Clinical diagnosis of COVID-19. A multivariate logistic regression analysis of symptoms of COVID-19 at presentation

Autores
Fleitas, Pedro Emanuel; Paz, Jorge Augusto; Simoy, Mario Ignacio; Vargas, Carlos; Cimino, Rubén Oscar; Krolewiecki, Alejandro Javier; Aparicio, Juan Pablo
Año de publicación
2021
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Introduction The objective of this cross-sectional study was to describe the main symptoms associated with COVID-19, and their diagnostic characteristics, to aid in the clinical diagnosis. Methods An analysis of all patients diagnosed by RT-PCR for SARS-CoV-2 between April and May 2020 in Argentina was conducted. The data includes clinical and demographic information from all subjects at the time of presentation (n=67318, where 12% were positive for SARS-CoV-2). The study population was divided into four age groups: pediatric (0-17 years), young adults (18-44 years), adults (45-64 years), and elderly (65-103 years). Multivariate logistic regression was used to measure the association of all symptoms and to create a diagnostic model based on symptoms.Results Symptoms associated with COVID-19 were anosmia, dysgeusia, headache, low-grade fever,odynophagia, and malaise. However, the presentation of these symptoms was different between thedifferent age groups. In turn, at the time of presentation, the symptoms associated with respiratoryproblems (chest pain, abdominal pain, and dyspnea) had a negative association with COVID-19 or did not present statistical relevance. On the other hand, the model based on 16 symptoms, age and sex, presented a sensitivity of 80% and a specificity of 46%.Conclusions There were significant differences between the different age groups. Additionally, therewere interactions between different symptoms that were highly associated with COVID-19. Finally, our findings showed that a regression model based on multiple factors (age, sex, interaction between symptoms) can be used as an accessory diagnostic method or a rapid screening of suspected COVID-19 cases.
Fil: Fleitas, Pedro Emanuel. Universidad Nacional de Salta. Sede Regional Orán. Instituto de Investigación de Enfermedades Tropicales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Paz, Jorge Augusto. Universidad Nacional de Salta. Facultad de Ciencias Económicas, Jurídicas y Sociales. Instituto de Estudios Laborales y del Desarrollo Económico; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Simoy, Mario Ignacio. Universidad Nacional de Salta. Facultad de Cs.exactas. Departamento de Física. Instituto de Energias No Convencionales; Argentina. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ciencias Exactas. Instituto Multidisciplinario sobre Ecosistemas y Desarrollo Sustentable. Grupo de Ecología Matemática; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Salta. Instituto de Investigaciones en Energía no Convencional. Grupo Vinculado al INENCO - Instituto de Investigaciones y Políticas del Ambiente Constituido | Universidad Nacional de Salta. Facultad de Cienicas Exactas. Departamento de Física. Instituto de Investigaciones en Energía no Convencional. Grupo Vinculado al INENCO - Instituto de Investigaciones y Políticas del Ambiente Constituido; Argentina
Fil: Vargas, Carlos. Universidad Nacional del Litoral. Facultad de Ciencias Económicas. Instituto de Investigación Estado, Territorio y Economía; Argentina
Fil: Cimino, Rubén Oscar. Universidad Nacional de Salta. Sede Regional Orán. Instituto de Investigación de Enfermedades Tropicales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Krolewiecki, Alejandro Javier. Universidad Nacional de Salta. Sede Regional Orán. Instituto de Investigación de Enfermedades Tropicales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Aparicio, Juan Pablo. Universidad Nacional de Salta. Facultad de Cs.exactas. Departamento de Física. Instituto de Energias No Convencionales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Materia
COVID-19
SYMPTOMS
CLINICAL DIAGNOSIS.
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/136995
Acceder
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repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Clinical diagnosis of COVID-19. A multivariate logistic regression analysis of symptoms of COVID-19 at presentationFleitas, Pedro EmanuelPaz, Jorge AugustoSimoy, Mario IgnacioVargas, CarlosCimino, Rubén OscarKrolewiecki, Alejandro JavierAparicio, Juan PabloCOVID-19SYMPTOMSCLINICAL DIAGNOSIS.https://purl.org/becyt/ford/3.2https://purl.org/becyt/ford/3Introduction The objective of this cross-sectional study was to describe the main symptoms associated with COVID-19, and their diagnostic characteristics, to aid in the clinical diagnosis. Methods An analysis of all patients diagnosed by RT-PCR for SARS-CoV-2 between April and May 2020 in Argentina was conducted. The data includes clinical and demographic information from all subjects at the time of presentation (n=67318, where 12% were positive for SARS-CoV-2). The study population was divided into four age groups: pediatric (0-17 years), young adults (18-44 years), adults (45-64 years), and elderly (65-103 years). Multivariate logistic regression was used to measure the association of all symptoms and to create a diagnostic model based on symptoms.Results Symptoms associated with COVID-19 were anosmia, dysgeusia, headache, low-grade fever,odynophagia, and malaise. However, the presentation of these symptoms was different between thedifferent age groups. In turn, at the time of presentation, the symptoms associated with respiratoryproblems (chest pain, abdominal pain, and dyspnea) had a negative association with COVID-19 or did not present statistical relevance. On the other hand, the model based on 16 symptoms, age and sex, presented a sensitivity of 80% and a specificity of 46%.Conclusions There were significant differences between the different age groups. Additionally, therewere interactions between different symptoms that were highly associated with COVID-19. Finally, our findings showed that a regression model based on multiple factors (age, sex, interaction between symptoms) can be used as an accessory diagnostic method or a rapid screening of suspected COVID-19 cases.Fil: Fleitas, Pedro Emanuel. Universidad Nacional de Salta. Sede Regional Orán. Instituto de Investigación de Enfermedades Tropicales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Paz, Jorge Augusto. Universidad Nacional de Salta. Facultad de Ciencias Económicas, Jurídicas y Sociales. Instituto de Estudios Laborales y del Desarrollo Económico; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Simoy, Mario Ignacio. Universidad Nacional de Salta. Facultad de Cs.exactas. Departamento de Física. Instituto de Energias No Convencionales; Argentina. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ciencias Exactas. Instituto Multidisciplinario sobre Ecosistemas y Desarrollo Sustentable. Grupo de Ecología Matemática; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Salta. Instituto de Investigaciones en Energía no Convencional. Grupo Vinculado al INENCO - Instituto de Investigaciones y Políticas del Ambiente Constituido | Universidad Nacional de Salta. Facultad de Cienicas Exactas. Departamento de Física. Instituto de Investigaciones en Energía no Convencional. Grupo Vinculado al INENCO - Instituto de Investigaciones y Políticas del Ambiente Constituido; ArgentinaFil: Vargas, Carlos. Universidad Nacional del Litoral. Facultad de Ciencias Económicas. Instituto de Investigación Estado, Territorio y Economía; ArgentinaFil: Cimino, Rubén Oscar. Universidad Nacional de Salta. Sede Regional Orán. Instituto de Investigación de Enfermedades Tropicales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Krolewiecki, Alejandro Javier. Universidad Nacional de Salta. Sede Regional Orán. Instituto de Investigación de Enfermedades Tropicales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Aparicio, Juan Pablo. Universidad Nacional de Salta. Facultad de Cs.exactas. Departamento de Física. Instituto de Energias No Convencionales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaEuropean Academy of HIV/AIDS and Infectious Diseases2021-07info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/136995Fleitas, Pedro Emanuel; Paz, Jorge Augusto; Simoy, Mario Ignacio; Vargas, Carlos; Cimino, Rubén Oscar; et al.; Clinical diagnosis of COVID-19. A multivariate logistic regression analysis of symptoms of COVID-19 at presentation; European Academy of HIV/AIDS and Infectious Diseases; Germs; 11; 2; 7-2021; 221-2372248-2997CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/http://www.germs.ro/en/Homepage/info:eu-repo/semantics/altIdentifier/doi/ 10.18683/germs.2021.1259info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:03:21Zoai:ri.conicet.gov.ar:11336/136995instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 10:03:21.476CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Clinical diagnosis of COVID-19. A multivariate logistic regression analysis of symptoms of COVID-19 at presentation
title Clinical diagnosis of COVID-19. A multivariate logistic regression analysis of symptoms of COVID-19 at presentation
spellingShingle Clinical diagnosis of COVID-19. A multivariate logistic regression analysis of symptoms of COVID-19 at presentation
Fleitas, Pedro Emanuel
COVID-19
SYMPTOMS
CLINICAL DIAGNOSIS.
title_short Clinical diagnosis of COVID-19. A multivariate logistic regression analysis of symptoms of COVID-19 at presentation
title_full Clinical diagnosis of COVID-19. A multivariate logistic regression analysis of symptoms of COVID-19 at presentation
title_fullStr Clinical diagnosis of COVID-19. A multivariate logistic regression analysis of symptoms of COVID-19 at presentation
title_full_unstemmed Clinical diagnosis of COVID-19. A multivariate logistic regression analysis of symptoms of COVID-19 at presentation
title_sort Clinical diagnosis of COVID-19. A multivariate logistic regression analysis of symptoms of COVID-19 at presentation
dc.creator.none.fl_str_mv Fleitas, Pedro Emanuel
Paz, Jorge Augusto
Simoy, Mario Ignacio
Vargas, Carlos
Cimino, Rubén Oscar
Krolewiecki, Alejandro Javier
Aparicio, Juan Pablo
author Fleitas, Pedro Emanuel
author_facet Fleitas, Pedro Emanuel
Paz, Jorge Augusto
Simoy, Mario Ignacio
Vargas, Carlos
Cimino, Rubén Oscar
Krolewiecki, Alejandro Javier
Aparicio, Juan Pablo
author_role author
author2 Paz, Jorge Augusto
Simoy, Mario Ignacio
Vargas, Carlos
Cimino, Rubén Oscar
Krolewiecki, Alejandro Javier
Aparicio, Juan Pablo
author2_role author
author
author
author
author
author
dc.subject.none.fl_str_mv COVID-19
SYMPTOMS
CLINICAL DIAGNOSIS.
topic COVID-19
SYMPTOMS
CLINICAL DIAGNOSIS.
purl_subject.fl_str_mv https://purl.org/becyt/ford/3.2
https://purl.org/becyt/ford/3
dc.description.none.fl_txt_mv Introduction The objective of this cross-sectional study was to describe the main symptoms associated with COVID-19, and their diagnostic characteristics, to aid in the clinical diagnosis. Methods An analysis of all patients diagnosed by RT-PCR for SARS-CoV-2 between April and May 2020 in Argentina was conducted. The data includes clinical and demographic information from all subjects at the time of presentation (n=67318, where 12% were positive for SARS-CoV-2). The study population was divided into four age groups: pediatric (0-17 years), young adults (18-44 years), adults (45-64 years), and elderly (65-103 years). Multivariate logistic regression was used to measure the association of all symptoms and to create a diagnostic model based on symptoms.Results Symptoms associated with COVID-19 were anosmia, dysgeusia, headache, low-grade fever,odynophagia, and malaise. However, the presentation of these symptoms was different between thedifferent age groups. In turn, at the time of presentation, the symptoms associated with respiratoryproblems (chest pain, abdominal pain, and dyspnea) had a negative association with COVID-19 or did not present statistical relevance. On the other hand, the model based on 16 symptoms, age and sex, presented a sensitivity of 80% and a specificity of 46%.Conclusions There were significant differences between the different age groups. Additionally, therewere interactions between different symptoms that were highly associated with COVID-19. Finally, our findings showed that a regression model based on multiple factors (age, sex, interaction between symptoms) can be used as an accessory diagnostic method or a rapid screening of suspected COVID-19 cases.
Fil: Fleitas, Pedro Emanuel. Universidad Nacional de Salta. Sede Regional Orán. Instituto de Investigación de Enfermedades Tropicales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Paz, Jorge Augusto. Universidad Nacional de Salta. Facultad de Ciencias Económicas, Jurídicas y Sociales. Instituto de Estudios Laborales y del Desarrollo Económico; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Simoy, Mario Ignacio. Universidad Nacional de Salta. Facultad de Cs.exactas. Departamento de Física. Instituto de Energias No Convencionales; Argentina. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ciencias Exactas. Instituto Multidisciplinario sobre Ecosistemas y Desarrollo Sustentable. Grupo de Ecología Matemática; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Salta. Instituto de Investigaciones en Energía no Convencional. Grupo Vinculado al INENCO - Instituto de Investigaciones y Políticas del Ambiente Constituido | Universidad Nacional de Salta. Facultad de Cienicas Exactas. Departamento de Física. Instituto de Investigaciones en Energía no Convencional. Grupo Vinculado al INENCO - Instituto de Investigaciones y Políticas del Ambiente Constituido; Argentina
Fil: Vargas, Carlos. Universidad Nacional del Litoral. Facultad de Ciencias Económicas. Instituto de Investigación Estado, Territorio y Economía; Argentina
Fil: Cimino, Rubén Oscar. Universidad Nacional de Salta. Sede Regional Orán. Instituto de Investigación de Enfermedades Tropicales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Krolewiecki, Alejandro Javier. Universidad Nacional de Salta. Sede Regional Orán. Instituto de Investigación de Enfermedades Tropicales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Aparicio, Juan Pablo. Universidad Nacional de Salta. Facultad de Cs.exactas. Departamento de Física. Instituto de Energias No Convencionales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
description Introduction The objective of this cross-sectional study was to describe the main symptoms associated with COVID-19, and their diagnostic characteristics, to aid in the clinical diagnosis. Methods An analysis of all patients diagnosed by RT-PCR for SARS-CoV-2 between April and May 2020 in Argentina was conducted. The data includes clinical and demographic information from all subjects at the time of presentation (n=67318, where 12% were positive for SARS-CoV-2). The study population was divided into four age groups: pediatric (0-17 years), young adults (18-44 years), adults (45-64 years), and elderly (65-103 years). Multivariate logistic regression was used to measure the association of all symptoms and to create a diagnostic model based on symptoms.Results Symptoms associated with COVID-19 were anosmia, dysgeusia, headache, low-grade fever,odynophagia, and malaise. However, the presentation of these symptoms was different between thedifferent age groups. In turn, at the time of presentation, the symptoms associated with respiratoryproblems (chest pain, abdominal pain, and dyspnea) had a negative association with COVID-19 or did not present statistical relevance. On the other hand, the model based on 16 symptoms, age and sex, presented a sensitivity of 80% and a specificity of 46%.Conclusions There were significant differences between the different age groups. Additionally, therewere interactions between different symptoms that were highly associated with COVID-19. Finally, our findings showed that a regression model based on multiple factors (age, sex, interaction between symptoms) can be used as an accessory diagnostic method or a rapid screening of suspected COVID-19 cases.
publishDate 2021
dc.date.none.fl_str_mv 2021-07
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/136995
Fleitas, Pedro Emanuel; Paz, Jorge Augusto; Simoy, Mario Ignacio; Vargas, Carlos; Cimino, Rubén Oscar; et al.; Clinical diagnosis of COVID-19. A multivariate logistic regression analysis of symptoms of COVID-19 at presentation; European Academy of HIV/AIDS and Infectious Diseases; Germs; 11; 2; 7-2021; 221-237
2248-2997
CONICET Digital
CONICET
url http://hdl.handle.net/11336/136995
identifier_str_mv Fleitas, Pedro Emanuel; Paz, Jorge Augusto; Simoy, Mario Ignacio; Vargas, Carlos; Cimino, Rubén Oscar; et al.; Clinical diagnosis of COVID-19. A multivariate logistic regression analysis of symptoms of COVID-19 at presentation; European Academy of HIV/AIDS and Infectious Diseases; Germs; 11; 2; 7-2021; 221-237
2248-2997
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://www.germs.ro/en/Homepage/
info:eu-repo/semantics/altIdentifier/doi/ 10.18683/germs.2021.1259
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv European Academy of HIV/AIDS and Infectious Diseases
publisher.none.fl_str_mv European Academy of HIV/AIDS and Infectious Diseases
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.069144

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