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dc.contributor.authorVieceli, Tarsilapt_BR
dc.contributor.authorOliveira Filho, Cilomar Martins dept_BR
dc.contributor.authorBerger, Marianapt_BR
dc.contributor.authorSaadi, Marina Petersenpt_BR
dc.contributor.authorSalvador, Pedro Antoniopt_BR
dc.contributor.authorAnizelli, Leonardo Bressanpt_BR
dc.contributor.authorCrivelaro, Pedro Castilhos de Freitaspt_BR
dc.contributor.authorButzke, Mauríciopt_BR
dc.contributor.authorZappelini, Roberta de Souzapt_BR
dc.contributor.authorSeligman, Beatriz Graeff Santospt_BR
dc.contributor.authorSeligman, Renatopt_BR
dc.date.accessioned2020-12-24T04:21:38Zpt_BR
dc.date.issued2020pt_BR
dc.identifier.issn1413-8670pt_BR
dc.identifier.urihttp://hdl.handle.net/10183/216890pt_BR
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 populations.en
dc.format.mimetypeapplication/pdfpt_BR
dc.language.isoengpt_BR
dc.relation.ispartofThe Brazilian journal of infectious diseases. Vol. 24, n. 4 (2020), p. 343-348pt_BR
dc.rightsOpen Accessen
dc.subjectInfecções por coronaviruspt_BR
dc.subjectDiagnosisen
dc.subjectDiagnósticopt_BR
dc.subjectCOVID-19en
dc.subjectSARS-CoV-2en
dc.subjectPrognósticopt_BR
dc.subjectPredictive scoreen
dc.titleA predictive score for COVID-19 diagnosis using clinical, laboratory and chest image datapt_BR
dc.typeArtigo de periódicopt_BR
dc.identifier.nrb001120359pt_BR
dc.type.originNacionalpt_BR


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