RESUMEN
Antibody tests are essential tools to investigate humoral immunity following SARS-CoV-2 infection. While first-generation antibody tests have primarily provided qualitative results with low specificity, accurate seroprevalence studies and tracking of antibody levels over time require highly specific, sensitive and quantitative test setups. Here, we describe two quantitative ELISA antibody tests based on the SARS-CoV-2 spike receptor-binding domain and the nucleocapsid protein. Comparative expression in bacterial, insect, mammalian and plant-based platforms enabled the identification of new antigen designs with superior quality and high suitability as diagnostic reagents. Both tests scored excellently in clinical validations with multi-centric specificity and sensitivity cohorts and showed unprecedented correlation with SARS-CoV-2 neutralization titers. Orthogonal testing increased assay specificity to 99.8%, thereby enabling robust serodiagnosis in low-prevalence settings. The inclusion of a calibrator permits accurate quantitative monitoring of antibody concentrations in samples collected at different time points during the acute and convalescent phase of COVID-19.
RESUMEN
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.
RESUMEN
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.