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Thoracic Aorta Diameter Calculation by Artificial Intelligence Can Predict the Degree of Arterial Stiffness

Background: Arterial aging is characterized by decreased vascular function, caused by arterial stiffness (AS), and vascular morphological changes, caused by arterial dilatation. We analyzed the relationship of pre-AS and AS, as assessed by cardio ankle vascular index (CAVI), with arterial diameters...

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Autores principales: Wang, Yaoling, Yang, Jinrong, Lu, Yichen, Fan, Wenliang, Bai, Lijuan, Nie, Zhuang, Wang, Ruiyun, Yu, Jie, Liu, Lihua, Liu, Yun, He, Linfeng, Wen, Kai, Chen, Li, Yang, Fan, Qi, Benling
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8714774/
https://www.ncbi.nlm.nih.gov/pubmed/34977168
http://dx.doi.org/10.3389/fcvm.2021.737161
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author Wang, Yaoling
Yang, Jinrong
Lu, Yichen
Fan, Wenliang
Bai, Lijuan
Nie, Zhuang
Wang, Ruiyun
Yu, Jie
Liu, Lihua
Liu, Yun
He, Linfeng
Wen, Kai
Chen, Li
Yang, Fan
Qi, Benling
author_facet Wang, Yaoling
Yang, Jinrong
Lu, Yichen
Fan, Wenliang
Bai, Lijuan
Nie, Zhuang
Wang, Ruiyun
Yu, Jie
Liu, Lihua
Liu, Yun
He, Linfeng
Wen, Kai
Chen, Li
Yang, Fan
Qi, Benling
author_sort Wang, Yaoling
collection PubMed
description Background: Arterial aging is characterized by decreased vascular function, caused by arterial stiffness (AS), and vascular morphological changes, caused by arterial dilatation. We analyzed the relationship of pre-AS and AS, as assessed by cardio ankle vascular index (CAVI), with arterial diameters (AD) at nine levels, from the aortic sinus to the abdominal aorta, as measured by artificial intelligence (AI) on non-enhanced chest computed tomography (CT) images. Methods: Overall, 801 patients who underwent both chest CT scan and arterial elasticity test were enrolled. Nine horizontal diameters of the thoracic aorta (from the aortic sinuses of Valsalva to the abdominal aorta at the celiac axis origin) were measured by AI using CT. Patients were divided into non-AS (mean value of the left and right CAVIs [M.CAVI] < 8), pre-AS (8 ≤ M.CAVI < 9), and AS (M.CAVI ≥ 9) groups. We compared AD differences among groups, analyzed the correlation of age, ADs, and M.CAVI or the mean pressure-independent CAVI (M.CAVI(0)), Furthermore, we evaluated the risk predictors and the diagnostic value of the nine ADs for pre-AS and AS. Results: The AD at mid descending aorta (MD) correlated strongest with CAVI (r = 0.46, p < 0.001) or M.CAVI(0) (r = 0.42, p < 0.001). M.CAVI was most affected by the MD AD and by age. An increase in the MD AD independently predicted the occurrence of pre-AS or AS. For MD AD, every 4.37 mm increase caused a 14% increase in the pre-AS and AS risk and a 13% increase in the AS risk. With a cut-off value of 26.95 mm for the MD AD, the area under the curve (AUC) for identifying the risk of AS was 0.743. With a cut-off value of 25.15 mm, the AUC for identifying the risk of the stage after the prophase of AS is 0.739. Conclusions: Aging is associated with an increase in AD and a decrease in arterial elasticity. An increase in AD, particularly at the MD level is an independent predictor of AS development.
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spelling pubmed-87147742021-12-30 Thoracic Aorta Diameter Calculation by Artificial Intelligence Can Predict the Degree of Arterial Stiffness Wang, Yaoling Yang, Jinrong Lu, Yichen Fan, Wenliang Bai, Lijuan Nie, Zhuang Wang, Ruiyun Yu, Jie Liu, Lihua Liu, Yun He, Linfeng Wen, Kai Chen, Li Yang, Fan Qi, Benling Front Cardiovasc Med Cardiovascular Medicine Background: Arterial aging is characterized by decreased vascular function, caused by arterial stiffness (AS), and vascular morphological changes, caused by arterial dilatation. We analyzed the relationship of pre-AS and AS, as assessed by cardio ankle vascular index (CAVI), with arterial diameters (AD) at nine levels, from the aortic sinus to the abdominal aorta, as measured by artificial intelligence (AI) on non-enhanced chest computed tomography (CT) images. Methods: Overall, 801 patients who underwent both chest CT scan and arterial elasticity test were enrolled. Nine horizontal diameters of the thoracic aorta (from the aortic sinuses of Valsalva to the abdominal aorta at the celiac axis origin) were measured by AI using CT. Patients were divided into non-AS (mean value of the left and right CAVIs [M.CAVI] < 8), pre-AS (8 ≤ M.CAVI < 9), and AS (M.CAVI ≥ 9) groups. We compared AD differences among groups, analyzed the correlation of age, ADs, and M.CAVI or the mean pressure-independent CAVI (M.CAVI(0)), Furthermore, we evaluated the risk predictors and the diagnostic value of the nine ADs for pre-AS and AS. Results: The AD at mid descending aorta (MD) correlated strongest with CAVI (r = 0.46, p < 0.001) or M.CAVI(0) (r = 0.42, p < 0.001). M.CAVI was most affected by the MD AD and by age. An increase in the MD AD independently predicted the occurrence of pre-AS or AS. For MD AD, every 4.37 mm increase caused a 14% increase in the pre-AS and AS risk and a 13% increase in the AS risk. With a cut-off value of 26.95 mm for the MD AD, the area under the curve (AUC) for identifying the risk of AS was 0.743. With a cut-off value of 25.15 mm, the AUC for identifying the risk of the stage after the prophase of AS is 0.739. Conclusions: Aging is associated with an increase in AD and a decrease in arterial elasticity. An increase in AD, particularly at the MD level is an independent predictor of AS development. Frontiers Media S.A. 2021-12-15 /pmc/articles/PMC8714774/ /pubmed/34977168 http://dx.doi.org/10.3389/fcvm.2021.737161 Text en Copyright © 2021 Wang, Yang, Lu, Fan, Bai, Nie, Wang, Yu, Liu, Liu, He, Wen, Chen, Yang and Qi. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Cardiovascular Medicine
Wang, Yaoling
Yang, Jinrong
Lu, Yichen
Fan, Wenliang
Bai, Lijuan
Nie, Zhuang
Wang, Ruiyun
Yu, Jie
Liu, Lihua
Liu, Yun
He, Linfeng
Wen, Kai
Chen, Li
Yang, Fan
Qi, Benling
Thoracic Aorta Diameter Calculation by Artificial Intelligence Can Predict the Degree of Arterial Stiffness
title Thoracic Aorta Diameter Calculation by Artificial Intelligence Can Predict the Degree of Arterial Stiffness
title_full Thoracic Aorta Diameter Calculation by Artificial Intelligence Can Predict the Degree of Arterial Stiffness
title_fullStr Thoracic Aorta Diameter Calculation by Artificial Intelligence Can Predict the Degree of Arterial Stiffness
title_full_unstemmed Thoracic Aorta Diameter Calculation by Artificial Intelligence Can Predict the Degree of Arterial Stiffness
title_short Thoracic Aorta Diameter Calculation by Artificial Intelligence Can Predict the Degree of Arterial Stiffness
title_sort thoracic aorta diameter calculation by artificial intelligence can predict the degree of arterial stiffness
topic Cardiovascular Medicine
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8714774/
https://www.ncbi.nlm.nih.gov/pubmed/34977168
http://dx.doi.org/10.3389/fcvm.2021.737161
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