A predictive score for COVID-19 diagnosis using clinical, laboratory and chest image data
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Outros Autores: | , , , , , , , , , |
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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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 |
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2020-12-24T04:21:38Z |
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2020 |
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http://hdl.handle.net/10183/216890 |
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001120359 |
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The Brazilian journal of infectious diseases. Vol. 24, n. 4 (2020), p. 343-348 |
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