Phenotype mapping of heart failure with preserved ejection fraction

Detalhes bibliográficos
Autor(a) principal: Mesquita,Evandro Tinoco
Data de Publicação: 2018
Outros Autores: Grion,Debora Carvalho, Kubrusly,Miguel Camargo, Silva,Bernardo Barcelos Fernandes Fumagalli, Santos,Érico Araújo Reis
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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spelling 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
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