Increasing test specificity without impairing sensitivity: lessons learned from SARS-CoV-2 serology.
Perkmann T., Koller T., Perkmann-Nagele N., Ozsvar-Kozma M., Eyre D., Matthews P., Bown A., Stoesser N., Breyer M-K., Breyer-Kohansal R., Burghuber OC., Hartl S., Aletaha D., Sieghart D., Quehenberger P., Marculescu R., Mucher P., Radakovics A., Klausberger M., Duerkop M., Holzer B., Hartmann B., Strassl R., Leitner G., Grebien F., Gerner W., Grabherr R., Wagner OF., Binder CJ., Haslacher H.
BackgroundSerological tests are widely used in various medical disciplines for diagnostic and monitoring purposes. Unfortunately, the sensitivity and specificity of test systems are often poor, leaving room for false-positive and false-negative results. However, conventional methods were used to increase specificity and 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 retesting 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 (1117 negative, 64 post-COVID-positive samples) and validated the algorithm in an independent British cohort (976 negatives and 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 with single tests or OTA. SIT² allowed correct identification of infected individuals even when a live virus neutralisation assay could not detect antibodies. Compared with 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.