ABSTRACT
BackgroundReliable quantification of the antibody response to SARS-CoV-2 vaccination is highly relevant for identifying possible vaccine failure and estimating the time of protection. Therefore, we aimed to evaluate the performance of five different Anti-SARS-CoV-2 antibody assays regarding the quantification of anti-spike (S) antibodies induced after a single dose of BNT162b2. MethodsSera of n=69 SARS-CoV-2 naive individuals 21{+/-}1 days after vaccination with BNT162b2 (Pfizer/BioNTech) were tested using the following quantitative SARS-CoV-2 antibody assays: Roche S total antibody, DiaSorin trimeric spike IgG, DiaSorin S1/S2 IgG, Abbott II IgG, and Serion/Virion IgG. Test agreement was assessed by Passing-Bablok regression. Results were further compared to the percent inhibition calculated from a surrogate virus neutralization test (sVNT) by correlation and ROC (receiver-operating-characteristics) analysis. ResultsIndividual values were distributed over several orders of magnitude for all assays evaluated. Although the assays were in good overall agreement ({rho}=0.80-0.94), Passing-Bablok regression revealed systematic and proportional differences, which could not be eliminated by converting the results to BAU/mL as suggested by the manufacturers. 7 (10%) individuals had a negative sVNT results (i.e. <30% inhibition). These samples were reliably identified by most assays and yielded low binding antibody levels (ROC-AUCs 0.84-0.93). ConclusionsAlthough all assays evaluated showed good correlation, readings from different assays were not interchangeable, even when converted to BAU/mL using the WHO international standard for SARS-CoV-2 immunoglobulin. This highlights the need for further standardization of SARS-CoV-2 serology.
ABSTRACT
BackgroundSerological tests are widely used in various medical disciplines for diagnostic and monitoring purposes. Unfortunately, the sensitivity and specificity of test systems is often poor, leaving room for false positive and false negative results. However, conventional methods used to increase specificity decrease sensitivity and vice versa. Using SARS-CoV-2 serology as an example, we propose here a novel testing strategy: the "Sensitivity Improved Two-Test" or " SIT2" algorithm. MethodsSIT2 involves confirmatory re-testing of samples with results falling in a predefined retesting-zone of an initial screening test, with adjusted cut-offs to increase sensitivity. We verified and compared the performance of SIT2 to single tests and orthogonal testing (OTA) in an Austrian cohort (1,117 negative, 64 post-COVID positive samples) and validated the algorithm in an independent British cohort (976 negatives, 536 positives). ResultsThe specificity of SIT2 was superior to single tests and non-inferior to OTA. The sensitivity was maintained or even improved using SIT2 when compared to single tests or OTA. SIT2 allowed correct identification of infected individuals even when a live virus neutralization assay could not detect antibodies. Compared to single testing or OTA, SIT2 significantly reduced total test errors to 0.46% (0.24-0.65) or 1.60% (0.94-2.38) at both 5% or 20% seroprevalence. ConclusionFor SARS-CoV-2 serology, SIT2 proved to be the best diagnostic choice at both 5% and 20% seroprevalence in all tested scenarios. It is an easy to apply algorithm and can potentially be helpful for the serology of other infectious diseases.
ABSTRACT
Background: In the context of the COVID-19 pandemic, numerous new serological test systems for the detection of anti-SARS-CoV-2 antibodies have become available quickly. However, the clinical performance of many of them is still insufficiently described. Therefore we compared three commercial, CE-marked, SARS-CoV-2 antibody assays side by side. Methods: We included a total of 1,154 specimens from pre-COVID-19 times and 65 samples from COVID-19 patients ([≥]14 days after symptom onset) to evaluate the test performance of SARS-CoV-2 serological assays by Abbott, Roche, and DiaSorin. Results: All three assays presented with high specificities: 99.2% (98.6-99.7) for Abbott, 99.7% (99.2-100.0) for Roche, and 98.3% (97.3-98.9) for DiaSorin. In contrast to the manufacturers specifications, sensitivities only ranged from 83.1% to 89.2%. Although the three methods were in good agreement (Cohens Kappa 0.71-0.87), McNemars test revealed significant differences between results obtained from Roche and DiaSorin. However, at low seroprevalences, the minor differences in specificity resulted in profound discrepancies of positive predictability at 1% seroprevalence: 52.3% (36.2-67.9), 77.6% (52.8-91.5), and 32.6% (23.6-43.1) for Abbott, Roche, and DiaSorin, respectively. Conclusion: We find diagnostically relevant differences in specificities for the anti-SARS-CoV-2 antibody assays by Abbott, Roche, and DiaSorin that have a significant impact on the positive predictability of these tests.
