GRACE PLUS: A data fusion-based approach to improve GRACE score in the risk assessment of Acute Coronary Syndrome
Autor(a) principal: | |
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Data de Publicação: | 2023 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10316/105083 https://doi.org/10.1016/j.inffus.2022.10.019 |
Resumo: | Cardiovascular Diseases (CVDs) are the world’s leading cause of morbidity and mortality, being responsible for almost 17 million deaths each year. In Europe, let alone it is estimated that 20% of all citizens suffer from one form of CVD, namely cerebrovascular disease or heart failure and Coronary Artery Disease (CAD). Among the latter, Acute Coronary Syndrome (ACS) is of particular importance since it is deadly and, hence, requires a prompt diagnosis and immediate medical attention. Aiming to deal with prognostication and promote consistency in managing patients with ACS, the Global Registry of Acute Coronary Events (GRACE) risk score has been proposed. This tool is based on eight independent risk factors roughly accounting for 89.9% of prognostic information. Nevertheless, as some other risk factors, not included in GRACE, are also known to be important vectors in the stratification of patients, namely haemoglobin at admission, it is expected that by embedding additional risk factors information into GRACE it will lead to a better characterisation of a patient’s risk. Making use of data-fusion techniques, the present work proposes a generalisable framework to improve the classification performance of GRACE in predicting the risk of death in the course of six months after an ACS event, while preserving its interpretability and applicability. Considering haemoglobin concentration at admission, as an additional risk factor, it is shown that the discrimination performance of new GRACE Plus score outperformed that of GRACE in a database of cohorts comprising 1506 patients admitted with ACS, showing a F-1 score of 0.6033 for GRACE Plus against 0.5828 for GRACE, which is corroborated by one-tailed t-test in terms of correct stratification of death and survival endpoints, namely, = −9.1876 and < 0.001. |
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GRACE PLUS: A data fusion-based approach to improve GRACE score in the risk assessment of Acute Coronary SyndromeAcute Coronary SyndromeGRACE risk scoreData fusionMachine learningCorrection factorCardiovascular Diseases (CVDs) are the world’s leading cause of morbidity and mortality, being responsible for almost 17 million deaths each year. In Europe, let alone it is estimated that 20% of all citizens suffer from one form of CVD, namely cerebrovascular disease or heart failure and Coronary Artery Disease (CAD). Among the latter, Acute Coronary Syndrome (ACS) is of particular importance since it is deadly and, hence, requires a prompt diagnosis and immediate medical attention. Aiming to deal with prognostication and promote consistency in managing patients with ACS, the Global Registry of Acute Coronary Events (GRACE) risk score has been proposed. This tool is based on eight independent risk factors roughly accounting for 89.9% of prognostic information. Nevertheless, as some other risk factors, not included in GRACE, are also known to be important vectors in the stratification of patients, namely haemoglobin at admission, it is expected that by embedding additional risk factors information into GRACE it will lead to a better characterisation of a patient’s risk. Making use of data-fusion techniques, the present work proposes a generalisable framework to improve the classification performance of GRACE in predicting the risk of death in the course of six months after an ACS event, while preserving its interpretability and applicability. Considering haemoglobin concentration at admission, as an additional risk factor, it is shown that the discrimination performance of new GRACE Plus score outperformed that of GRACE in a database of cohorts comprising 1506 patients admitted with ACS, showing a F-1 score of 0.6033 for GRACE Plus against 0.5828 for GRACE, which is corroborated by one-tailed t-test in terms of correct stratification of death and survival endpoints, namely, = −9.1876 and < 0.001.This work has been partially supported by Fundação para a Ciência e Tecnologia (FCT), Portugal, I.P./MCTES through national funds (PIDDAC), within the scope of CISUC R&D Unit - UIDB/00326/2020, CTS - Centro de Tecnologia e Sistemas - UIDB/00066/2020, and grant SFRH/BSAB/150268/2019.Elsevier20232024-06-29T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://hdl.handle.net/10316/105083http://hdl.handle.net/10316/105083https://doi.org/10.1016/j.inffus.2022.10.019eng15662535https://doi.org/10.1016/j.inffus.2022.10.019Neto, Afonso B.L.Sousa, José P.Gil, PauloHenriques, Jorgeinfo:eu-repo/semantics/embargoedAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-10-27T11:12:02Zoai:estudogeral.uc.pt:10316/105083Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T21:21:40.110255Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
GRACE PLUS: A data fusion-based approach to improve GRACE score in the risk assessment of Acute Coronary Syndrome |
title |
GRACE PLUS: A data fusion-based approach to improve GRACE score in the risk assessment of Acute Coronary Syndrome |
spellingShingle |
GRACE PLUS: A data fusion-based approach to improve GRACE score in the risk assessment of Acute Coronary Syndrome Neto, Afonso B.L. Acute Coronary Syndrome GRACE risk score Data fusion Machine learning Correction factor |
title_short |
GRACE PLUS: A data fusion-based approach to improve GRACE score in the risk assessment of Acute Coronary Syndrome |
title_full |
GRACE PLUS: A data fusion-based approach to improve GRACE score in the risk assessment of Acute Coronary Syndrome |
title_fullStr |
GRACE PLUS: A data fusion-based approach to improve GRACE score in the risk assessment of Acute Coronary Syndrome |
title_full_unstemmed |
GRACE PLUS: A data fusion-based approach to improve GRACE score in the risk assessment of Acute Coronary Syndrome |
title_sort |
GRACE PLUS: A data fusion-based approach to improve GRACE score in the risk assessment of Acute Coronary Syndrome |
author |
Neto, Afonso B.L. |
author_facet |
Neto, Afonso B.L. Sousa, José P. Gil, Paulo Henriques, Jorge |
author_role |
author |
author2 |
Sousa, José P. Gil, Paulo Henriques, Jorge |
author2_role |
author author author |
dc.contributor.author.fl_str_mv |
Neto, Afonso B.L. Sousa, José P. Gil, Paulo Henriques, Jorge |
dc.subject.por.fl_str_mv |
Acute Coronary Syndrome GRACE risk score Data fusion Machine learning Correction factor |
topic |
Acute Coronary Syndrome GRACE risk score Data fusion Machine learning Correction factor |
description |
Cardiovascular Diseases (CVDs) are the world’s leading cause of morbidity and mortality, being responsible for almost 17 million deaths each year. In Europe, let alone it is estimated that 20% of all citizens suffer from one form of CVD, namely cerebrovascular disease or heart failure and Coronary Artery Disease (CAD). Among the latter, Acute Coronary Syndrome (ACS) is of particular importance since it is deadly and, hence, requires a prompt diagnosis and immediate medical attention. Aiming to deal with prognostication and promote consistency in managing patients with ACS, the Global Registry of Acute Coronary Events (GRACE) risk score has been proposed. This tool is based on eight independent risk factors roughly accounting for 89.9% of prognostic information. Nevertheless, as some other risk factors, not included in GRACE, are also known to be important vectors in the stratification of patients, namely haemoglobin at admission, it is expected that by embedding additional risk factors information into GRACE it will lead to a better characterisation of a patient’s risk. Making use of data-fusion techniques, the present work proposes a generalisable framework to improve the classification performance of GRACE in predicting the risk of death in the course of six months after an ACS event, while preserving its interpretability and applicability. Considering haemoglobin concentration at admission, as an additional risk factor, it is shown that the discrimination performance of new GRACE Plus score outperformed that of GRACE in a database of cohorts comprising 1506 patients admitted with ACS, showing a F-1 score of 0.6033 for GRACE Plus against 0.5828 for GRACE, which is corroborated by one-tailed t-test in terms of correct stratification of death and survival endpoints, namely, = −9.1876 and < 0.001. |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023 2024-06-29T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10316/105083 http://hdl.handle.net/10316/105083 https://doi.org/10.1016/j.inffus.2022.10.019 |
url |
http://hdl.handle.net/10316/105083 https://doi.org/10.1016/j.inffus.2022.10.019 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
15662535 https://doi.org/10.1016/j.inffus.2022.10.019 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/embargoedAccess |
eu_rights_str_mv |
embargoedAccess |
dc.publisher.none.fl_str_mv |
Elsevier |
publisher.none.fl_str_mv |
Elsevier |
dc.source.none.fl_str_mv |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
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RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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