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. 2022 Dec 12;11(24):7363.
doi: 10.3390/jcm11247363.

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

Andrea Barbieri  1 , Alessandro Albini  1 , Simona Chiusolo  1 , Nicola Forzati  1 , Vera Laus  1 , Anna Maisano  1 , Federico Muto  1 , Matteo Passiatore  1 , Marco Stuani  1 , Laura Torlai Triglia  1 , Marco Vitolo  1   2 , Valentina Ziveri  1 , Giuseppe Boriani  1
Affiliations

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

Andrea Barbieri et al. J Clin Med. .

Abstract

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.

Keywords: 3D echocardiography; artificial intelligence; cardiac chamber quantification; machine learning.

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Conflict of interest statement

G.B. received small speaker’s fees from Boston, Boehringer, Bayer, Daiichi-and Sankyo outside of the submitted work. The other authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
Three-dimensional (3D)-DHM echocardiographic metrics stratified according to the presence of VRFs and CVDs. LAEF = left atrial ejection fraction; LAVi = left atrial volume indexed, LVGFI = left ventricle global function index, M/V = left ventricular mass/left ventricular end-diastolic volume ratio, Svi = stroke volume indexed.
Figure 2
Figure 2
Associations between 3D-DHM echocardiographic metrics and vascular risk factors or prevalent cardiovascular disease. * Adjusted analysis for sex and age; EDVi = end-diastolic volume indexed; ESVi = end-systolic volume indexed; LAEF = left atrial ejection fraction; LAVi = left atrial volume indexed; LVEF = left ventricular ejection fraction, LVGFI = left ventricle global function index, LVM = left ventricular mass, Svi = stroke volume indexed.
See this image and copyright information in PMC

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