Comparison of one single-antigen assay and three multi-antigen SARS-CoV-2 IgG assays in Nigeria.

Objectives: Determining an accurate estimate of SARS-CoV-2 seroprevalence has been challenging in African countries where malaria and other pathogens are endemic. We compared the performance of one single-antigen assay and three multi-antigen SARS-CoV-2 IgG assays in a Nigerian population endemic for malaria. Methods: De-identified plasma specimens from SARS-CoV-2 RT-PCR positive, dried blood spot (DBS) SARS-CoV-2 RT-PCR positive, and pre-pandemic negatives were used to evaluate the performance of the four SARS-CoV-2 assays (Tetracore, SARS2MBA, RightSign, xMAP). Results: Results showed higher sensitivity with the multi-antigen (81% (Tetracore), 96% (SARS2MBA), 85% (xMAP)) versus the single-antigen (RightSign (64%)) SARS-CoV-2 assay. The overall specificities were 98% (Tetracore), 100% (SARS2MBA and RightSign), and 99% (xMAP). When stratified based on <15 days to ≥15 days post-RT-PCR confirmation, the sensitivities increased from 75% to 88.2% for Tetracore; from 93% to 100% for the SARS2MBA; from 58% to 73% for RightSign; and from 83% to 88% for xMAP. With DBS, there was no positive increase after 15-28 days for the three assays (Tetracore, SARS2MBA, and xMAP). Conclusion: Multi-antigen assays performed well in Nigeria, even with samples with known malaria reactivity, and might provide more accurate measures of COVID-19 seroprevalence and vaccine efficacy.

Validation of xMAP SARS-CoV-2 Multi-Antigen IgG assay in Nigeria.

OBJECTIVE: There is a need for reliable serological assays to determine accurate estimates of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) seroprevalence. Most single target antigen assays have shown some limitations in Africa. To assess the performance of a multi-antigen assay, we evaluated a commercially available SARS-CoV-2 Multi-Antigen IgG assay for human coronavirus disease 2019 (COVID-19) in Nigeria. METHODS: Validation of the xMAP SARS-CoV-2 Multi-Antigen IgG assay was carried out using well-characterized SARS-CoV-2 reverse transcription polymerase chain reactive positive (97) and pre-COVID-19 pandemic (86) plasma panels. Cross-reactivity was assessed using pre-COVID-19 pandemic plasma specimens (213) from the 2018 Nigeria HIV/AIDS Indicator and Impact Survey (NAIIS). RESULTS: The overall sensitivity of the xMAP SARS-CoV-2 Multi-Antigen IgG assay was 75.3% [95% CI: 65.8%- 82.8%] and specificity was 99.0% [95% CI: 96.8%- 99.7%]. The sensitivity estimate increased to 83.3% [95% CI: 70.4%- 91.3%] for specimens >14 days post-confirmation of diagnosis. However, using the NAIIS pre-pandemic specimens, the false positivity rate was 1.4% (3/213). CONCLUSIONS: Our results showed overall lower sensitivity and a comparable specificity with the manufacturer's validation. There appears to be less cross-reactivity with NAIIS pre-pandemic COVID-19 specimens using the xMAP SARS-CoV-2 Multi-Antigen IgG assay. In-country SARS-CoV-2 serology assay validation can help guide the best choice of assays in Africa.

Considerations for quality assurance of multiplex malaria antigen detection assays with large sample sets.

Multiplex assays for malaria antigen detection can gather data from large sample sets, but considerations for the consistency and quality assurance (QA) of mass testing lack evaluation. We present a QA framework for a study occurring November 2019 to March 2020 involving 504 assay plates detecting four Plasmodium antigens: pan-Plasmodium aldolase and lactate dehydrogenase (LDH), histidine-rich protein 2 (HRP2), P. vivax LDH (PvLDH). Controls on each plate included buffer blank, antigen negative blood, and 4-point positive dilution curve. The blank and negative blood provided consistently low signal for all targets except for pAldolase, which showed variability. Positive curve signals decreased throughout the 5-month study duration but retained a coefficient of variation (CV) of < 5%, with the exception of HRP2 in month 5 (CV of 11%). Regression fittings for inter-plate control signals provided mean and standard deviations (SDs), and of 504 assay plates, 6 (1.2%) violated the acceptable deviation limits and were repeated. For the 40,272 human blood samples assayed in this study, of 161,088 potential data points (each sample × 4 antigens), 160,641 (99.7%) successfully passed quality checks. The QA framework presented here can be utilized to ensure quality of laboratory antigen detection for large sample sets.