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Emerg Radiol ; 27(6): 641-651, 2020 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-32691211

RESUMO

PURPOSE: We aimed to investigate the diagnostic performance of chest CT compared with first RT-PCR results in adult patients suspected of COVID-19 infection in an ED setting. We also constructed a predictive machine learning model based on chest CT and additional data to improve the diagnostic accuracy of chest CT. METHODS: This study's cohort consisted of 319 patients who underwent chest CT and RT-PCR testing at the ED. Patient characteristics, demographics, symptoms, vital signs, laboratory tests, and chest CT results (CO-RADS) were collected. With first RT-PCR as reference standard, the diagnostic performance of chest CT using the CO-RADS score was assessed. Additionally, a predictive machine learning model was constructed using logistic regression. RESULTS: Chest CT, with first RT-PCR as a reference, had a sensitivity, specificity, PPV, and NPV of 90.2%, 88.2%, 84.5%, and 92.7%, respectively. The prediction model with CO-RADS, ferritin, leucocyte count, CK, days of complaints, and diarrhea as predictors had a sensitivity, specificity, PPV, and NPV of 89.3%, 93.4%, 90.8%, and 92.3%, respectively. CONCLUSION: Chest CT, using the CO-RADS scoring system, is a sensitive and specific method that can aid in the diagnosis of COVID-19, especially if RT-PCR tests are scarce during an outbreak. Combining a predictive machine learning model could further improve the accuracy of diagnostic chest CT for COVID-19. Further candidate predictors should be analyzed to improve our model. However, RT-PCR should remain the primary standard of testing as up to 9% of RT-PCR positive patients are not diagnosed by chest CT or our machine learning model.


Assuntos
Infecções por Coronavirus/diagnóstico por imagem , Serviço Hospitalar de Emergência , Pneumonia Viral/diagnóstico por imagem , Radiografia Torácica/métodos , Tomografia Computadorizada por Raios X/métodos , Triagem , Adolescente , Adulto , Idoso , Idoso de 80 Anos ou mais , Betacoronavirus , COVID-19 , Teste para COVID-19 , Vacinas contra COVID-19 , Técnicas de Laboratório Clínico , Infecções por Coronavirus/diagnóstico , Infecções por Coronavirus/epidemiologia , Feminino , Humanos , Aprendizado de Máquina , Masculino , Pessoa de Meia-Idade , Países Baixos/epidemiologia , Pandemias , Pneumonia Viral/epidemiologia , Estudos Prospectivos , SARS-CoV-2 , Sensibilidade e Especificidade
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