraction

measurements.36

The absence of a comparison with a reference standard, such as CMR, could be regarded

as a limitation of this study. However, several studies comparing 3DE and CMR to assess RV

volumes and EF have documented the accuracy of 3DE and consistently reported smaller

volumes by 3DE than by CMR in normal subject, as well as in a variety of cardiac diseases.18

There is now enough evidence that the two techniques are different and that specific

references values are needed for each of them. Furthermore, the analysis algorithm used in

this study has been extensively validated against CMR.14,26,31,32 Moreover, limited availability

and costs of CMR, as well as ethical reasons prevented its use for studying healthy subjects

having no clinical indication for CMR examination. The inclusion of weight as a predictor

may be questionable as it may change rapidly in adults. However, the exclusion of

overweight subjects could have prevented this confounding factor.

Finally, despite subjects fulfilled all the inclusion criteria as healthy adults, we cannot

exclude the possibility of subclinical coronary artery disease, particularly in older subjects.

Conclusion

Reference values and normative equations for RV volumes and function obtained using 3DE

have been reported for the first time in a large multicenter population of healthy adults,

investigating the influence of demographic and anthropometric parameters. These results are

of significant clinical and research utility for the interpretation of 3D studies and for the

inclusion of this technique in everyday clinical practice.

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Disclosures

Denisa Muraru was supported by a scientific grant awarded by the European Association of

Echocardiography. Luigi P. Badano and Denisa Muraru have received equipment grants and

speakers’ honoraria from GE Vingmed. Luigi P. Badano is on the speaker’s bureau of GE

Vingmed.

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26. Gopal AS, Chukwu EO, Iwuchukwu CJ, Katz AS, Toole RS, Schapiro W, Reichek N. Normal values of right ventricular size and function by real-time 3-dimensional echocardiography: comparison with cardiac magnetic resonance imaging. J Am Soc Echocardiogr. 2007;20:445-455.

27. Lipshultz SE, Easley KA, Orav EJ, Kaplan S, Starc TJ, Bricker JT, Lai WW, Moodie DS, Sopko G, Schluchter MD, Colan SD. Reliability of multicenter pediatric chocardiographic measurements of left ventricular structure and function: the prospective P2C2 HIV study. Circulation. 2001;104:310-316.

28. Mor-Avi V, Jenkins C, Kuhl HP, Nesser HJ, Marwick T, Franke A, Ebner C, Freed BH, Steringer-Mascherbauer R, Pollard H, Weinert L, Niel J, Sugeng L, Lang RM. Real-time 3-dimensional echocardiographic quantification of left ventricular volumes. J Am Coll Cardiol Img. 2008;1:413-423.

29. Maceira AM, Prasad SK, Khan M, Pennell DJ. Reference right ventricular systolic and diastolic function normalized to age , gender and body surface area from steady-state free precession cardiovascular magnetic resonance. Eur Heart J. 2006;27:2879-2888.

30. Tandri H, Daya SK, Nasir K, Bomma C, Lima JAC, Calkins H, Bluemke DA. Normal reference values for the adult right ventricle by magnetic resonance imaging. Am J Cardiol. 2006; 98:1660-1664.

31. Kjaergaard J, Petersen CL, Kjaer A, Schaadt BK, Oh JK, Hassager C. Evaluation of right ventricular volume and function by 2D and 3D echocardiography compared to MRI. Eur J Echocardiogr. 2006;7:430-438.

32. Sugeng L, Mor-Avi V, Weinert L, Niel J, Ebner C, Steringer-Mascherbauer R, Bartolles R, Baumann R, Schummers G, Lang RM, Nesser H-J. Multimodality comparison of quantitative volumetric analysis of the right ventricle. J Am Coll Cardiol Img. 2010; 3:10-18.

33. Marwick TH. Consistency of myocardial deformation imaging between vendors. Eur J Echocardiogr. 2010;11:414-416.

34. Beerbaum P, Barth P, Kropf S, Sarikouch S, Kelter-Kloepping A, Franke D, Gutberlet M, Kuehne T. Cardiac function by MRI in congenital heart disease: impact of consensus training on interinstitutional variance. J Magn Reson Imaging. 2009;30:956-966.

35. Willis J, Augustine D, Shah R, Stevens C, Easaw J. Right ventricular normal measurements: time to index? J Am Soc Echocardiogr. 2012;25:1259-1267.

36. Macron L, Lim P, Bensaid A, Nahum J, Dussault C, Mitchell-Heggs L, Dubois-Randé JL, Deux JF, Gueret P. Single-beat versus multibeat real-time 3D echocardiography for

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AM, Prasad SK, Khan M, Pennell DJ. Reference right ventricularunction normalized to age gender and body surface area from sr

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assessing left ventricular volumes and ejection fraction: a comparison study with cardiac magnetic resonance. Circ Cardiovasc Imaging. 2010;3:450-455.



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Table 1. Clinical, echocardiographic and demographic characteristics of study population

All (n=507)

Women (n=260)

Men (n=247)

p-value

Age (years) 45±16 47±16 43±16 <0.01

Height (cm) 170±9 164±6 177±7 <0.01

Weight (kg) 69±12 61±9 77±10 <0.01

BMI (kg/m2) 23.8±3.3 23.0±3.4 24.7±3.0 <0.01

BSA (m2) 1.79±0.19 1.66±0.12 1.93±0.14 <0.01

Systolic blood pressure (mmHg) 121±15 117±14 125±14 <0.01

Diastolic blood pressure (mmHg) 73±10 71±10 75±9 <0.01

Heart rate (bpm) 68±11 69±11 67±11 0.02

RV systolic pressure (mmHg) 24.5±5.4 24.7±5.3 24.2±5.6 0.37

TAPSE (mm) 24.7±3.0 24.5±2.8 24.9±3.2 0.22

RV ED Area (cm2) 18.0±4.2 15.7±2.8 20.8±3.9 <0.01

RV ES Area (cm2) 9.2±2.6 7.9±1.8 10.8±2.6 <0.01

RV FAC (%) 49±8 50±7 48±8 <0.01

LV EDV (ml) 95±20 83±13 108±18 <0.01

LV ESV (ml) 36±11 30±8 42±11 <0.01

LV EF (%) 63±7 64±7 62±7 <0.01

Values reported as mean ± standard deviation.

p-value refers to unpaired Student’s t-test, Women vs Men.

RV systolic pressure gradient was feasible in 373 (74%) subjects.

Abbreviations: bpm, beats per minute; BMI, body mass index; BSA, body surface area; EDA, end-diastolic area; ESA, end-systolic area; EDV, end-diastolic volume; EF, ejection fraction; ESV, end-systolic volume; FAC, fractional area change; LV, left ventricular; RV, right ventricular; TAPSE, tricuspid annulus peak systolic excursion.

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Table 2. Normal ranges for 3D echocardiographic RV EDV, ESV and EF by gender and age decade.

Unless otherwise stated, all Women vs Men comparisons are significant (p<0.01)

RV EDV (ml) RV ESV (ml)

Age (years) n (Women,Men) All Women Men All Women Men

<30 114 (46,68) 105 (69,183) 88 (66,136) 122 (80,189) 46 (18,88)

35 (14,71) 51 (30,94)

30-39 98 (50,48) 92 (64,147) 85 (63,117) 114 (72,153) 36 (18,67)

31 (17,52) 45 (25,66)

40-49 98 (53,45) 90 (63,132) 82 (64,106) 101 (75,137) 35 (16,54)

30 (15,44) 40 (23,62)

50-59 91 (49,42) 90 (62,138) 79 (62,117) 101 (72,138) 33 (18,62)

29 (18,46) 37 (22,63)

60-69 69 (39,30) 85 (47,139) 79 (43,100) 98 (76,149) 32 (14,61)

30 (13,40) 37 (20,68)

70 37 (23,14) 77 (50, 125) 70 (51,86) 98 (64,129) 23 (11,53)

20 (12,32) 34 (18,54)

All 507 (260,247) 91 (61,150) 81 (58,120) 107 (74,163) 35 (16,72)

30 (15,52) 44 (22,80)

RV SV (ml) RV EF (%)

Age (years) n (Women,Men) All Women Men All Women Men

<30 114 (46,68) 63 (41,95) 56 (42,77) 69 (41,101) 58 (42,75)

60 (45,80) 56 (42,68)

30-39 98 (50,48) 60 (36, 93) 56 (38,72) 68(37,97) 61 (48,76)

63 (52,77) 60 (48,72)

40-49 98 (53,45) 56 (37,82) 51 (39,71) 63 (39,86) 63 (51,79)

65 (50,79) 61 (51,75)

50-59 91 (49,42) 56 (35,78) 50 (35,68) 63 (44,84) 62 (46,75)

62 (47,76) 62 (45,73), p=0.51

60-69 69 (39,30) 52 (28,85) 49 (25,61) 64 (48,88) 61 (50,79)

61 (53,75) 63 (50,78), p=0.75

70 37 (23,14) 54 (31,77) 49 (31,64) 61 (45,79) 68 (56,81)

71 (60,81) 65 (56,75)

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All 507 (260,247) 57 (36,87) 52 (35,72) 66 (40,91) 62 (47,77)

63 (49,79) 60 (45,75)

Data expressed median (5th, 95th percentile).

p-value refers to unpaired Student’s t-test, Women vs Men.

Abbreviations: EDV, end-diastolic volume; EF, ejection fraction; ESV, end-systolic volume; RV, right ventricular; SV, stroke volume.



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Table 3. Pearson correlation coefficients of 3D right ventricular parameters with age and body size

parameters

RV EDV RV ESV RV SV RV EF

Age -0.36* -0.37* -0.25* 0.24*

Height 0.54* 0.46* 0.49* -0.20*

Weight 0.47* 0.38* 0.44* -0.16*

BMI 0.17* 0.13* 0.17* -0.05

BSA 0.55* 0.45* 0.50* -0.19*

*: correlation is significant at the 0.01 level (two-tailed t test)

Abbreviations: BMI, body mass index; BSA, body surface area; EDV, end-diastolic volume; EF, ejection fraction; ESV, end-systolic volume; RV, right ventricular; SV, stroke volume. e.



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Table 4. Results of the multivariate linear regression for right ventricular measurements, adjusted for gender, and age (Model AGr, ratiometric), age and BSA

(Model AGBr, ratiometric, or Model AGBa, allometric), age, height and weight (Model AGHWr, ratiometric). Data presented as model coefficient ( ), 95%

confidence interval (CI), and Pearson’s correlation coefficient (r2)

RV EDV (ml) RV ESV (ml) RV SV (mL) RV EF (%)

(95% CI) r2 (95% CI) r2 (95% CI) r2 (95% CI) r2

Model AGr 0.34 0.29 0.22 0.10

Constant 108.2 (101.6, 114.7)* 46.6 (42.6, 50.6)* 61.6 (57.6, 65.5)* 58.0 (55.6, 60.4)*

Gender 25.9 (21.9, 30.0)* 13.3 (10.8, 15.7)* 12.7 (10.2, 15.1)* -3.4 (-4.9, -1.9)*

Age (years) -0.53 (-0.66, -0.40)* -0.34 (-0.41, -0.26)* -0.19 (-0.27, -0.12)* 0.12 (0.08, 0.17)*

Model AGBr 0.42 0.34 0.30 0.10

Constant 15.7 (-9.6, 41.0) 7.1 (-8.9, 23.2) 8.6 (-6.8, 23.9) 62.0 (52.0, 71.9)*

Gender 11.4 (5.9, 17.0)* 7.3 (3.7, 10.8)* 4.2 (0.8, 7.6)* -2.9 (-5.1, -0.7)*

Age (years) -0.55 (-0.67, -0.43)* -0.35 (-0.43, -0.27)* -0.20 (-0.27, -0.12)* 0.13 (0.08, 0.18)*

BSA (m2) 56.2 (41.5, 70.9)* 24.2 (14.9, 33.5)* 32.0 (23.1, 40.9)* -2.5 (-8.3, 3.2)

Model AGHWr 0.42 0.34 0.30 0.10

Constant -15.5 (-66.8, 35.9) -0.2 (-32.8, 32.4) -15.2 (-46.4, 15.9) 55.8 (35.7, 76.0)*

Gender 11.2 (5.4, 16.9)* 7.4 (3.7, 11.0)* 3.8 (0.3, 7.3)* -3.2 (-5.4, -0.9)*

Age (years) -0.53 (-0.66, -0.40)* -0.35 (-0.43, -0.27)* -0.18 (-0.26, -0.10)* 0.13 (0.08, 0.18)*

Height (cm) 0.55 (0.23, 0.88)* 0.19 (0.01, 0.40)* 0.36 (0.16, 0.55)* 0.03 (-0.10, 0.16)

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Weight (kg) 0.55 (0.32,0.77)* 0.25 (0.11, 0.40)* 0.29 (0.16, 0.43)* -0.05 (-0.14, 0.04)

Model AGBa 0.43 0.35 0.28 0.11

Constant 4.79 (4.56, 5.02)* 4.19 (3.80, 4.18)* 4.01 (3.76, 4.26)* 3.83 (3.67, 3.98)*

Gender 0.11 (0.06, 0.17)* 0.18 (0.09, 0.27)* 0.07 (0.01, 0.13)* -0.05 (-0.08, -0.01)*

Age (years) -0.24 (-0.29, -0.19)* -0.39 (-0.47, -0.30)* -0.14 (-0.20, -0.09)* 0.10 (0.06, 0.13)*

BSA (m2) 1.02 (0.77, 1.28)* 1.23 (0.80, 1.66)* 0.94 (0.67, 1.22)* -0.08 (-0.25, 0.09)

Normative equations (ABGa)

Women 120.4·Age-0.24·BSA1.02 66.2·Age-0.39·BSA1.23 55.2·Age-0.14·BSA0.94 45.9·Age0.10

Men 134.9·Age-0.24·BSA1.02 79.3·Age-0.39·BSA1.23 59.0·Age-0.14·BSA0.94 43.8·Age0.10

*: p<0.01 versus null coefficient. Abbreviations: BSA, body surface area; EDV, end-diastolic volume; ESV, end-systolic volume; EF, ejection fraction; SV, stroke volume; RV, right ventricular.

Indexed RV measures can be obtained by applying the following formulas:

AGr: RV index = RV parameter/(constantF/M + Age · Age)

AGBr: RV index = RV parameter/(constantF/M + Age · Age + BSA · BSA)

AGHWr: RV index = RV parameter/(constantF/M + Age · Age + BSA · Height + Weight · Weight)

AGBa: RV index = RV parameter/(constantF/M · Age Age · BSA BSA)

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y surface arrrrreaeaeaeaea; ; ; ; EDEDEDEDEDV,V,V,V,V, eeeendnddndnd-dddddiaiaiaiaiaststststs olololloliciciccic vvvolololololumumumumume;e;e;e;e; EEEEESVSVSVSVSV, end-systo

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Table 5. Pearson correlation coefficients for indexed right ventricular parameters with height and

weight

Height Weight

RV EDV

RV EDVAbs 0.54* 0.47*

RV EDVBSA 0.29* 0.16*

RV EDVBMI 0.52* 0.12*

RV EDVAGBr 0.00 (p=0.92) -0.02 (p=0.59)

RV EDVAGHWr -0.02 (p=0.63) -0.01 (p=0.81)

RV EDVAGBa 0.02 (p=0.62) 0.01 (p=0.88)

RV ESV

RV ESVAbs 0.46* 0.38*

RV ESVBSA 0.29* 0.18*

RV ESVBMI 0.45* 0.16*

RV ESVAGBr -0.02 (p=0.60) -0.02 (p=0.68)

RV ESVAGHWr -0.04 (p=0.38) -0.02 (p=0.61)

RV ESVAGBa -0.01 (p=0.80) -0.01 (p=0.89)

RV SV

RV SVAbs 0.49* 0.44*

RV SVBSA 0.18* 0.07 (p=0.15)

RV SVBMI 0.45* 0.04 (p=0.37)

RV SVAGBr 0.01 (p=0.82) -0.03 (p=0.56)

RV SVAGHWr -0.01 (p=0.83) 0.00 (p=0.96)

RV SVAGBa 0.04 (p=0.41) 0.00 (p=0.95)

RV EF

RV EFAbs -0.20* -0.16*

V ESV 0 46* 0 38*

V

V

V

V ESVSVSVSVS AbsAbsAbsAbsAbs 0.46* 0.38*

V ESVBSA 0.2.229* 0.18*8*8*8*8*

V ESVBMI 0.45* 0.16*

VV EESSVSVAGBBrr -0-0-000.0.0.022222 (p(p(p(p(p=0=0=00.6.6.6.6.60)0)0)00 -0-0-000.0.0.00.02222 (p(p(pp(p=0=0=000.6.6.6. 8)8)8)



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Abbreviations: Abs, absolute value; BMI, body mass index; BSA, body surface area; AGBa, adjusted for age and BSA (allometric); AGBr, adjusted for age, gender and BSA (ratiometric); AGHWr, adjusted for age, gender, height and weight (ratiometric); EDV, end-diastolic volume; EF, ejection fraction; ESV, end-systolic volume; RV, right ventricular, SV, stroke volume.

*: correlation is significant at the 0.01 level (2-tailed t-test).

RV EFBSA -0.59* -0.63*

RV EFBMI -0.17* -0.63*

RV EFAGBr 0.02 (p=0.70) -0.01 (p=0.83)

RV EFAGHWr 0.00 (p=0.95) 0.00 (p=0.99)

RV EFAGBa 0.03 (p=0.52) -0.01 (p=0.84)



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Table 6. Results of reproducibility analysis, evaluated using Pearson correlation coefficient (r2),

Bland-Altman analysis (BA) and coefficient of variation (CV) between repeated measurements

End-Diastolic Volume End-Systolic Volume

r2 BA (ml) CV (%) r2 BA (ml) CV (%)

Intra-operator C1 0.94 0.6±5.1 2.0 0.92 0.7±4.3 5.5

C2 0.96 0.4±5.8 3.8 0.91 1.2±5.2 6.8

C3 0.91 0.3±11.5 4.6 0.76 1.5±8.5 8.6

Inter-center C1vsC2 0.58 -9±33 12.1 0.60 -3±13 12.3

C1vsC3 0.87 -3±15 6.8 0.63 -5±13 15.2

C2vsC3 0.58 6±33 12.1 0.43 -2±17 14.1

Values expressed as bias±1.96 standard deviation.

Abbreviations: BA, Bland-Altman analysis; CV, coefficient of variation.

Bland-Altman analysis; CV, coefficient of variation.Blaaandndndndnd-A-A-A-A-Altltltlltmamamamaman n nn n analysis; CV, coefficiennnnnt t tt t ofoooo variation.



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Figure Legends

Figure 1. Example of the three-dimensional reconstruction of the right ventricle: the 3D

dataset acquired from an apical view, specifically adapted to better visualize the right

ventricle, as seen in a cut-plane mimicking a 4-chamber view (A) and in a multi-planar short

axis view, useful to confirm the inclusion of the endocardial borders (B); the rough model

obtained immediately after manual initialization of the endocardial wall projected on the

sagittal, 4-chamber and coronal views (C), and the obtained 3D model of the right ventricle,

depicted in its systolic configuration together with the end-diastolic wireframe model and the

volume curve (D).

Figure 2. Right ventricular (RV) end-diastolic (EDVi, left panel) and end-systolic (ESVi,

right panel) volume indexed to body surface area (BSA); values are shown as mean±SD,

separately for women and men. The numbers indicate the number of patients in each age

decade (women, men).

Figure 3. Comparison between predicted values obtained by applying to our study population

the normative equations derived using 3D echocardiography and those obtained, in the same

population, by applying normative equations by Kawut et al.9 derived using cardiovascular

magnetic resonance (gray surface). Scatter plot (bottom) represents the difference between

the predicted values ( CMR-3DE) as a function of the average predicted value. From top to

bottom, left to right: right ventricular end-diastolic (EDV), end-systolic (ESV) and stroke

(SV) volumes, and ejection fraction (EF). Pearson correlation coefficient (R2) between

EDVCMR-3DE and EDVavg.

y

ume indexed to body surface area (BSA); values are shown a

omen and men. The numbers indicate the number of atients

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Figure 4. Comparison between predicted values obtained by applying to our study population

the normative equations derived using 3D echocardiography (red surface) and those obtained,

in the same population, by applying normative equations by Kawut et al.9 derived using

cardiovascular magnetic resonance (gray surface). Normative surfaces are shown as a

function of body surface area (BSA) and age, separately for women and men.



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Figure 2

<30 30-39 40-49 50-59 60-69 0

20

40

60

80

100

<30 30-39 40-49 50-59 60-69 0

10

20

30

40

50 RV EDVi RV ESVi

Age (years) Age (years)

Volu

me/

BSA

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2 )

Volu

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Women Men

114 (46,68) 98

(50,48) 98

(53,45) 91

(49,42) 69 (39,30)

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Mean EDV (ml)

EDV C

MR-

3DE(m

L)

Women Men

75 100 125 150 175 200

20

40

60

80

100R2=0.96, p<0.01

CMR-3DE(ml)=-21.3+0.53·EDVavg

0 25 50 75 100

-20Mean ESV (ml)

ESV C

MR-

3DE(m

l)

Women Men

-10

10

20

30

40R2=0.94, p<0.01

CMR-3DE(ml)=-9.5+0.42·ESVavg

025 50 75 1000

0

Mean SV (ml)

SVCM

R-3D

E(ml)

Women Men

20

40

60

80R2=0.85, p<0.01

CMR-3DE(ml)=-9.4+0.55·SVavg

40 60 80 12020 100 550

Mean EF (%)

EFCM

R-3D

E(%)

Women Men

60 65 70 75

2

4

6

8

10R2=0.003, p<0.79bias=6.7%, LOA=[4.8, 8.4]

Figure 3

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Santoro, Gloria Tamborini, Maurizio Galderisi, Mauro Pepi and Luigi P. BadanoFrancesco Maffessanti, Denisa Muraru, Roberta Esposito, Paola Gripari, Davide Ermacora, Ciro

Study in 507 Healthy VolunteersEjection Fraction by Three-dimensional Echocardiography: A Multicenter Echocardiographic

Age-, Body Size- and Gender-specific Reference Values for Right Ventricular Volumes and

Print ISSN: 1941-9651. Online ISSN: 1942-0080 Copyright © 2013 American Heart Association, Inc. All rights reserved.

TX 75231is published by the American Heart Association, 7272 Greenville Avenue, Dallas,Circulation: Cardiovascular Imaging

published online June 27, 2013;Circ Cardiovasc Imaging.

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S1

SUPPLEMENTAL MATERIAL

Supplemental Methods

Reproducibility and quality control

An investigator meeting was hold to perform a joint 3DE analysis on random RV datasets, aiming at

identifying the potential sources of bias, leading to weak inter-center reproducibility, and to harmonize

the methodological workflow among the three involved institutes. Several methodological details were

agreed as in the following:

- selection of end-diastolic and end-systolic frames to be carried out manually;

- software sensitivity for automated identification of endocardial surface to be kept constant (low -

25%);

- endocardium should be positioned 1-2 mm outside the visible interface, in order to include trabeculae,

papillary muscles and moderator band in the RV cavity;

- special attention should be paid at the position of tricuspid leaflet hinges by observing the cine RV

images in the 4-chamber and coronal views, in order to accurately capture the RV longitudinal

shortening;

- to not extend the manually-traced RV outflow tract by interpolation beyond the visible endocardium

(i.e. outside the acquired volume);

- manual editing should be avoided whenever possible; re-initialization of RV endocardial contour and

subsequent automated processing should be performed instead.

The results of inter-center reproducibility analysis, before and after the harmonization of the

methodological workflow, are shown in Table S1.

S2

Statistical Analysis

Inter-center differences in subject characteristics were assessed using independent samples Kruskal-

Wallis test (Table S2).

Sensitivity analysis for normative equations was performed using a split and sample approach: after

random sub-sampling, the dataset was split into training and validation data subsets; model was fit on

the training data and the normality cut-off, determined as the lower/upper normality values (5th

/95th

percentile) in the training data, was applied to the validation subset. A prevalence of abnormal RV of

approximately 5% was considered as adequate fit. Split and sample analysis has been repeated 10 times

and the results are listed in Supplemental Tables S3, S4, S5 and S6.

S3

Supplemental Tables

Table S1. Comparison between inter-center reproducibility before and after investigator meeting and

agreement on a workflow of analysis, evaluated using Pearson correlation coefficient (r2) and Bland-Altman

analysis between repeated measurements.

EDV (ml) ESV (ml)

Inter-center reproducibility C1vsC2 -11±27 (0.76) -9±16 (0.70)

(before workflow agreement) C1vsC3 -2±37 (0.37) -1±16 (0.38)

C2vsC3 9±41 (0.44) 9±21 (0.43)

Inter-center reproducibility C1vsC2 -9±33 (0.58) -3±13 (0.60)

(after workflow agreement) C1vsC3 -3±15 (0.87) -5±13 (0.63)

C2vsC3 6±33 (0.58) -2±17 (0.43)

Values expressed as bias±1.96 SD (r2).

S4

Table S2. Inter-center differences in subject characteristics assessed using independent samples Kruskal-Wallis

test.

p-value

Age (years) <0.01

Height (cm) 0.096

Weight (kg) 0.194

BMI (kg/m2) 0.230

BSA (m2) 0.083

Systolic blood pressure (mmHg) 0.054

Diastolic blood pressure (mmHg) 0.253

Heart rate (bpm) 0.384

RV systolic pressure (mmHg) 0.542

TAPSE (mm) 0.810

RV ED Area (cm2) 0.810

RV ES Area (cm2) 0.414

RV FAC (%) 0.113

LV EDV (ml) 0.753

LV ESV (ml) 0.857

LV EF (%) 0.905

S5

Table S3. Split and sample (10 repetitions) sensitivity analysis for the end-diastolic volume normative equation.

n Constant Gender Age BSA Prevalence (%)

244 4.75 (4.40, 5.10) 0.10 (0.03, 0.18) -0.23 (-0.31, -0.16) 1.06 (0.70, 1.42) 6.6

231 4.70 (4.39, 5.01) 0.12 (0.05, 0.20) -0.19 (-0.26, -0.12) 0.85 (0.47, 1.23) 5.0

245 4.79 (4.46, 5.12) 0.13 (0.06, 0.21) -0.22 (-0.30, -0.15) 0.89 (0.53, 1.26) 5.3

241 4.98 (4.66, 5.30) 0.20 (0.12, 0.28) -0.25 (-0.32, -0.17) 0.63 (0.25, 1.00) 5.4

234 4.72 (4.36, 5.08) 0.12 (0.03, 0.20) -0.23 (-0.31, -0.16) 1.12 (0.73, 1.51) 5.6

242 4.71 (4.39, 5.02) 0.08 (0.01, 0.15) -0.24 (-0.31, -0.17) 1.19 (0.83, 1.55) 4.1

259 4.91 (4.56, 5.26) 0.11 (0.03, 0.19) -0.29 (-0.36, -0.21) 1.13 (0.77, 1.48) 4.6

246 4.76 (4.45, 5.08) 0.15 (0.07, 0.23) -0.23 (-0.30, -0.71) 1.02 (0.67, 1.38) 2.8

249 4.58 (4.25, 4.91) 0.04 (-0.03, 0.12) -0.22 (-0.29, -0.15) 1.35 (0.99, 1.70) 5.2

248 4.87 (4.52, 5.21) 0.15 (0.07, 0.24) -0.24 (-0.31, -0.16) 0.84 (0.47, 1.22) 4.8

Data presented as coefficient/exponent (95% confidence interval).

S6

Table S4. Split and sample (10 repetitions) sensitivity analysis for the end-systolic normative equation.


244 4.11 (3.56, 4.66) 0.13 (0.01, 0.25) -0.34 (-0.46, -0.22) 1.17 (0.60, 1.74) 4.1

231 3.92 (3.36, 4.48) 0.16 (0.02, 0.30) -0.33 (-0.45, -0.20) 1.32 (0.65, 2.00) 4.8

245 4.21 (3.67, 4.75) 0.17 (0.05, 0.29) -0.37 (-0.49, -0.25) 1.08 (0.49, 1.66) 4.3

241 4.47 (3.96, 4.98) 0.28 (0.16, 0.41) -0.40 (-0.51, -0.28) 0.72 (0.12, 1.31) 5.8

234 4.23 (3.64, 4.83) 0.20 (0.07, 0.34) -0.38 (-0.51, -0.26) 1.15 (0.52, 1.78) 6.0

242 4.18 (3.63, 4.72) 0.11 (-0.01, 0.24) -0.41 (-0.53, -0.29) 1.46 (0.85, 2.07) 4.1

259 4.52 (3.97, 5.07) 0.21 (0.09, 0.33) -0.45 (-0.57, -0.33) 1.08 (0.52, 1.65) 3.1

246 4.13 (3.59, 4.67) 0.23 (0.09, 0.36) -0.37 (-0.49, -0.25) 1.20 (0.60, 1.81) 4.4

249 3.88 (3.30, 4.47) 0.09 (-0.04, 0.23) -0.36 (-0.48, -0.23) 1.68 (1.06, 2.30) 4.8

248 4.19 (3.63, 4.75) 0.26 (0.13, 0.40) -0.36 (-0.48, -0.23) 0.98 (0.36, 1.59) 4.8


S7

Table S5. Split and sample (10 repetitions) sensitivity analysis for the stroke volume normative equation.


244 4.03 (3.65, 4.41) 0.08 (0.00, 0.16) -0.71 (-0.25, -0.09) 1.03 (0.64, 1.42) 3.3

231 4.04 (3.70, 4.37) 0.09 (0.01, 0.18) -0.10 (-0.18, -0.03) 0.62 (0.22, 1.02) 3.9

245 4.04 (3.68, 4.39) 0.10 (0.02, 0.18) -0.14 (-0.22, -0.06) 0.84 (0.45, 1.23) 4.1

241 4.17 (3.61, 4.53) 0.14 (0.05, 0.23) -0.16 (-0.24, -0.07) 0.64 (0.21, 1.06) 7.9

234 3.83 (3.45, 4.21) 0.06 (-0.03, 0.14) -0.13 (-0.21, -0.05) 1.17 (0.76, 1.57) 3.4

242 3.88 (3.55, 4.21) 0.05 (-0.02, 0.13) -0.13 (-0.20, -0.05) 1.08 (0.71, 1.45) 3.3

259 3.99 (3.62, 4.36) 0.05 (-0.03, 0.13) -0.18 (-0.26, -0.10) 1.19 (0.81, 1.57) 5.4

246 4.03 (3.68, 4.38) 0.11 (0.02, 0.19) -0.15 (-0.23, -0.08) 0.96 (0.57, 1.35) 3.3

249 3.84 (3.50, 4.18) 0.01 (-0.07, 0.09) -0.12 (-0.20, -0.05) 1.18 (0.82, 1.54) 4.0

248 4.14 (3.76, 4.52) 0.08 (-0.01, 0.17) -0.16 (-0.24, -0.08) 0.81 (0.39, 1.22) 6.5


S8

Table S6. Split and sample (10 repetitions) sensitivity analysis for the ejection fraction normative equation.


244 3.88 (3.66, 4.10) -0.03 (-0.07, 0.02) 0.07 (0.02, 0.11) -0.03 (-0.25, 0.20) 4.9

231 3.95 (3.72, 4.17) -0.03 (-0.09, 0.02) 0.09 (0.04, 0.14) -0.23 (-0.50, 0.04) 5.2

245 3.85 (3.64, 4.06) -0.03 (-0.08, 0.02) 0.08 (0.04, 0.13) -0.05 (-0.28, 0.18) 4.4

241 3.79 (3.58, 4.00) -0.06 (-0.11, -0.01) 0.09 (0.04, 0.14) -0.01 (-0.24, 0.25) 4.6

234 3.71 (3.49, 3.94) -0.06 (-0.11, -0.01) 0.11 (0.06, 0.15) 0.05 (-0.19, 0.28) 3.4

242 3.78 (3.57, 3.99) -0.02 (-0.07, 0.02) 0.11 (0.06, 0.16) -0.11 (-0.35, 0.13) 5.0

259 3.69 (3.47, 3.91) -0.06 (-0.11, -0.01) 0.11 (/0.06, 0.16) 0.06 (-0.16, 0.29) 3.5

246 3.87 (3.65, 4.09) -0.05 (-0.10, 0.01) 0.08 (0.03, 0.13) -0.06 (-0.31, 0.18) 4.5

249 3.86 (3.63, 4.09) -0.03 (-0.08, 0.02) 0.10 (0.05, 0.15) -0.16 (-0.40, 0.08) 6.0

248 3.88 (3.65, 4.11) -0.07 (-0.13, 0.13) 0.08 (0.03, 0.13) -0.04 (-0.29, 0.21) 4.4


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