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Analytical and Clinical Comparison of Three Nucleic Acid Amplification Tests for SARS-CoV-2 Detection (preprint)
biorxiv; 2020.
Preprint in English | bioRxiv | ID: ppzbmed-10.1101.2020.05.14.097311
ABSTRACT
Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) was first identified in December 2019 and has quickly become a worldwide pandemic. In response, many diagnostic manufacturers have developed molecular assays for SARS-CoV-2 under the Food and Drug Administration (FDA) Emergency Use Authorization (EUA) pathway. This study compared three of these assays the Hologic Panther Fusion SARS-CoV-2 assay (Fusion), the Hologic Aptima SARS-CoV-2 assay (Aptima) and the BioFire Diagnostics COVID-19 test (BioFire), to determine analytical and clinical performance, as well as workflow. All three assays showed a similar limit of detection (LOD) using inactivated virus, with 100% detection ranging from 500-1,000 genome equivalents/ml, whereas use of a quantified RNA transcript standard showed the same trend, but had values ranging from 62.5 to 125 copies/ml, confirming variability in absolute quantification of reference standards. The clinical correlation found that the Fusion and BioFire assays had a positive percent agreement (PPA) of 98.7%, followed by the Aptima assay at 94.7% when compared to the consensus result. All three assays exhibited 100% negative percent agreement (NPA). Analysis of discordant results revealed that all four samples missed by the Aptima assay had Ct values >37 on the Fusion assay. In conclusion, while all three assays showed similar relative LODs, we showed differences in absolute LODs depending on which standard was employed. In addition, the Fusion and BioFire assays showed better clinical performance, while the Aptima assay showed a modest decrease in overall PPA. These findings should be kept in mind when making platform testing decisions.

Full text: Available Collection: Preprints Database: bioRxiv Language: English Year: 2020 Document Type: Preprint

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Full text: Available Collection: Preprints Database: bioRxiv Language: English Year: 2020 Document Type: Preprint