Phenotype mapping of heart failure with preserved ejection fraction
Autor(a) principal: | |
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Data de Publicação: | 2018 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | International Journal of Cardiovascular Sciences (Online) |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S2359-56472018000600652 |
Resumo: | Abstract Heart failure with preserved ejection fraction (HFPEF) has become the main phenotypic model of heart failure (HF) in community and referral patients in Brazil and in the world. Despite advances in the development of new drugs for HF treatment, there has been no significant improvement in mortality of this condition. According to many studies, this can be explained by the heterogeneous nature of HF physiopathology, whose basic mechanisms may result in different clinical presentations, culminating in the emerging of different phenogroups in this syndrome. In this context, phenotype mapping of HFPEF has emerged as a possible solution, since it enables the development of clinical trials that establish specific therapeutic strategies for each phenotypic profile. New technologies in the field of artificial intelligence have enabled the assessment of a large volume of data and infer intrinsic patterns and different outcomes. Thereby, it is possible to obtain mutually exclusive categories of HFPEF, with a phenotype mapping of the syndrome and grouping of patients according to their phenotypic features. Besides, other diseases can have the same clinical phenotype but different pathophysiological basis, the so called “phenocopies”. These tools enable the analysis and categorization of the wide spectrum of heart failure, contributing to solve the dilemmas of the treatment of this syndrome. |
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International Journal of Cardiovascular Sciences (Online) |
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Phenotype mapping of heart failure with preserved ejection fractionHeart Failure / physiopathologyStroke VolumePhenotypeMachine LearningArtificial IntelligenceAbstract Heart failure with preserved ejection fraction (HFPEF) has become the main phenotypic model of heart failure (HF) in community and referral patients in Brazil and in the world. Despite advances in the development of new drugs for HF treatment, there has been no significant improvement in mortality of this condition. According to many studies, this can be explained by the heterogeneous nature of HF physiopathology, whose basic mechanisms may result in different clinical presentations, culminating in the emerging of different phenogroups in this syndrome. In this context, phenotype mapping of HFPEF has emerged as a possible solution, since it enables the development of clinical trials that establish specific therapeutic strategies for each phenotypic profile. New technologies in the field of artificial intelligence have enabled the assessment of a large volume of data and infer intrinsic patterns and different outcomes. Thereby, it is possible to obtain mutually exclusive categories of HFPEF, with a phenotype mapping of the syndrome and grouping of patients according to their phenotypic features. Besides, other diseases can have the same clinical phenotype but different pathophysiological basis, the so called “phenocopies”. These tools enable the analysis and categorization of the wide spectrum of heart failure, contributing to solve the dilemmas of the treatment of this syndrome.Sociedade Brasileira de Cardiologia2018-12-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S2359-56472018000600652International Journal of Cardiovascular Sciences v.31 n.6 2018reponame:International Journal of Cardiovascular Sciences (Online)instname:Sociedade Brasileira de Cardiologia (SBC)instacron:SBC10.5935/2359-4802.20180047info:eu-repo/semantics/openAccessMesquita,Evandro TinocoGrion,Debora CarvalhoKubrusly,Miguel CamargoSilva,Bernardo Barcelos Fernandes FumagalliSantos,Érico Araújo Reiseng2018-11-13T00:00:00Zoai:scielo:S2359-56472018000600652Revistahttp://publicacoes.cardiol.br/portal/ijcshttps://old.scielo.br/oai/scielo-oai.phptailanerodrigues@cardiol.br||revistaijcs@cardiol.br2359-56472359-4802opendoar:2018-11-13T00:00International Journal of Cardiovascular Sciences (Online) - Sociedade Brasileira de Cardiologia (SBC)false |
dc.title.none.fl_str_mv |
Phenotype mapping of heart failure with preserved ejection fraction |
title |
Phenotype mapping of heart failure with preserved ejection fraction |
spellingShingle |
Phenotype mapping of heart failure with preserved ejection fraction Mesquita,Evandro Tinoco Heart Failure / physiopathology Stroke Volume Phenotype Machine Learning Artificial Intelligence |
title_short |
Phenotype mapping of heart failure with preserved ejection fraction |
title_full |
Phenotype mapping of heart failure with preserved ejection fraction |
title_fullStr |
Phenotype mapping of heart failure with preserved ejection fraction |
title_full_unstemmed |
Phenotype mapping of heart failure with preserved ejection fraction |
title_sort |
Phenotype mapping of heart failure with preserved ejection fraction |
author |
Mesquita,Evandro Tinoco |
author_facet |
Mesquita,Evandro Tinoco Grion,Debora Carvalho Kubrusly,Miguel Camargo Silva,Bernardo Barcelos Fernandes Fumagalli Santos,Érico Araújo Reis |
author_role |
author |
author2 |
Grion,Debora Carvalho Kubrusly,Miguel Camargo Silva,Bernardo Barcelos Fernandes Fumagalli Santos,Érico Araújo Reis |
author2_role |
author author author author |
dc.contributor.author.fl_str_mv |
Mesquita,Evandro Tinoco Grion,Debora Carvalho Kubrusly,Miguel Camargo Silva,Bernardo Barcelos Fernandes Fumagalli Santos,Érico Araújo Reis |
dc.subject.por.fl_str_mv |
Heart Failure / physiopathology Stroke Volume Phenotype Machine Learning Artificial Intelligence |
topic |
Heart Failure / physiopathology Stroke Volume Phenotype Machine Learning Artificial Intelligence |
description |
Abstract Heart failure with preserved ejection fraction (HFPEF) has become the main phenotypic model of heart failure (HF) in community and referral patients in Brazil and in the world. Despite advances in the development of new drugs for HF treatment, there has been no significant improvement in mortality of this condition. According to many studies, this can be explained by the heterogeneous nature of HF physiopathology, whose basic mechanisms may result in different clinical presentations, culminating in the emerging of different phenogroups in this syndrome. In this context, phenotype mapping of HFPEF has emerged as a possible solution, since it enables the development of clinical trials that establish specific therapeutic strategies for each phenotypic profile. New technologies in the field of artificial intelligence have enabled the assessment of a large volume of data and infer intrinsic patterns and different outcomes. Thereby, it is possible to obtain mutually exclusive categories of HFPEF, with a phenotype mapping of the syndrome and grouping of patients according to their phenotypic features. Besides, other diseases can have the same clinical phenotype but different pathophysiological basis, the so called “phenocopies”. These tools enable the analysis and categorization of the wide spectrum of heart failure, contributing to solve the dilemmas of the treatment of this syndrome. |
publishDate |
2018 |
dc.date.none.fl_str_mv |
2018-12-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S2359-56472018000600652 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S2359-56472018000600652 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.5935/2359-4802.20180047 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
text/html |
dc.publisher.none.fl_str_mv |
Sociedade Brasileira de Cardiologia |
publisher.none.fl_str_mv |
Sociedade Brasileira de Cardiologia |
dc.source.none.fl_str_mv |
International Journal of Cardiovascular Sciences v.31 n.6 2018 reponame:International Journal of Cardiovascular Sciences (Online) instname:Sociedade Brasileira de Cardiologia (SBC) instacron:SBC |
instname_str |
Sociedade Brasileira de Cardiologia (SBC) |
instacron_str |
SBC |
institution |
SBC |
reponame_str |
International Journal of Cardiovascular Sciences (Online) |
collection |
International Journal of Cardiovascular Sciences (Online) |
repository.name.fl_str_mv |
International Journal of Cardiovascular Sciences (Online) - Sociedade Brasileira de Cardiologia (SBC) |
repository.mail.fl_str_mv |
tailanerodrigues@cardiol.br||revistaijcs@cardiol.br |
_version_ |
1754732625300291584 |