RESUMO
To compare the RT-PCR Allplex SARS-CoV-2/FluA/FluB/RSV Assay (Allplex assay) with other methods of detection of VOC B.1.1.7. Suspected and non-suspected cases of VOC B.1.1.7 were defined according to the VirSNiP assay, which detects N501Y and deletion H69-V70. For pre-screening, the Allplex™ and TaqPath assays were used. One hundred and sixteen suspected and 113 non-suspected cases were included. In the suspected cases, the Allplex assay showed N-gene dropout, or delayed Ct values of 6.27 ± 1.21 and 6.66 ± 1.41 compared with those of the RdRP and S-gene target, respectively. Agreement between the Allplex and TaqPath assays was 100% when the RdRP and S-gene targets had Ct values <35. Agreement between the Allplex and VirSNiP assays was 100% with Ct value <30. The Allplex assay showed excellent agreement with the current pre-screening method for VOC B.1.1.7. In addition, its automated processing enhances the feasibility of widespread use in laboratories.
Assuntos
COVID-19/virologia , Proteínas do Nucleocapsídeo de Coronavírus/genética , SARS-CoV-2/genética , SARS-CoV-2/isolamento & purificação , COVID-19/diagnóstico , Estudos de Viabilidade , Regulação Viral da Expressão Gênica , Genoma Viral , Humanos , Fosfoproteínas/genética , Sensibilidade e Especificidade , Sequenciamento Completo do GenomaRESUMO
The COVID-19 outbreak has spread extensively around the world. Loss of smell and taste have emerged as main predictors for COVID-19. The objective of our study is to develop a comprehensive machine learning (ML) modelling framework to assess the predictive value of smell and taste disorders, along with other symptoms, in COVID-19 infection. A multicenter case-control study was performed, in which suspected cases for COVID-19, who were tested by real-time reverse-transcription polymerase chain reaction (RT-PCR), informed about the presence and severity of their symptoms using visual analog scales (VAS). ML algorithms were applied to the collected data to predict a COVID-19 diagnosis using a 50-fold cross-validation scheme by randomly splitting the patients in training (75%) and testing datasets (25%). A total of 777 patients were included. Loss of smell and taste were found to be the symptoms with higher odds ratios of 6.21 and 2.42 for COVID-19 positivity. The ML algorithms applied reached an average accuracy of 80%, a sensitivity of 82%, and a specificity of 78% when using VAS to predict a COVID-19 diagnosis. This study concludes that smell and taste disorders are accurate predictors, with ML algorithms constituting helpful tools for COVID-19 diagnostic prediction.