In-hospital outcomes of infective endocarditis from 1978 to 2015: analysis through machine-learning techniques
| Autor(a) principal: | |
|---|---|
| Data de Publicação: | 2022 |
| 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/10451/51900 |
Resumo: | © 2021 The Authors. Published by Elsevier Inc. on behalf of the Canadian Cardiovascular Society. This is an open access article under the CC BY-NC- ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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7160 |
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In-hospital outcomes of infective endocarditis from 1978 to 2015: analysis through machine-learning techniques© 2021 The Authors. Published by Elsevier Inc. on behalf of the Canadian Cardiovascular Society. This is an open access article under the CC BY-NC- ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)Background: Early identification of patients with infective endocarditis (IE) at higher risk for in-hospital mortality is essential to guide management and improve prognosis. Methods: A retrospective analysis was conducted of a cohort of patients followed up from 1978 to 2015, classified according to the modified Duke criteria. Clinical parameters, echocardiographic data, and blood cultures were assessed. Techniques of machine learning, such as the classification tree, were used to explain the association between clinical characteristics and in-hospital mortality. Additionally, the log-linear model and graphical random forests (GRaFo) representation were used to assess the degree of dependence among in-hospital outcomes of IE. Results: This study analyzed 653 patients: 449 (69.0%) with definite IE; 204 (31.0%) with possible IE; mean age, 41.3 ± 19.2 years; 420 (64%) men. Mode of IE acquisition: community-acquired (67.6%), nosocomial (17.0%), undetermined (15.4%). Complications occurred in 547 patients (83.7%), the most frequent being heart failure (47.0%), neurologic complications (30.7%), and dialysis-dependent renal failure (6.5%). In-hospital mortality was 36.0%. The classification tree analysis identified subgroups with higher in-hospital mortality: patients with community-acquired IE and peripheral stigmata on admission; and patients with nosocomial IE. The log-linear model showed that surgical treatment was related to higher in-hospital mortality in patients with neurologic complications. Conclusions: The use of a machine-learning model allowed identification of subgroups of patients at higher risk for in-hospital mortality. Peripheral stigmata, nosocomial IE, absence of vegetation, and surgery in the presence of neurologic complications are predictors of fatal outcomes in machine learning-based analysis.ElsevierRepositório da Universidade de LisboaResende, PlinioFortes, Claudio QueridoNascimento, Emilia Matos doSantos De Sousa, Catarina IsabelQuerido Fortes, Natalia RodriguesThomaz, Diego CentenaroBragança Pereira, Basilio dePinto, Fausto J.Oliveira, Glaucia Maria Moraes de2022-03-22T16:34:51Z20222022-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10451/51900engCJC Open. 2021 Sep 11;4(2):164-17210.1016/j.cjco.2021.08.0172589-790Xinfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-11-08T16:56:53Zoai:repositorio.ul.pt:10451/51900Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T22:03:06.246479Repositório 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 |
In-hospital outcomes of infective endocarditis from 1978 to 2015: analysis through machine-learning techniques |
| title |
In-hospital outcomes of infective endocarditis from 1978 to 2015: analysis through machine-learning techniques |
| spellingShingle |
In-hospital outcomes of infective endocarditis from 1978 to 2015: analysis through machine-learning techniques Resende, Plinio |
| title_short |
In-hospital outcomes of infective endocarditis from 1978 to 2015: analysis through machine-learning techniques |
| title_full |
In-hospital outcomes of infective endocarditis from 1978 to 2015: analysis through machine-learning techniques |
| title_fullStr |
In-hospital outcomes of infective endocarditis from 1978 to 2015: analysis through machine-learning techniques |
| title_full_unstemmed |
In-hospital outcomes of infective endocarditis from 1978 to 2015: analysis through machine-learning techniques |
| title_sort |
In-hospital outcomes of infective endocarditis from 1978 to 2015: analysis through machine-learning techniques |
| author |
Resende, Plinio |
| author_facet |
Resende, Plinio Fortes, Claudio Querido Nascimento, Emilia Matos do Santos De Sousa, Catarina Isabel Querido Fortes, Natalia Rodrigues Thomaz, Diego Centenaro Bragança Pereira, Basilio de Pinto, Fausto J. Oliveira, Glaucia Maria Moraes de |
| author_role |
author |
| author2 |
Fortes, Claudio Querido Nascimento, Emilia Matos do Santos De Sousa, Catarina Isabel Querido Fortes, Natalia Rodrigues Thomaz, Diego Centenaro Bragança Pereira, Basilio de Pinto, Fausto J. Oliveira, Glaucia Maria Moraes de |
| author2_role |
author author author author author author author author |
| dc.contributor.none.fl_str_mv |
Repositório da Universidade de Lisboa |
| dc.contributor.author.fl_str_mv |
Resende, Plinio Fortes, Claudio Querido Nascimento, Emilia Matos do Santos De Sousa, Catarina Isabel Querido Fortes, Natalia Rodrigues Thomaz, Diego Centenaro Bragança Pereira, Basilio de Pinto, Fausto J. Oliveira, Glaucia Maria Moraes de |
| description |
© 2021 The Authors. Published by Elsevier Inc. on behalf of the Canadian Cardiovascular Society. This is an open access article under the CC BY-NC- ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) |
| publishDate |
2022 |
| dc.date.none.fl_str_mv |
2022-03-22T16:34:51Z 2022 2022-01-01T00:00:00Z |
| dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
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info:eu-repo/semantics/article |
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article |
| status_str |
publishedVersion |
| dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10451/51900 |
| url |
http://hdl.handle.net/10451/51900 |
| dc.language.iso.fl_str_mv |
eng |
| language |
eng |
| dc.relation.none.fl_str_mv |
CJC Open. 2021 Sep 11;4(2):164-172 10.1016/j.cjco.2021.08.017 2589-790X |
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info:eu-repo/semantics/openAccess |
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openAccess |
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application/pdf |
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Elsevier |
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Elsevier |
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reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
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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 |
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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) |
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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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1799134581750759424 |