Background: Despite increasing COVID-19 infection rates, low overall prevalence resulting in a poor positive predictive value (PPV) of serological tests requires strategies to increase specificity. We therefore investigated a dual diagnostic strategy and evaluated the correlation between the severity of a SARS-CoV-2 infection and the detectable immune-response.
Methods: Participants were systematically categorized into positive and control cohorts and a probability score of COVID-19 was calculated based on clinical symptoms. Six hundred eighty-two serum samples were analyzed using a highly specific high-throughput system. Combining the serological test result and probability score was performed as a dual diagnostic strategy.
Results: Specificity of 99.61% and sensitivity of 86.0% were the basis of our approach. A dual diagnostic strategy led to increased pre-test probability and thus to a test specificity of 100%. In a flu-like symptomatic population, we estimated a COVID-prevalence of 4.79%. Moreover, we detected significantly higher antibody values in patients with fever than without fever.
Conclusions: Based on sensitivity and specificity results of our study being in line with previous findings, we demonstrated a dual assessment strategy including a symptom-based probability score and serological testing to increase the PPV. Moreover, the presence of fever seems to trigger a stronger immune-response.