Nasrallah, Gheyath K; Dargham, Soha R; Shurrab, Farah; Al-Sadeq, Duaa W; Al-Jighefee, Hadeel; Chemaitelly, Hiam; Al Kanaani, Zaina; Al Khal, Abdullatif; Al Kuwari, Einas; Coyle, Peter; +14 more... Jeremijenko, Andrew; Kaleeckal, Anvar Hassan; Latif, Ali Nizar; Shaik, Riyazuddin Mohammad; Rahim, Hanan F Abdul; Yassine, Hadi M; Al Kuwari, Mohamed G; Qotba, Hamda; Al Romaihi, Hamad Eid; Tang, Patrick; Bertollini, Roberto; Al-Thani, Mohamed H; Althani, Asmaa A; Abu-Raddad, Laith J; (2021) Analytic comparison between three high-throughput commercial SARS-CoV-2 antibody assays reveals minor discrepancies in a high-incidence population. Scientific reports, 11 (1). 11837-. ISSN 2045-2322 DOI: https://doi.org/10.1038/s41598-021-91235-x
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Abstract
Performance of three automated commercial serological IgG-based assays was investigated for assessing SARS-CoV-2 "ever" (past or current) infection in a population-based sample in a high exposure setting. PCR and serological testing was performed on 394 individuals. SARS-CoV-2-IgG seroprevalence was 42.9% (95% CI 38.1-47.8%), 40.6% (95% CI 35.9-45.5%), and 42.4% (95% CI 37.6-47.3%) using the CL-900i, VidasIII, and Elecsys assays, respectively. Between the three assays, overall, positive, and negative percent agreements ranged between 93.2-95.7%, 89.3-92.8%, and 93.8-97.8%, respectively; Cohen's kappa statistic ranged from 0.86 to 0.91; and 35 specimens (8.9%) showed discordant results. Among all individuals, 12.5% (95% CI 9.6-16.1%) had current infection, as assessed by PCR. Of these, only 34.7% (95% CI 22.9-48.7%) were seropositive by at least one assay. A total of 216 individuals (54.8%; 95% CI 49.9-59.7%) had evidence of ever infection using antibody testing and/or PCR during or prior to this study. Of these, only 78.2%, 74.1%, and 77.3% were seropositive in the CL-900i, VidasIII, and Elecsys assays, respectively. All three assays had comparable performance and excellent agreement, but missed at least 20% of individuals with past or current infection. Commercial antibody assays can substantially underestimate ever infection, more so when infection rates are high.
Item Type | Article |
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Faculty and Department | Faculty of Epidemiology and Population Health > Dept of Infectious Disease Epidemiology |
Research Centre | Covid-19 Research |
PubMed ID | 34088944 |
Elements ID | 162316 |