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On the Left Ventricular Remodeling of Patients with Stenotic Aortic Valve: A Statistical Shape Analysis

The left ventricle (LV) constantly changes its shape and function as a response to pathological conditions, and this process is known as remodeling. In the presence of aortic stenosis (AS), the degenerative process is not limited to the aortic valve but also involves the remodeling of LV. Statistica...

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Autores principales: Cutugno, Salvatore, Ingrassia, Tommaso, Nigrelli, Vincenzo, Pasta, Salvatore
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8153107/
https://www.ncbi.nlm.nih.gov/pubmed/34068270
http://dx.doi.org/10.3390/bioengineering8050066
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author Cutugno, Salvatore
Ingrassia, Tommaso
Nigrelli, Vincenzo
Pasta, Salvatore
author_facet Cutugno, Salvatore
Ingrassia, Tommaso
Nigrelli, Vincenzo
Pasta, Salvatore
author_sort Cutugno, Salvatore
collection PubMed
description The left ventricle (LV) constantly changes its shape and function as a response to pathological conditions, and this process is known as remodeling. In the presence of aortic stenosis (AS), the degenerative process is not limited to the aortic valve but also involves the remodeling of LV. Statistical shape analysis (SSA) offers a powerful tool for the visualization and quantification of the geometrical and functional patterns of any anatomic changes. In this paper, a SSA method was developed to determine shape descriptors of the LV under different degrees of AS and thus to shed light on the mechanistic link between shape and function. A total of [Formula: see text] patients underwent computed tomography (CT) for the evaluation of valvulopathy were segmented to obtain the LV surface and then were automatically aligned to a reference template by rigid registrations and transformations. Shape modes of the anatomical LV variation induced by the degree of AS were assessed by principal component analysis (PCA). The first shape mode represented nearly 50% of the total variance of LV shape in our patient population and was mainly associated to a spherical LV geometry. At Pearson’s analysis, the first shape mode was positively correlated to both the end-diastolic volume ([Formula: see text] , [Formula: see text]) and end-systolic volume ([Formula: see text] , and [Formula: see text]), suggesting LV impairment in patients with severe AS. A predictive model built with PCA-related shape modes achieved better performance in stratifying the occurrence of adverse events with respect to a baseline model using clinical demographic data as risk predictors. This study demonstrated the potential of SSA approaches to detect the association of complex 3D shape features with functional LV parameters.
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spelling pubmed-81531072021-05-27 On the Left Ventricular Remodeling of Patients with Stenotic Aortic Valve: A Statistical Shape Analysis Cutugno, Salvatore Ingrassia, Tommaso Nigrelli, Vincenzo Pasta, Salvatore Bioengineering (Basel) Article The left ventricle (LV) constantly changes its shape and function as a response to pathological conditions, and this process is known as remodeling. In the presence of aortic stenosis (AS), the degenerative process is not limited to the aortic valve but also involves the remodeling of LV. Statistical shape analysis (SSA) offers a powerful tool for the visualization and quantification of the geometrical and functional patterns of any anatomic changes. In this paper, a SSA method was developed to determine shape descriptors of the LV under different degrees of AS and thus to shed light on the mechanistic link between shape and function. A total of [Formula: see text] patients underwent computed tomography (CT) for the evaluation of valvulopathy were segmented to obtain the LV surface and then were automatically aligned to a reference template by rigid registrations and transformations. Shape modes of the anatomical LV variation induced by the degree of AS were assessed by principal component analysis (PCA). The first shape mode represented nearly 50% of the total variance of LV shape in our patient population and was mainly associated to a spherical LV geometry. At Pearson’s analysis, the first shape mode was positively correlated to both the end-diastolic volume ([Formula: see text] , [Formula: see text]) and end-systolic volume ([Formula: see text] , and [Formula: see text]), suggesting LV impairment in patients with severe AS. A predictive model built with PCA-related shape modes achieved better performance in stratifying the occurrence of adverse events with respect to a baseline model using clinical demographic data as risk predictors. This study demonstrated the potential of SSA approaches to detect the association of complex 3D shape features with functional LV parameters. MDPI 2021-05-13 /pmc/articles/PMC8153107/ /pubmed/34068270 http://dx.doi.org/10.3390/bioengineering8050066 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Cutugno, Salvatore
Ingrassia, Tommaso
Nigrelli, Vincenzo
Pasta, Salvatore
On the Left Ventricular Remodeling of Patients with Stenotic Aortic Valve: A Statistical Shape Analysis
title On the Left Ventricular Remodeling of Patients with Stenotic Aortic Valve: A Statistical Shape Analysis
title_full On the Left Ventricular Remodeling of Patients with Stenotic Aortic Valve: A Statistical Shape Analysis
title_fullStr On the Left Ventricular Remodeling of Patients with Stenotic Aortic Valve: A Statistical Shape Analysis
title_full_unstemmed On the Left Ventricular Remodeling of Patients with Stenotic Aortic Valve: A Statistical Shape Analysis
title_short On the Left Ventricular Remodeling of Patients with Stenotic Aortic Valve: A Statistical Shape Analysis
title_sort on the left ventricular remodeling of patients with stenotic aortic valve: a statistical shape analysis
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8153107/
https://www.ncbi.nlm.nih.gov/pubmed/34068270
http://dx.doi.org/10.3390/bioengineering8050066
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