A Comparison of Five SARS-CoV-2 Molecular Assays With Clinical Correlations
- PMID: 33015712
- PMCID: PMC7665304
- DOI: 10.1093/ajcp/aqaa181
A Comparison of Five SARS-CoV-2 Molecular Assays With Clinical Correlations
Abstract
Objectives: Comparative assessments of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) molecular assays that have been operationalized through the US Food and Drug Administration's Emergency Use Authorization process are warranted to assess real-world performance. Characteristics such as sensitivity, specificity, and false-negative rate are important to inform clinical use.
Methods: We compared five SARS-CoV-2 assays using nasopharyngeal and nasal swab specimens submitted in transport media; we enriched this cohort for positive specimens, since we were particularly interested in the sensitivity and false-negative rate. Performance of each test was compared with a composite standard.
Results: The sensitivities and false-negative rates of the 239 specimens that met inclusion criteria were, respectively, as follows: Centers for Disease Control and Prevention 2019 nCoV Real-Time RT-PCR Diagnostic Panel, 100% and 0%; TIB MOLBIOL/Roche z 480 Assay, 96.5% and 3.5%; Xpert Xpress SARS-CoV-2 (Cepheid), 97.6% and 2.4%; Simplexa COVID-19 Direct Kit (DiaSorin), 88.1% and 11.9%; and ID Now COVID-19 (Abbott), 83.3% and 16.7%.
Conclusions: The assays that included a nucleic acid extraction followed by reverse transcription polymerase chain reaction were more sensitive than assays that lacked a full extraction. Most false negatives were seen in patients with low viral loads, as extrapolated from crossing threshold values.
Keywords: COVID-19; Coronavirus; Nucleic acid amplification tests; SARS-CoV-2.
© American Society for Clinical Pathology, 2020. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.
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References
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- US Food and Drug Administration. Emergency Use Authorization 2020. https://www.fda.gov/medical-devices/coronavirus-disease-2019-covid-19-em.... Accessed June 19, 2020.
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- R Core Team. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing; 2020. https://www.rproject.org/.
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- Kowarik A, Templ M. Imputation with the R Package VIM. J Stat Softw. 2016;74:1-16.
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