Normal Values of Left Ventricular Size and Function on Three-Dimensional Echocardiography: Results of the World Alliance Societies of Echocardiography Study

Addetia, Karima; Miyoshi, Tatsuya; Amuthan, Vivekanandan; Citro, Rodolfo; Daimon, Masao; Fajardo, Pedro Gutiérrez; Kasliwal, Ravi R; Kirkpatrick, James N.; Monaghan, Mark; Muraru, Denisa; Ogunyankin, Kofo O.; Park, Seung Woo; Ronderos, Ricardo; Sadeghpour, Anita; Scalia, G.; Takeuchi, Masaaki; Tsang, Wendy; Tucay, Edwin S.; Rodrigues, Ana Clara Tude; Zhang, Yun; Hitschrich, Niklas; Blankenhagen, Michael; Degel, Markus; Schreckenberg, Marcus; Mor‐Avi, Victor; Asch, Federico M.; Lang, Roberto M.; Prado, Aldo; Filipini, Eduardo; Kwon, Agatha; Hoschke-Edwards, Samantha; Afonso, Tânia Regina; Thampinathan, Babitha; Sooriyakanthan, Maala; Tong, Zhu; Wang, Zhilong; Wang, Yingbin; Yin, Long; Li, Shuang; Alagesan, R; Balasubramanian, Sowmya; Ananth, R.V.A.; Bansal, Manish; Badano, Luigi P.; Bossone, Eduardo; Vece, Davide Di; Bellino, Michele; Nakao, Tomoko; Kawata, Takayuki; Hirokawa, Megumi; Sawada, Naruhiko; Nabeshima, Yousuke; Yun, Hae-Ran; Hwang, Jiwon

doi:10.1016/j.echo.2021.12.004

Cited by 22 publications

(22 citation statements)

References 44 publications

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“…In comparison, mitral regurgitation was present in 39% of subjects < 50 years old, with 57% having tricuspid regurgitation, 22% pulmonary regurgitation, and none with aortic regurgitation in the same age group. This study showed that trivial to mild valve regurgitation is common amongst older individuals, and the prevalence increases with advancing age [50] .…”

Section: Valve Morphology and Functionmentioning

confidence: 57%

“…With respect to valvular function, Klein et al [50] studied the prevalence of valve regurgitation amongst healthy volunteers using color flow Doppler echocardiography. Among older participants (> 50 years), they reported a 58% prevalence of trivial to mild mitral regurgitation, 74% prevalence of tricuspid regurgitation, 11% prevalence of aortic regurgitation, and 41% prevalence of pulmonary regurgitation.…”

Section: Valve Morphology and Functionmentioning

confidence: 99%

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Changes in cardiac structure and function with aging

Agrawal¹,

Nagueh²

2022

JCA

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Aging is associated with progressive changes in cardiac structure and function. The prevalence of cardiovascular risk factors and disease also increases profoundly with advancing age. Therefore, understanding the spectrum of physiological changes in the aging heart is crucial for the identification and risk stratification of cardiovascular disease. In this review, we discuss echocardiographic features of age-related cardiac remodeling.

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Section: Valve Morphology and Functionmentioning

confidence: 57%

Section: Valve Morphology and Functionmentioning

confidence: 99%

Changes in cardiac structure and function with aging

Agrawal¹,

Nagueh²

2022

JCA

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“…Given that data for defining reference values for DHM are currently unavailable, we also analyzed the subgroup of healthy subjects in the present study. We found that the mean LV volumes assessed by DHM were larger than contemporary commercial 3DE automated ultrasound imaging systems values [42,43] and more similar to CMR volumes measures [44]. In clinical practice, the LV volume assessment differs greatly depending on whether the operator draws the boundary by the blood tissue interface (i.e., at the tip of the trabeculation) or by the compact myocardium.…”

Section: Discussionmentioning

confidence: 69%

Three-Dimensional Automated, Machine-Learning-Based Left Heart Chamber Metrics: Associations with Prevalent Vascular Risk Factors and Cardiovascular Diseases

Barbieri

Albini

Chiusolo

et al. 2022

JCM

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Background. Three-dimensional transthoracic echocardiography (3DE) powered by artificial intelligence provides accurate left chamber quantification in good accordance with cardiac magnetic resonance and has the potential to revolutionize our clinical practice. Aims. To evaluate the association and the independent value of dynamic heart model (DHM)-derived left atrial (LA) and left ventricular (LV) metrics with prevalent vascular risk factors (VRFs) and cardiovascular diseases (CVDs) in a large, unselected population. Materials and Methods. We estimated the association of DHM metrics with VRFs (hypertension, diabetes) and CVDs (atrial fibrillation, stroke, ischemic heart disease, cardiomyopathies, >moderate valvular heart disease/prosthesis), stratified by prevalent disease status: participants without VRFs or CVDs (healthy), with at least one VRFs but without CVDs, and with at least one CVDs. Results. We retrospectively included 1069 subjects (median age 62 [IQR 49–74]; 50.6% women). When comparing VRFs with the healthy, significant difference in maximum and minimum indexed atrial volume (LAVi max and LAVi min), left atrial ejection fraction (LAEF), left ventricular mass/left ventricular end-diastolic volume ratio, and left ventricular global function index (LVGFI) were recorded (p < 0.05). In the adjusted logistic regression, LAVi min, LAEF, LV ejection fraction, and LVGFI showed the most robust association (OR 3.03 [95% CI 2.48–3.70], 0.45 [95% CI 0.39–0.51], 0.28 [95% CI 0.22–0.35], and 0.22 [95% CI 0.16–0.28], respectively, with CVDs. Conclusions. The present data suggested that novel 3DE left heart chamber metrics by DHM such as LAEF, LAVi min, and LVGFI can refine our echocardiographic disease discrimination capacity.

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“…Guidelines and recommendations for LV chamber quantification using echocardiography state 3DE as the preferred method of volumetric assessment (over conventional 2DE), where available and feasible ( 39 ), in keeping with the advantage of 3DE in being able to circumvent the need for geometric assumptions. Recently published normative values stratified by age, sex, and ethnic groups by the World Alliance Societies of Echocardiography (WASE) ( 40 ) further endorses the use of 3DE for the assessment of LV chamber size and function. Nevertheless, 3DE has not yet been universally incorporated into standard clinical routine due to requiring specialized expertise in both acquisition and analysis, resulting in higher costs compared to 2DE.…”

Section: Discussionmentioning

confidence: 99%

MITEA: A dataset for machine learning segmentation of the left ventricle in 3D echocardiography using subject-specific labels from cardiac magnetic resonance imaging

Zhao

Ferdian²,

Talou³

et al. 2023

Front. Cardiovasc. Med.

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Segmentation of the left ventricle (LV) in echocardiography is an important task for the quantification of volume and mass in heart disease. Continuing advances in echocardiography have extended imaging capabilities into the 3D domain, subsequently overcoming the geometric assumptions associated with conventional 2D acquisitions. Nevertheless, the analysis of 3D echocardiography (3DE) poses several challenges associated with limited spatial resolution, poor contrast-to-noise ratio, complex noise characteristics, and image anisotropy. To develop automated methods for 3DE analysis, a sufficiently large, labeled dataset is typically required. However, ground truth segmentations have historically been difficult to obtain due to the high inter-observer variability associated with manual analysis. We address this lack of expert consensus by registering labels derived from higher-resolution subject-specific cardiac magnetic resonance (CMR) images, producing 536 annotated 3DE images from 143 human subjects (10 of which were excluded). This heterogeneous population consists of healthy controls and patients with cardiac disease, across a range of demographics. To demonstrate the utility of such a dataset, a state-of-the-art, self-configuring deep learning network for semantic segmentation was employed for automated 3DE analysis. Using the proposed dataset for training, the network produced measurement biases of −9 ± 16 ml, −1 ± 10 ml, −2 ± 5 %, and 5 ± 23 g, for end-diastolic volume, end-systolic volume, ejection fraction, and mass, respectively, outperforming an expert human observer in terms of accuracy as well as scan-rescan reproducibility. As part of the Cardiac Atlas Project, we present here a large, publicly available 3DE dataset with ground truth labels that leverage the higher resolution and contrast of CMR, to provide a new benchmark for automated 3DE analysis. Such an approach not only reduces the effect of observer-specific bias present in manual 3DE annotations, but also enables the development of analysis techniques which exhibit better agreement with CMR compared to conventional methods. This represents an important step for enabling more efficient and accurate diagnostic and prognostic information to be obtained from echocardiography.

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Normal Values of Left Ventricular Size and Function on Three-Dimensional Echocardiography: Results of the World Alliance Societies of Echocardiography Study

Cited by 22 publications

References 44 publications

Changes in cardiac structure and function with aging

Changes in cardiac structure and function with aging

Three-Dimensional Automated, Machine-Learning-Based Left Heart Chamber Metrics: Associations with Prevalent Vascular Risk Factors and Cardiovascular Diseases

MITEA: A dataset for machine learning segmentation of the left ventricle in 3D echocardiography using subject-specific labels from cardiac magnetic resonance imaging

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