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Increasing both specificity and sensitivity of SARS-CoV-2 antibody tests by using an adaptive orthogonal testing approach (preprint)
medrxiv; 2020.
Preprint
in English
| medRxiv | ID: ppzbmed-10.1101.2020.11.05.20226449
ABSTRACT
Background SARS-CoV-2 antibody tests have undergone a remarkable improvement in performance. However, due to the low seroprevalence in several areas, very high demands are made on their specificity. Furthermore, the low antibody-response in some individuals requires high test sensitivity to avoid underestimating true seroprevalence. Optimization of testing has been reported through lowering manufacturer cut-offs to improve SARS-CoV-2 assay sensitivity or by combining two tests to improve specificity at the cost of sensitivity. However, these strategies have thus far been used in isolation of each other. Methods To increase sensitivity, cut-offs of three commercially available SARS-CoV-2 automated assays (Roche, Abbott, and DiaSorin) were reduced according to published values in a pre-pandemic specificity cohort (n=1117) and a SARS-CoV-2 positive cohort (n=64). All three testing systems were combined in an orthogonal approach with a confirmatory test, which was one of the remaining automated assays or one of two commercial ELISAs directed against the spike protein receptor binding-domain (RBD) or the nucleocapsid antigen (NP). Results The modified orthogonal test strategy resulted in an improved specificity of at least 99.8%, often even 100%, in all 12 tested combinations with no significant decline in sensitivity. In our cohort, regardless of whether the assays were used for screening or confirmation, combining Roche and Abbott delivered the best overall performance (+~10% sensitivity compared to the single tests and 100% specificity). Conclusion Here we propose a novel orthogonal assay strategy that approaches 100% specificity while maintaining or even significantly improving the screening test's sensitivity.
Full text:
Available
Collection:
Preprints
Database:
medRxiv
Language:
English
Year:
2020
Document Type:
Preprint
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