Prediction of intensive care admission and hospital mortality in COVID-19 patients using demographics and baseline laboratory data

Detalhes bibliográficos
Autor(a) principal: Avelino-Silva, Vivian I.
Data de Publicação: 2023
Outros Autores: Avelino-Silva, Thiago J., Aliberti, Marlon J.R., Ferreira, Juliana C., Cobello Junior, Vilson, Silva, Katia R., Pompeu, Jose E., Antonangelo, Leila, Magri, Marcello M., Barros Filho, Tarcisio E.P., Souza, Heraldo P., Kallás, Esper G., HCFMUSP COVID-19 Study Group
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Clinics
Texto Completo: https://www.revistas.usp.br/clinics/article/view/213762
Resumo: Introduction: Optimized allocation of medical resources to patients with COVID-19 has been a critical concern since the onset of the pandemic. Methods: In this retrospective cohort study, the authors used data from a Brazilian tertiary university hospital to explore predictors of Intensive Care Unit (ICU) admission and hospital mortality in patients admitted for COVID-19. Our primary aim was to create and validate prediction scores for use in hospitals and emergency departments to aid clinical decisions and resource allocation. Results: The study cohort included 3,022 participants, of whom 2,485 were admitted to the ICU; 1968 survived, and 1054 died in the hospital. From the complete cohort, 1,496 patients were randomly assigned to the derivation sample and 1,526 to the validation sample. The final scores included age, comorbidities, and baseline laboratory data. The areas under the receiver operating characteristic curves were very similar for the derivation and validation samples. Scores for ICU admission had a 75% accuracy in the validation sample, whereas scores for death had a 77% accuracy in the validation sample. The authors found that including baseline flu-like symptoms in the scores added no significant benefit to their accuracy. Furthermore, our scores were more accurate than the previously published NEWS-2 and 4C Mortality Scores. Discussion and conclusions: The authors developed and validated prognostic scores that use readily available clinical and laboratory information to predict ICU admission and mortality in COVID-19. These scores can become valuable tools to support clinical decisions and improve the allocation of limited health resources.
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spelling Prediction of intensive care admission and hospital mortality in COVID-19 patients using demographics and baseline laboratory dataCOVID-19SARS-CoV-2Critical CareMortalityIntensive CareRisk ScorePredictionIntroduction: Optimized allocation of medical resources to patients with COVID-19 has been a critical concern since the onset of the pandemic. Methods: In this retrospective cohort study, the authors used data from a Brazilian tertiary university hospital to explore predictors of Intensive Care Unit (ICU) admission and hospital mortality in patients admitted for COVID-19. Our primary aim was to create and validate prediction scores for use in hospitals and emergency departments to aid clinical decisions and resource allocation. Results: The study cohort included 3,022 participants, of whom 2,485 were admitted to the ICU; 1968 survived, and 1054 died in the hospital. From the complete cohort, 1,496 patients were randomly assigned to the derivation sample and 1,526 to the validation sample. The final scores included age, comorbidities, and baseline laboratory data. The areas under the receiver operating characteristic curves were very similar for the derivation and validation samples. Scores for ICU admission had a 75% accuracy in the validation sample, whereas scores for death had a 77% accuracy in the validation sample. The authors found that including baseline flu-like symptoms in the scores added no significant benefit to their accuracy. Furthermore, our scores were more accurate than the previously published NEWS-2 and 4C Mortality Scores. Discussion and conclusions: The authors developed and validated prognostic scores that use readily available clinical and laboratory information to predict ICU admission and mortality in COVID-19. These scores can become valuable tools to support clinical decisions and improve the allocation of limited health resources.Hospital das Clínicas, Faculdade de Medicina, Universidade de São Paulo2023-03-10info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://www.revistas.usp.br/clinics/article/view/21376210.1016/j.clinsp.2023.100183Clinics; Vol. 78 (2023); 100183Clinics; v. 78 (2023); 100183Clinics; Vol. 78 (2023); 1001831980-53221807-5932reponame:Clinicsinstname:Universidade de São Paulo (USP)instacron:USPenghttps://www.revistas.usp.br/clinics/article/view/213762/195923Copyright (c) 2023 Clinicsinfo:eu-repo/semantics/openAccessAvelino-Silva, Vivian I.Avelino-Silva, Thiago J.Aliberti, Marlon J.R.Ferreira, Juliana C.Cobello Junior, VilsonSilva, Katia R.Pompeu, Jose E.Antonangelo, LeilaMagri, Marcello M.Barros Filho, Tarcisio E.P.Souza, Heraldo P.Kallás, Esper G.HCFMUSP COVID-19 Study Group2023-07-06T13:05:38Zoai:revistas.usp.br:article/213762Revistahttps://www.revistas.usp.br/clinicsPUBhttps://www.revistas.usp.br/clinics/oai||clinics@hc.fm.usp.br1980-53221807-5932opendoar:2023-07-06T13:05:38Clinics - Universidade de São Paulo (USP)false
dc.title.none.fl_str_mv Prediction of intensive care admission and hospital mortality in COVID-19 patients using demographics and baseline laboratory data
title Prediction of intensive care admission and hospital mortality in COVID-19 patients using demographics and baseline laboratory data
spellingShingle Prediction of intensive care admission and hospital mortality in COVID-19 patients using demographics and baseline laboratory data
Avelino-Silva, Vivian I.
COVID-19
SARS-CoV-2
Critical Care
Mortality
Intensive Care
Risk Score
Prediction
title_short Prediction of intensive care admission and hospital mortality in COVID-19 patients using demographics and baseline laboratory data
title_full Prediction of intensive care admission and hospital mortality in COVID-19 patients using demographics and baseline laboratory data
title_fullStr Prediction of intensive care admission and hospital mortality in COVID-19 patients using demographics and baseline laboratory data
title_full_unstemmed Prediction of intensive care admission and hospital mortality in COVID-19 patients using demographics and baseline laboratory data
title_sort Prediction of intensive care admission and hospital mortality in COVID-19 patients using demographics and baseline laboratory data
author Avelino-Silva, Vivian I.
author_facet Avelino-Silva, Vivian I.
Avelino-Silva, Thiago J.
Aliberti, Marlon J.R.
Ferreira, Juliana C.
Cobello Junior, Vilson
Silva, Katia R.
Pompeu, Jose E.
Antonangelo, Leila
Magri, Marcello M.
Barros Filho, Tarcisio E.P.
Souza, Heraldo P.
Kallás, Esper G.
HCFMUSP COVID-19 Study Group
author_role author
author2 Avelino-Silva, Thiago J.
Aliberti, Marlon J.R.
Ferreira, Juliana C.
Cobello Junior, Vilson
Silva, Katia R.
Pompeu, Jose E.
Antonangelo, Leila
Magri, Marcello M.
Barros Filho, Tarcisio E.P.
Souza, Heraldo P.
Kallás, Esper G.
HCFMUSP COVID-19 Study Group
author2_role author
author
author
author
author
author
author
author
author
author
author
author
dc.contributor.author.fl_str_mv Avelino-Silva, Vivian I.
Avelino-Silva, Thiago J.
Aliberti, Marlon J.R.
Ferreira, Juliana C.
Cobello Junior, Vilson
Silva, Katia R.
Pompeu, Jose E.
Antonangelo, Leila
Magri, Marcello M.
Barros Filho, Tarcisio E.P.
Souza, Heraldo P.
Kallás, Esper G.
HCFMUSP COVID-19 Study Group
dc.subject.por.fl_str_mv COVID-19
SARS-CoV-2
Critical Care
Mortality
Intensive Care
Risk Score
Prediction
topic COVID-19
SARS-CoV-2
Critical Care
Mortality
Intensive Care
Risk Score
Prediction
description Introduction: Optimized allocation of medical resources to patients with COVID-19 has been a critical concern since the onset of the pandemic. Methods: In this retrospective cohort study, the authors used data from a Brazilian tertiary university hospital to explore predictors of Intensive Care Unit (ICU) admission and hospital mortality in patients admitted for COVID-19. Our primary aim was to create and validate prediction scores for use in hospitals and emergency departments to aid clinical decisions and resource allocation. Results: The study cohort included 3,022 participants, of whom 2,485 were admitted to the ICU; 1968 survived, and 1054 died in the hospital. From the complete cohort, 1,496 patients were randomly assigned to the derivation sample and 1,526 to the validation sample. The final scores included age, comorbidities, and baseline laboratory data. The areas under the receiver operating characteristic curves were very similar for the derivation and validation samples. Scores for ICU admission had a 75% accuracy in the validation sample, whereas scores for death had a 77% accuracy in the validation sample. The authors found that including baseline flu-like symptoms in the scores added no significant benefit to their accuracy. Furthermore, our scores were more accurate than the previously published NEWS-2 and 4C Mortality Scores. Discussion and conclusions: The authors developed and validated prognostic scores that use readily available clinical and laboratory information to predict ICU admission and mortality in COVID-19. These scores can become valuable tools to support clinical decisions and improve the allocation of limited health resources.
publishDate 2023
dc.date.none.fl_str_mv 2023-03-10
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv https://www.revistas.usp.br/clinics/article/view/213762
10.1016/j.clinsp.2023.100183
url https://www.revistas.usp.br/clinics/article/view/213762
identifier_str_mv 10.1016/j.clinsp.2023.100183
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv https://www.revistas.usp.br/clinics/article/view/213762/195923
dc.rights.driver.fl_str_mv Copyright (c) 2023 Clinics
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Copyright (c) 2023 Clinics
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Hospital das Clínicas, Faculdade de Medicina, Universidade de São Paulo
publisher.none.fl_str_mv Hospital das Clínicas, Faculdade de Medicina, Universidade de São Paulo
dc.source.none.fl_str_mv Clinics; Vol. 78 (2023); 100183
Clinics; v. 78 (2023); 100183
Clinics; Vol. 78 (2023); 100183
1980-5322
1807-5932
reponame:Clinics
instname:Universidade de São Paulo (USP)
instacron:USP
instname_str Universidade de São Paulo (USP)
instacron_str USP
institution USP
reponame_str Clinics
collection Clinics
repository.name.fl_str_mv Clinics - Universidade de São Paulo (USP)
repository.mail.fl_str_mv ||clinics@hc.fm.usp.br
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