A predictive model and risk factors for case fatality of covid-19

This study aimed to create an individualized analysis model of the risk of intensive care unit (ICU) admission or death for coronavirus disease 2019 (COVID-19) patients as a tool for the rapid clinical management of hospitalized patients in order to achieve a resilience of medical resources. This is...

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Detalhes bibliográficos
Autores: Álvarez-Mon, Melchor, Ortega, Miguel Ángel, Gasulla, Óscar, Fortuny-Profitós, Jordi, Mazaira-Font, Ferrán A., Saurina, P., Monserrat, Jorge, Plana, M.N., Troncoso, Daniel, Moreno, J.S., Muñoz, Benjamin, Arranz-Caso, José Alberto, Varona, José F., Lopez-Escobar, A., Asúnsolo, Ángel
Formato: artículo
Estado:Versión publicada
Fecha de publicación:2021
País:España
Recursos:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/250363
Acesso em linha:http://hdl.handle.net/10261/250363
Access Level:acceso abierto
Palavra-chave:COVID-19
C-reactive protein
oxygen saturation
ICU
Death
predictive model
http://metadata.un.org/sdg/3
Ensure healthy lives and promote well-being for all at all ages
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spelling A predictive model and risk factors for case fatality of covid-19Álvarez-Mon, MelchorOrtega, Miguel ÁngelGasulla, ÓscarFortuny-Profitós, JordiMazaira-Font, Ferrán A.Saurina, P.Monserrat, JorgePlana, M.N.Troncoso, DanielMoreno, J.S.Muñoz, BenjaminArranz-Caso, José AlbertoVarona, José F.Lopez-Escobar, A.Asúnsolo, ÁngelCOVID-19C-reactive proteinoxygen saturationICUDeathpredictive modelhttp://metadata.un.org/sdg/3Ensure healthy lives and promote well-being for all at all agesThis study aimed to create an individualized analysis model of the risk of intensive care unit (ICU) admission or death for coronavirus disease 2019 (COVID-19) patients as a tool for the rapid clinical management of hospitalized patients in order to achieve a resilience of medical resources. This is an observational, analytical, retrospective cohort study with longitudinal follow-up. Data were collected from the medical records of 3489 patients diagnosed with COVID-19 using RT-qPCR in the period of highest community transmission recorded in Europe to date: February–June 2020. The study was carried out in in two health areas of hospital care in the Madrid region: the central area of the Madrid capital (Hospitales de Madrid del Grupo HM Hospitales (CH-HM), n = 1931) and the metropolitan area of Madrid (Hospital Universitario Príncipe de Asturias (MH-HUPA) n = 1558). By using a regression model, we observed how the different patient variables had unequal importance. Among all the analyzed variables, basal oxygen saturation was found to have the highest relative importance with a value of 20.3%, followed by age (17.7%), lymphocyte/leukocyte ratio (14.4%), CRP value (12.5%), comorbidities (12.5%), and leukocyte count (8.9%). Three levels of risk of ICU/death were established: low-risk level (<5%), medium-risk level (5–20%), and high-risk level (>20%). At the high-risk level, 13% needed ICU admission, 29% died, and 37% had an ICU–death outcome. This predictive model allowed us to individualize the risk for worse outcome for hospitalized patients affected by COVID-19.National Institutes of Health (U.S.). PubMed CentralConsejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]2021202120212021info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Publisher's versioninfo:eu-repo/semantics/publishedVersionhttp://hdl.handle.net/10261/250363reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Ingléshttp://dx.doi.org/10.3390/jpm11010036Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/2503632026-05-22T06:33:51Z
dc.title.none.fl_str_mv A predictive model and risk factors for case fatality of covid-19
title A predictive model and risk factors for case fatality of covid-19
spellingShingle A predictive model and risk factors for case fatality of covid-19
Álvarez-Mon, Melchor
COVID-19
C-reactive protein
oxygen saturation
ICU
Death
predictive model
http://metadata.un.org/sdg/3
Ensure healthy lives and promote well-being for all at all ages
title_short A predictive model and risk factors for case fatality of covid-19
title_full A predictive model and risk factors for case fatality of covid-19
title_fullStr A predictive model and risk factors for case fatality of covid-19
title_full_unstemmed A predictive model and risk factors for case fatality of covid-19
title_sort A predictive model and risk factors for case fatality of covid-19
dc.creator.none.fl_str_mv Álvarez-Mon, Melchor
Ortega, Miguel Ángel
Gasulla, Óscar
Fortuny-Profitós, Jordi
Mazaira-Font, Ferrán A.
Saurina, P.
Monserrat, Jorge
Plana, M.N.
Troncoso, Daniel
Moreno, J.S.
Muñoz, Benjamin
Arranz-Caso, José Alberto
Varona, José F.
Lopez-Escobar, A.
Asúnsolo, Ángel
author Álvarez-Mon, Melchor
author_facet Álvarez-Mon, Melchor
Ortega, Miguel Ángel
Gasulla, Óscar
Fortuny-Profitós, Jordi
Mazaira-Font, Ferrán A.
Saurina, P.
Monserrat, Jorge
Plana, M.N.
Troncoso, Daniel
Moreno, J.S.
Muñoz, Benjamin
Arranz-Caso, José Alberto
Varona, José F.
Lopez-Escobar, A.
Asúnsolo, Ángel
author_role author
author2 Ortega, Miguel Ángel
Gasulla, Óscar
Fortuny-Profitós, Jordi
Mazaira-Font, Ferrán A.
Saurina, P.
Monserrat, Jorge
Plana, M.N.
Troncoso, Daniel
Moreno, J.S.
Muñoz, Benjamin
Arranz-Caso, José Alberto
Varona, José F.
Lopez-Escobar, A.
Asúnsolo, Ángel
author2_role author
author
author
author
author
author
author
author
author
author
author
author
author
author
dc.contributor.none.fl_str_mv Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv COVID-19
C-reactive protein
oxygen saturation
ICU
Death
predictive model
http://metadata.un.org/sdg/3
Ensure healthy lives and promote well-being for all at all ages
topic COVID-19
C-reactive protein
oxygen saturation
ICU
Death
predictive model
http://metadata.un.org/sdg/3
Ensure healthy lives and promote well-being for all at all ages
description This study aimed to create an individualized analysis model of the risk of intensive care unit (ICU) admission or death for coronavirus disease 2019 (COVID-19) patients as a tool for the rapid clinical management of hospitalized patients in order to achieve a resilience of medical resources. This is an observational, analytical, retrospective cohort study with longitudinal follow-up. Data were collected from the medical records of 3489 patients diagnosed with COVID-19 using RT-qPCR in the period of highest community transmission recorded in Europe to date: February–June 2020. The study was carried out in in two health areas of hospital care in the Madrid region: the central area of the Madrid capital (Hospitales de Madrid del Grupo HM Hospitales (CH-HM), n = 1931) and the metropolitan area of Madrid (Hospital Universitario Príncipe de Asturias (MH-HUPA) n = 1558). By using a regression model, we observed how the different patient variables had unequal importance. Among all the analyzed variables, basal oxygen saturation was found to have the highest relative importance with a value of 20.3%, followed by age (17.7%), lymphocyte/leukocyte ratio (14.4%), CRP value (12.5%), comorbidities (12.5%), and leukocyte count (8.9%). Three levels of risk of ICU/death were established: low-risk level (<5%), medium-risk level (5–20%), and high-risk level (>20%). At the high-risk level, 13% needed ICU admission, 29% died, and 37% had an ICU–death outcome. This predictive model allowed us to individualize the risk for worse outcome for hospitalized patients affected by COVID-19.
publishDate 2021
dc.date.none.fl_str_mv 2021
2021
2021
2021
dc.type.none.fl_str_mv info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
Publisher's version
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/250363
url http://hdl.handle.net/10261/250363
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv http://dx.doi.org/10.3390/jpm11010036
Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv National Institutes of Health (U.S.). PubMed Central
publisher.none.fl_str_mv National Institutes of Health (U.S.). PubMed Central
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
reponame_str DIGITAL.CSIC. Repositorio Institucional del CSIC
collection DIGITAL.CSIC. Repositorio Institucional del CSIC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.812429

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