Evaluation of the performance of SARS--CoV--2 antibody assays for a longitudinal population-based study of COVID--19 spread in St. Petersburg, Russia.

ABSTRACT

Geographical variation in severe acute respiratory syndrome coronavirus 2 (SARS--CoV--2) spread requires seroprevalence studies based on local tests, but robust validation is needed. We summarize an evaluation of antibody tests used in a serological study of SARS--CoV--2 in Saint Petersburg, Russia. We validated three different antibody assays chemiluminescent microparticle immunoassay (CMIA) Abbott Architect SARS--CoV--2 immunoglobulin G (IgG), enzyme- linked immunosorbent assay (ELISA) CoronaPass total antibodies test, and ELISA SARS--CoV--2--IgG--EIA--BEST. Clinical sensitivity was estimated with the SARS--CoV--2 polymerase chain reaction (PCR) test as the gold standard using manufacturer recommended cutoff. Specificity was estimated using pre-pandemic sera samples. The median time between positive PCR test results and antibody tests was 21 weeks. Measures of concordance were calculated against the microneutralization test (MNA).Sensitivity was equal to 91.1% (95% confidence intervbal [CI] 78.8-97.5), 90% (95% CI 76.4-96.4), and 63.1% (95% CI [50.2-74.7]) for ELISA Coronapass, ELISA Vector-Best, and CMIA Abbott, respectively. Specificity was equal to 100% for all the tests. Comparison of receiver operating characteristics has shown lower AUC for CMIA Abbott. The cut-off SC/O ratio of 0.28 for CMIA Abbott resulted in a sensitivity of 80% at the same level of specificity. Less than 33% of the participants with positive antibody test results had neutralizing antibodies in titers 180 and above. Antibody assays results and MNA correlated moderately. This study encourages the use of local antibody tests and sets the reference for seroprevalence correction. Available tests' sensitivity allows detecting antibodies within the majority of PCR- positive individuals. The Abbott assay sensitivity can be improved by incorporating a new cut-off. Manufacturers' test characteristics may introduce bias into the study results.