A predictive score for COVID-19 diagnosis using clinical, laboratory and chest image data

Detalhes bibliográficos
Autor(a) principal: Vieceli, Tarsila
Data de Publicação: 2020
Outros Autores: Oliveira Filho, Cilomar Martins de, Berger, Mariana, Saadi, Marina Petersen, Salvador, Pedro Antonio, Anizelli, Leonardo Bressan, Crivelaro, Pedro Castilhos de Freitas, Butzke, Maurício, Zappelini, Roberta de Souza, Seligman, Beatriz Graeff Santos, Seligman, Renato
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da UFRGS
Texto Completo: http://hdl.handle.net/10183/216890
Resumo: Objectives: Differential diagnosis of COVID-19 includes a broad range of conditions. Prioritizing containment efforts, protective personal equipment and testing can be challenging. Our aim was to develop a tool to identify patients with higher probability of COVID-19 diagnosis at admission. Methods: This cross-sectional study analyzed data from 100 patients admitted with suspected COVID-19. Predictive models of COVID-19 diagnosis were performed based on radiology, clinical and laboratory findings; bootstrapping was performed in order to account for overfitting. Results: A total of 29% of patients tested positive for SARS-CoV-2. Variables associated with COVID-19 diagnosis in multivariate analysis were leukocyte count ≤7.7 × 103 mm–3, LDH >273 U/L, and chest radiographic abnormality. A predictive score was built for COVID-19 diagnosis, with an area under ROC curve of 0.847 (95% CI 0.77–0.92), 96% sensitivity and 73.5% specificity. After bootstrapping, the corrected AUC for this model was 0.827 (95% CI 0.75–0.90). Conclusions: Considering unavailability of RT-PCR at some centers, as well as its questionable early sensitivity, other tools might be used in order to identify patients who should be prioritized for testing, re-testing and admission to isolated wards. We propose a predictive score that can be easily applied in clinical practice. This score is yet to be validated in larger populations.
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spelling Vieceli, TarsilaOliveira Filho, Cilomar Martins deBerger, MarianaSaadi, Marina PetersenSalvador, Pedro AntonioAnizelli, Leonardo BressanCrivelaro, Pedro Castilhos de FreitasButzke, MaurícioZappelini, Roberta de SouzaSeligman, Beatriz Graeff SantosSeligman, Renato2020-12-24T04:21:38Z20201413-8670http://hdl.handle.net/10183/216890001120359Objectives: Differential diagnosis of COVID-19 includes a broad range of conditions. Prioritizing containment efforts, protective personal equipment and testing can be challenging. Our aim was to develop a tool to identify patients with higher probability of COVID-19 diagnosis at admission. Methods: This cross-sectional study analyzed data from 100 patients admitted with suspected COVID-19. Predictive models of COVID-19 diagnosis were performed based on radiology, clinical and laboratory findings; bootstrapping was performed in order to account for overfitting. Results: A total of 29% of patients tested positive for SARS-CoV-2. Variables associated with COVID-19 diagnosis in multivariate analysis were leukocyte count ≤7.7 × 103 mm–3, LDH >273 U/L, and chest radiographic abnormality. A predictive score was built for COVID-19 diagnosis, with an area under ROC curve of 0.847 (95% CI 0.77–0.92), 96% sensitivity and 73.5% specificity. After bootstrapping, the corrected AUC for this model was 0.827 (95% CI 0.75–0.90). Conclusions: Considering unavailability of RT-PCR at some centers, as well as its questionable early sensitivity, other tools might be used in order to identify patients who should be prioritized for testing, re-testing and admission to isolated wards. We propose a predictive score that can be easily applied in clinical practice. This score is yet to be validated in larger populations.application/pdfengThe Brazilian journal of infectious diseases. Vol. 24, n. 4 (2020), p. 343-348Infecções por coronavirusDiagnósticoPrognósticoDiagnosisCOVID-19SARS-CoV-2Predictive scoreA predictive score for COVID-19 diagnosis using clinical, laboratory and chest image datainfo:eu-repo/semantics/articleinfo:eu-repo/semantics/otherinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da UFRGSinstname:Universidade Federal do Rio Grande do Sul (UFRGS)instacron:UFRGSTEXT001120359.pdf.txt001120359.pdf.txtExtracted Texttext/plain26043http://www.lume.ufrgs.br/bitstream/10183/216890/2/001120359.pdf.txt0011fdafc078b9c42b67c39ceec41ea3MD52ORIGINAL001120359.pdfTexto completo (inglês)application/pdf369169http://www.lume.ufrgs.br/bitstream/10183/216890/1/001120359.pdfebc43741712181e02b988461711e5308MD5110183/2168902020-12-25 05:12:17.473656oai:www.lume.ufrgs.br:10183/216890Repositório de PublicaçõesPUBhttps://lume.ufrgs.br/oai/requestopendoar:2020-12-25T07:12:17Repositório Institucional da UFRGS - Universidade Federal do Rio Grande do Sul (UFRGS)false
dc.title.pt_BR.fl_str_mv A predictive score for COVID-19 diagnosis using clinical, laboratory and chest image data
title A predictive score for COVID-19 diagnosis using clinical, laboratory and chest image data
spellingShingle A predictive score for COVID-19 diagnosis using clinical, laboratory and chest image data
Vieceli, Tarsila
Infecções por coronavirus
Diagnóstico
Prognóstico
Diagnosis
COVID-19
SARS-CoV-2
Predictive score
title_short A predictive score for COVID-19 diagnosis using clinical, laboratory and chest image data
title_full A predictive score for COVID-19 diagnosis using clinical, laboratory and chest image data
title_fullStr A predictive score for COVID-19 diagnosis using clinical, laboratory and chest image data
title_full_unstemmed A predictive score for COVID-19 diagnosis using clinical, laboratory and chest image data
title_sort A predictive score for COVID-19 diagnosis using clinical, laboratory and chest image data
author Vieceli, Tarsila
author_facet Vieceli, Tarsila
Oliveira Filho, Cilomar Martins de
Berger, Mariana
Saadi, Marina Petersen
Salvador, Pedro Antonio
Anizelli, Leonardo Bressan
Crivelaro, Pedro Castilhos de Freitas
Butzke, Maurício
Zappelini, Roberta de Souza
Seligman, Beatriz Graeff Santos
Seligman, Renato
author_role author
author2 Oliveira Filho, Cilomar Martins de
Berger, Mariana
Saadi, Marina Petersen
Salvador, Pedro Antonio
Anizelli, Leonardo Bressan
Crivelaro, Pedro Castilhos de Freitas
Butzke, Maurício
Zappelini, Roberta de Souza
Seligman, Beatriz Graeff Santos
Seligman, Renato
author2_role author
author
author
author
author
author
author
author
author
author
dc.contributor.author.fl_str_mv Vieceli, Tarsila
Oliveira Filho, Cilomar Martins de
Berger, Mariana
Saadi, Marina Petersen
Salvador, Pedro Antonio
Anizelli, Leonardo Bressan
Crivelaro, Pedro Castilhos de Freitas
Butzke, Maurício
Zappelini, Roberta de Souza
Seligman, Beatriz Graeff Santos
Seligman, Renato
dc.subject.por.fl_str_mv Infecções por coronavirus
Diagnóstico
Prognóstico
topic Infecções por coronavirus
Diagnóstico
Prognóstico
Diagnosis
COVID-19
SARS-CoV-2
Predictive score
dc.subject.eng.fl_str_mv Diagnosis
COVID-19
SARS-CoV-2
Predictive score
description Objectives: Differential diagnosis of COVID-19 includes a broad range of conditions. Prioritizing containment efforts, protective personal equipment and testing can be challenging. Our aim was to develop a tool to identify patients with higher probability of COVID-19 diagnosis at admission. Methods: This cross-sectional study analyzed data from 100 patients admitted with suspected COVID-19. Predictive models of COVID-19 diagnosis were performed based on radiology, clinical and laboratory findings; bootstrapping was performed in order to account for overfitting. Results: A total of 29% of patients tested positive for SARS-CoV-2. Variables associated with COVID-19 diagnosis in multivariate analysis were leukocyte count ≤7.7 × 103 mm–3, LDH >273 U/L, and chest radiographic abnormality. A predictive score was built for COVID-19 diagnosis, with an area under ROC curve of 0.847 (95% CI 0.77–0.92), 96% sensitivity and 73.5% specificity. After bootstrapping, the corrected AUC for this model was 0.827 (95% CI 0.75–0.90). Conclusions: Considering unavailability of RT-PCR at some centers, as well as its questionable early sensitivity, other tools might be used in order to identify patients who should be prioritized for testing, re-testing and admission to isolated wards. We propose a predictive score that can be easily applied in clinical practice. This score is yet to be validated in larger populations.
publishDate 2020
dc.date.accessioned.fl_str_mv 2020-12-24T04:21:38Z
dc.date.issued.fl_str_mv 2020
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dc.identifier.issn.pt_BR.fl_str_mv 1413-8670
dc.identifier.nrb.pt_BR.fl_str_mv 001120359
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dc.language.iso.fl_str_mv eng
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dc.relation.ispartof.pt_BR.fl_str_mv The Brazilian journal of infectious diseases. Vol. 24, n. 4 (2020), p. 343-348
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eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Repositório Institucional da UFRGS
instname:Universidade Federal do Rio Grande do Sul (UFRGS)
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reponame_str Repositório Institucional da UFRGS
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