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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Autores: Loncaric, Filip, Cikes, Maja, Sitges, Marta, Bijnens, Bart
Tipo de recurso: artículo
Estado:Versión aceptada para publicación
Fecha de publicación:2020
País:España
Institución:Universitat Pompeu Fabra
Repositorio:Repositorio Digital de la UPF
OAI Identifier:oai:repositori.upf.edu:10230/46077
Acceso en línea:http://hdl.handle.net/10230/46077
http://dx.doi.org/10.1111/echo.14749
Access Level:acceso abierto
Palabra clave:Diastolic dysfunction
Diastolic function
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spelling Comprehensive data integration—Toward a more personalized assessment of diastolic functionLoncaric, FilipCikes, MajaSitges, MartaBijnens, BartDiastolic dysfunctionDiastolic functionBackground 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.This work was supported by Horizon 2020 European Commission Project H2020-MSCA-ITN-2016 (764738).Wiley20202020info:eu-repo/semantics/articleinfo:eu-repo/semantics/acceptedVersionapplication/pdfapplication/pdfhttp://hdl.handle.net/10230/46077http://dx.doi.org/10.1111/echo.14749reponame:Repositorio Digital de la UPFinstname:Universitat Pompeu FabraInglésEchocardiography. 2020 Jun 10info:eu-repo/grantAgreement/EC/H2020/764738This is the peer reviewed version of the following article: Loncaric F, Cikes M, Sitges M, Bijnens B. Comprehensive data integration—Toward a more personalized assessment of diastolic function. Echocardiography. 2020 Jun 10, which has been published in final form at http://dx.doi.org/10.1111/echo.14749. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions.info:eu-repo/semantics/openAccessoai:repositori.upf.edu:10230/460772026-06-12T07:21:37Z
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
Diastolic dysfunction
Diastolic function
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, Marta
Bijnens, Bart
author Loncaric, Filip
author_facet Loncaric, Filip
Cikes, Maja
Sitges, Marta
Bijnens, Bart
author_role author
author2 Cikes, Maja
Sitges, Marta
Bijnens, Bart
author2_role author
author
author
dc.subject.none.fl_str_mv Diastolic dysfunction
Diastolic function
topic Diastolic dysfunction
Diastolic function
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
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 http://hdl.handle.net/10230/46077
http://dx.doi.org/10.1111/echo.14749
url http://hdl.handle.net/10230/46077
http://dx.doi.org/10.1111/echo.14749
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Echocardiography. 2020 Jun 10
info:eu-repo/grantAgreement/EC/H2020/764738
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Wiley
publisher.none.fl_str_mv Wiley
dc.source.none.fl_str_mv reponame:Repositorio Digital de la UPF
instname:Universitat Pompeu Fabra
instname_str Universitat Pompeu Fabra
reponame_str Repositorio Digital de la UPF
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