Comprehensive data integration-Toward a more personalized assessment of diastolic function

Background and aim: The main challenge of assessing diastolic function is the balance between clinical utility, in the sense of usability and time‐efficiency, and overall applicability, in the sense of precision for the patient under investigation. In this review, we aim to explore the challenges of...

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Authors: Loncaric, Filip, Cikes, Maja, Sitges Carreño, Marta, Bijnens, Bart
Format: article
Status:Versión aceptada para publicación
Publication Date:2020
Country:España
Institution:Universidad de Barcelona
Repository:Dipòsit Digital de la UB
OAI Identifier:oai:diposit.ub.edu:2445/168657
Online Access:https://hdl.handle.net/2445/168657
Access Level:Open access
Keyword:Ventricles cardíacs
Fenotip
Ventricle of heart
Phenotype
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oai_identifier_str oai:diposit.ub.edu:2445/168657
network_acronym_str ES
network_name_str España
repository_id_str
spelling Comprehensive data integration-Toward a more personalized assessment of diastolic functionLoncaric, FilipCikes, MajaSitges Carreño, MartaBijnens, BartVentricles cardíacsFenotipVentricle of heartPhenotypeBackground and aim: The main challenge of assessing diastolic function is the balance between clinical utility, in the sense of usability and time‐efficiency, and overall applicability, in the sense of precision for the patient under investigation. In this review, we aim to explore the challenges of integrating data in the assessment of diastolic function and discuss the perspectives of a more comprehensive data integration approach. Methods: Review of traditional and novel approaches regarding data integration in the assessment of diastolic function. Results: Comprehensive data integration can lead to improved understanding of disease phenotypes and better relation of these phenotypes to underlying pathophysiological processes—which may help affirm diagnostic reasoning, guide treatment options, and reduce limitations related to previously unaddressed confounders. The optimal assessment of diastolic function should ideally integrate all relevant clinical information with all available structural and functional whole cardiac cycle echocardiographic data—envisioning a personalized approach to patient care, a high‐reaching future goal in medicine. Conclusion: Complete data integration seems to be a long‐lasting goal, the way forward in diastology, and machine learning seems to be one of the tools suited for the challenge. With perpetual evidence that traditional approaches to complex problems may not the optimal solution, there is room for a steady and cautious, and inherently very exciting paradigm shift toward novel diagnostic tools and workflows to reach a more personalized, comprehensive, and integrated assessment of cardiac function.Wiley2020info:eu-repo/semantics/articleinfo:eu-repo/semantics/acceptedVersionapplication/pdfhttps://hdl.handle.net/2445/168657Articles publicats en revistes (IDIBAPS: Institut d'investigacions Biomèdiques August Pi i Sunyer)reponame:Dipòsit Digital de la UBinstname:Universidad de BarcelonaInglésVersió postprint del document publicat a: https://doi.org/10.1111/echo.14749Echocardiography, 2020https://doi.org/10.1111/echo.14749info:eu-repo/grantAgreement/EC/H2020/764738(c) Wiley Periodicals LLC., 2020info:eu-repo/semantics/openAccessoai:diposit.ub.edu:2445/1686572026-05-27T06:46:51Z
dc.title.none.fl_str_mv Comprehensive data integration-Toward a more personalized assessment of diastolic function
title Comprehensive data integration-Toward a more personalized assessment of diastolic function
spellingShingle Comprehensive data integration-Toward a more personalized assessment of diastolic function
Loncaric, Filip
Ventricles cardíacs
Fenotip
Ventricle of heart
Phenotype
title_short Comprehensive data integration-Toward a more personalized assessment of diastolic function
title_full Comprehensive data integration-Toward a more personalized assessment of diastolic function
title_fullStr Comprehensive data integration-Toward a more personalized assessment of diastolic function
title_full_unstemmed Comprehensive data integration-Toward a more personalized assessment of diastolic function
title_sort Comprehensive data integration-Toward a more personalized assessment of diastolic function
dc.creator.none.fl_str_mv Loncaric, Filip
Cikes, Maja
Sitges Carreño, Marta
Bijnens, Bart
author Loncaric, Filip
author_facet Loncaric, Filip
Cikes, Maja
Sitges Carreño, Marta
Bijnens, Bart
author_role author
author2 Cikes, Maja
Sitges Carreño, Marta
Bijnens, Bart
author2_role author
author
author
dc.subject.none.fl_str_mv Ventricles cardíacs
Fenotip
Ventricle of heart
Phenotype
topic Ventricles cardíacs
Fenotip
Ventricle of heart
Phenotype
description Background and aim: The main challenge of assessing diastolic function is the balance between clinical utility, in the sense of usability and time‐efficiency, and overall applicability, in the sense of precision for the patient under investigation. In this review, we aim to explore the challenges of integrating data in the assessment of diastolic function and discuss the perspectives of a more comprehensive data integration approach. Methods: Review of traditional and novel approaches regarding data integration in the assessment of diastolic function. Results: Comprehensive data integration can lead to improved understanding of disease phenotypes and better relation of these phenotypes to underlying pathophysiological processes—which may help affirm diagnostic reasoning, guide treatment options, and reduce limitations related to previously unaddressed confounders. The optimal assessment of diastolic function should ideally integrate all relevant clinical information with all available structural and functional whole cardiac cycle echocardiographic data—envisioning a personalized approach to patient care, a high‐reaching future goal in medicine. Conclusion: Complete data integration seems to be a long‐lasting goal, the way forward in diastology, and machine learning seems to be one of the tools suited for the challenge. With perpetual evidence that traditional approaches to complex problems may not the optimal solution, there is room for a steady and cautious, and inherently very exciting paradigm shift toward novel diagnostic tools and workflows to reach a more personalized, comprehensive, and integrated assessment of cardiac function.
publishDate 2020
dc.date.none.fl_str_mv 2020
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/acceptedVersion
format article
status_str acceptedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/2445/168657
url https://hdl.handle.net/2445/168657
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Versió postprint del document publicat a: https://doi.org/10.1111/echo.14749
Echocardiography, 2020
https://doi.org/10.1111/echo.14749
info:eu-repo/grantAgreement/EC/H2020/764738
dc.rights.none.fl_str_mv (c) Wiley Periodicals LLC., 2020
info:eu-repo/semantics/openAccess
rights_invalid_str_mv (c) Wiley Periodicals LLC., 2020
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Wiley
publisher.none.fl_str_mv Wiley
dc.source.none.fl_str_mv Articles publicats en revistes (IDIBAPS: Institut d'investigacions Biomèdiques August Pi i Sunyer)
reponame:Dipòsit Digital de la UB
instname:Universidad de Barcelona
instname_str Universidad de Barcelona
reponame_str Dipòsit Digital de la UB
collection Dipòsit Digital de la UB
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.301603

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