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Analysis of Prevalence and Clinical Features of Aortic Stenosis in Patients with and without Bicuspid Aortic Valve Using Machine Learning Methods

Aortic stenosis (AS) is the most commonly diagnosed valvular heart disease, and its prevalence increases with the aging of the general population. However, AS is often diagnosed at a severe stage, necessitating surgical treatment, due to its long asymptomatic period. The objective of this study was...

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Autores principales: Irtyuga, Olga, Babakekhyan, Mary, Kostareva, Anna, Uspensky, Vladimir, Gordeev, Michail, Faggian, Giuseppe, Malashicheva, Anna, Metsker, Oleg, Shlyakhto, Evgeny, Kopanitsa, Georgy
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10671965/
https://www.ncbi.nlm.nih.gov/pubmed/38003903
http://dx.doi.org/10.3390/jpm13111588
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author Irtyuga, Olga
Babakekhyan, Mary
Kostareva, Anna
Uspensky, Vladimir
Gordeev, Michail
Faggian, Giuseppe
Malashicheva, Anna
Metsker, Oleg
Shlyakhto, Evgeny
Kopanitsa, Georgy
author_facet Irtyuga, Olga
Babakekhyan, Mary
Kostareva, Anna
Uspensky, Vladimir
Gordeev, Michail
Faggian, Giuseppe
Malashicheva, Anna
Metsker, Oleg
Shlyakhto, Evgeny
Kopanitsa, Georgy
author_sort Irtyuga, Olga
collection PubMed
description Aortic stenosis (AS) is the most commonly diagnosed valvular heart disease, and its prevalence increases with the aging of the general population. However, AS is often diagnosed at a severe stage, necessitating surgical treatment, due to its long asymptomatic period. The objective of this study was to analyze the frequency of AS in a population of cardiovascular patients using echocardiography (ECHO) and to identify clinical factors and features associated with these patient groups. We utilized machine learning methods to analyze 84,851 echocardiograms performed between 2010 and 2018 at the National Medical Research Center named after V.A. Almazov. The primary indications for ECHO were coronary artery disease (CAD) and hypertension (HP), accounting for 33.5% and 14.2% of the cases, respectively. The frequency of AS was found to be 13.26% among the patients (n = 11,252). Within our study, 1544 patients had a bicuspid aortic valve (BAV), while 83,316 patients had a tricuspid aortic valve (TAV). BAV patients were observed to be younger compared to TAV patients. AS was more prevalent in the BAV group (59%) compared to the TAV group (12%), with a p-value of <0.0001. By employing a machine learning algorithm, we randomly identified significant features present in AS patients, including age, hypertension (HP), aortic regurgitation (AR), ascending aortic dilatation (AscAD), and BAV. These findings could serve as additional indications for earlier observation and more frequent ECHO in specific patient groups for the earlier detection of developing AS.
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spelling pubmed-106719652023-11-09 Analysis of Prevalence and Clinical Features of Aortic Stenosis in Patients with and without Bicuspid Aortic Valve Using Machine Learning Methods Irtyuga, Olga Babakekhyan, Mary Kostareva, Anna Uspensky, Vladimir Gordeev, Michail Faggian, Giuseppe Malashicheva, Anna Metsker, Oleg Shlyakhto, Evgeny Kopanitsa, Georgy J Pers Med Article Aortic stenosis (AS) is the most commonly diagnosed valvular heart disease, and its prevalence increases with the aging of the general population. However, AS is often diagnosed at a severe stage, necessitating surgical treatment, due to its long asymptomatic period. The objective of this study was to analyze the frequency of AS in a population of cardiovascular patients using echocardiography (ECHO) and to identify clinical factors and features associated with these patient groups. We utilized machine learning methods to analyze 84,851 echocardiograms performed between 2010 and 2018 at the National Medical Research Center named after V.A. Almazov. The primary indications for ECHO were coronary artery disease (CAD) and hypertension (HP), accounting for 33.5% and 14.2% of the cases, respectively. The frequency of AS was found to be 13.26% among the patients (n = 11,252). Within our study, 1544 patients had a bicuspid aortic valve (BAV), while 83,316 patients had a tricuspid aortic valve (TAV). BAV patients were observed to be younger compared to TAV patients. AS was more prevalent in the BAV group (59%) compared to the TAV group (12%), with a p-value of <0.0001. By employing a machine learning algorithm, we randomly identified significant features present in AS patients, including age, hypertension (HP), aortic regurgitation (AR), ascending aortic dilatation (AscAD), and BAV. These findings could serve as additional indications for earlier observation and more frequent ECHO in specific patient groups for the earlier detection of developing AS. MDPI 2023-11-09 /pmc/articles/PMC10671965/ /pubmed/38003903 http://dx.doi.org/10.3390/jpm13111588 Text en © 2023 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
Irtyuga, Olga
Babakekhyan, Mary
Kostareva, Anna
Uspensky, Vladimir
Gordeev, Michail
Faggian, Giuseppe
Malashicheva, Anna
Metsker, Oleg
Shlyakhto, Evgeny
Kopanitsa, Georgy
Analysis of Prevalence and Clinical Features of Aortic Stenosis in Patients with and without Bicuspid Aortic Valve Using Machine Learning Methods
title Analysis of Prevalence and Clinical Features of Aortic Stenosis in Patients with and without Bicuspid Aortic Valve Using Machine Learning Methods
title_full Analysis of Prevalence and Clinical Features of Aortic Stenosis in Patients with and without Bicuspid Aortic Valve Using Machine Learning Methods
title_fullStr Analysis of Prevalence and Clinical Features of Aortic Stenosis in Patients with and without Bicuspid Aortic Valve Using Machine Learning Methods
title_full_unstemmed Analysis of Prevalence and Clinical Features of Aortic Stenosis in Patients with and without Bicuspid Aortic Valve Using Machine Learning Methods
title_short Analysis of Prevalence and Clinical Features of Aortic Stenosis in Patients with and without Bicuspid Aortic Valve Using Machine Learning Methods
title_sort analysis of prevalence and clinical features of aortic stenosis in patients with and without bicuspid aortic valve using machine learning methods
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10671965/
https://www.ncbi.nlm.nih.gov/pubmed/38003903
http://dx.doi.org/10.3390/jpm13111588
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