Prediction model to discriminate leptospirosis from hantavirus
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Revista da Associação Médica Brasileira (Online) |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0104-42302021000901102 |
Resumo: | SUMMARY OBJECTIVE: The aim of this study was to build a prediction model to discriminate precociously hantavirus infection from leptospirosis, identifying the conditions and risk factors associated with these diseases. METHODS: A logistic regression model in which the response variable was the presence of hantavirus or leptospirosis was adjusted. RESULTS: As a result, the method selected the following variables that influenced the prediction formula: sociodemographic variables, clinical manifestations, and exposure to environmental risks. All variables considered in the model presented statistical significance with a p<0.05 value. The accuracy of the model to differentiate hantavirus from leptospirosis was 88.7%. CONCLUSIONS: Concluding that the development of statistical tools with high potential to predict the disease, and thus differentiate them precociously, can reduce hospital costs, speed up the patient’s care, reduce morbidity and mortality, and assist health professionals and public managers in decision-making. |
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Prediction model to discriminate leptospirosis from hantavirusLeptospirosisHantavirusesDifferential diagnosisPublic healthSUMMARY OBJECTIVE: The aim of this study was to build a prediction model to discriminate precociously hantavirus infection from leptospirosis, identifying the conditions and risk factors associated with these diseases. METHODS: A logistic regression model in which the response variable was the presence of hantavirus or leptospirosis was adjusted. RESULTS: As a result, the method selected the following variables that influenced the prediction formula: sociodemographic variables, clinical manifestations, and exposure to environmental risks. All variables considered in the model presented statistical significance with a p<0.05 value. The accuracy of the model to differentiate hantavirus from leptospirosis was 88.7%. CONCLUSIONS: Concluding that the development of statistical tools with high potential to predict the disease, and thus differentiate them precociously, can reduce hospital costs, speed up the patient’s care, reduce morbidity and mortality, and assist health professionals and public managers in decision-making.Associação Médica Brasileira2021-08-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0104-42302021000901102Revista da Associação Médica Brasileira v.67 n.8 2021reponame:Revista da Associação Médica Brasileira (Online)instname:Associação Médica Brasileira (AMB)instacron:AMB10.1590/1806-9282.20210257info:eu-repo/semantics/openAccessRabelo,Maura Regina GuimarãesAmâncio,Natália de Fátima GonçalvesRamos,Salvador Boccalettieng2021-10-27T00:00:00Zoai:scielo:S0104-42302021000901102Revistahttps://ramb.amb.org.br/ultimas-edicoes/#https://old.scielo.br/oai/scielo-oai.php||ramb@amb.org.br1806-92820104-4230opendoar:2021-10-27T00:00Revista da Associação Médica Brasileira (Online) - Associação Médica Brasileira (AMB)false |
dc.title.none.fl_str_mv |
Prediction model to discriminate leptospirosis from hantavirus |
title |
Prediction model to discriminate leptospirosis from hantavirus |
spellingShingle |
Prediction model to discriminate leptospirosis from hantavirus Rabelo,Maura Regina Guimarães Leptospirosis Hantaviruses Differential diagnosis Public health |
title_short |
Prediction model to discriminate leptospirosis from hantavirus |
title_full |
Prediction model to discriminate leptospirosis from hantavirus |
title_fullStr |
Prediction model to discriminate leptospirosis from hantavirus |
title_full_unstemmed |
Prediction model to discriminate leptospirosis from hantavirus |
title_sort |
Prediction model to discriminate leptospirosis from hantavirus |
author |
Rabelo,Maura Regina Guimarães |
author_facet |
Rabelo,Maura Regina Guimarães Amâncio,Natália de Fátima Gonçalves Ramos,Salvador Boccaletti |
author_role |
author |
author2 |
Amâncio,Natália de Fátima Gonçalves Ramos,Salvador Boccaletti |
author2_role |
author author |
dc.contributor.author.fl_str_mv |
Rabelo,Maura Regina Guimarães Amâncio,Natália de Fátima Gonçalves Ramos,Salvador Boccaletti |
dc.subject.por.fl_str_mv |
Leptospirosis Hantaviruses Differential diagnosis Public health |
topic |
Leptospirosis Hantaviruses Differential diagnosis Public health |
description |
SUMMARY OBJECTIVE: The aim of this study was to build a prediction model to discriminate precociously hantavirus infection from leptospirosis, identifying the conditions and risk factors associated with these diseases. METHODS: A logistic regression model in which the response variable was the presence of hantavirus or leptospirosis was adjusted. RESULTS: As a result, the method selected the following variables that influenced the prediction formula: sociodemographic variables, clinical manifestations, and exposure to environmental risks. All variables considered in the model presented statistical significance with a p<0.05 value. The accuracy of the model to differentiate hantavirus from leptospirosis was 88.7%. CONCLUSIONS: Concluding that the development of statistical tools with high potential to predict the disease, and thus differentiate them precociously, can reduce hospital costs, speed up the patient’s care, reduce morbidity and mortality, and assist health professionals and public managers in decision-making. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-08-01 |
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=S0104-42302021000901102 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0104-42302021000901102 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/1806-9282.20210257 |
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 |
Associação Médica Brasileira |
publisher.none.fl_str_mv |
Associação Médica Brasileira |
dc.source.none.fl_str_mv |
Revista da Associação Médica Brasileira v.67 n.8 2021 reponame:Revista da Associação Médica Brasileira (Online) instname:Associação Médica Brasileira (AMB) instacron:AMB |
instname_str |
Associação Médica Brasileira (AMB) |
instacron_str |
AMB |
institution |
AMB |
reponame_str |
Revista da Associação Médica Brasileira (Online) |
collection |
Revista da Associação Médica Brasileira (Online) |
repository.name.fl_str_mv |
Revista da Associação Médica Brasileira (Online) - Associação Médica Brasileira (AMB) |
repository.mail.fl_str_mv |
||ramb@amb.org.br |
_version_ |
1754212836507123712 |