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dc.creatorVieceli, Tarsila
dc.creatorOliveira Filho, Cilomar Martins de
dc.creatorBerger, Mariana
dc.creatorPetersen Saadi, Marina
dc.creatorSalvador, Pedro Antonio
dc.creatorBressan Anizelli, Leonardo
dc.creatorFreitas Crivelaro, Pedro Castilhos de
dc.creatorButzke, Mauricio
dc.creatorSouza Zappelini, Roberta de
dc.creatorSantos Seligman, Beatriz Graeff dos
dc.creatorSeligman, Renato
dc.date.accessioned2020-09-28T19:30:53Z
dc.date.available2020-09-28T19:30:53Z
dc.date.created2020
dc.identifier.issn1413-8670spa
dc.identifier.otherhttps://doi.org/10.1016/j.bjid.2020.06.009spa
dc.identifier.urihttp://hdl.handle.net/20.500.12010/13931
dc.description.abstractObjectives: 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 populationsspa
dc.format.extent6 páginasspa
dc.format.mimetypeapplication/pdfspa
dc.language.isoengspa
dc.publisherThe Brazilian Journal of ifectius deseasesspa
dc.sourcereponame:Expeditio Repositorio Institucional UJTLspa
dc.sourceinstname:Universidad de Bogotá Jorge Tadeo Lozanospa
dc.subjectDiagnosisspa
dc.subjectCOVID-19spa
dc.subjectSARS-CoV-2spa
dc.subjectPredictive scorespa
dc.titleA predictive score for COVID-19 diagnosis using clinical, laboratory and chest image dataspa
dc.type.localArtículospa
dc.subject.lembSíndrome respiratorio agudo gravespa
dc.subject.lembCOVID-19spa
dc.subject.lembSARS-CoV-2spa
dc.subject.lembCoronavirusspa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.type.hasversioninfo:eu-repo/semantics/acceptedVersionspa
dc.rights.localAbierto (Texto Completo)spa
dc.identifier.doihttps://doi.org/10.1016/j.bjid.2020.06.009spa
dc.type.coarhttp://purl.org/coar/resource_type/c_2df8fbb1spa


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