Introduction

Serological tests are a powerful tool in the monitoring of infectious diseases and the detection of host immunity. To help fight the recent coronavirus disease-2019 (COVID-19) pandemic representing our time’s sincerest health and socioeconomic crisis, various serological assays have been brought to market in record time. [1-5]. Many of these tests were developed with the ultimate goal to monitor the infection burden within a community, assess vaccination responses, and determine the likelihood of protection against re-infection [5, 6].Broad implementation of serological COVID-19 tests has also been envisioned to assess the effectiveness of control strategies and facilitate decision-making on the reopening of schools, cultural facilities, and businesses [7-10]. Further, such tests might form a basis for the issue of immunity passports, the authorization of international traveling, and the return of employees to work [9, 11]. Numerous serological studies have recently been conducted [12-15], and governments worldwide have ordered millions of serological tests to identify individuals with antibodies against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) [16] without prior in-depth clinical validation of the assays.
To enable a meaningful application and interpretation of serological test results, such assays must (a) accurately identify patients with previous COVID-19 and (b) correctly predict protective immunity acquired by previous infection or vaccination [4, 6, 10]. Tests with inadequate performance characteristics will result in misinterpretation of data and might lead to questionable or even counter-productive health policy decision [5, 16]. Problematically, manufacturers of serological assays often provide diagnostic accuracy data generated through biased studies and claiming to have a sensitivity and specificity close to 100% [2, 5, 12] [17] [2, 10, 13, 18, 19]. Thus, estimates of diagnostic accuracy are regarded as unreliable [2, 10, 20]. Many organizations, including the WHO, now call for the development of reliable antibody tests and evaluation in appropriate diagnostic accuracy studies [5, 9].
Here, we conducted a prospective cross-sectional study in a real-life clinical setting, stringently fulfilling the requirements of a diagnostic accuracy study, including (1) an adequately powered prospective design studying clearly defined clinical questions, (2) selection of a representative study population, (3) head-to-head comparison of all significant serological testing strategies, (4) rigorous choice and determination of reference standard, and (e) optimal flow and timing. Specifically, we assessed whether different serological testing strategies may (a) accurately identify patients with previous COVID-19 and (b) correctly predict neutralizing antibodies against SARS-CoV-2.