SARS-CoV-2 Diagnostic Tests: Algorithm and Field Evaluation From the Near Patient Testing to the Automated Diagnostic...

Introduction: Since the first wave of COVID-19 in Europe, new diagnostic tools using antigen detection and rapid molecular techniques have been developed. Our objective was to elaborate a diagnostic algorithm combining antigen rapid diagnostic tests, automated antigen dosing and rapid molecular tests and to assess its performance under routine conditions. Methods: An analytical performance evaluation of four antigen rapid tests, one automated antigen dosing and one molecular point-of-care test was performed on samples sent to our laboratory for a SARS-CoV-2 reverse transcription PCR. We then established a diagnostic algorithm by approaching median viral loads in target populations and evaluated the limit of detection of each test using the PCR cycle threshold values. A field performance evaluation including a clinical validation and a user-friendliness assessment was then conducted on the antigen rapid tests in point-of-care settings (general practitioners and emergency rooms) for outpatients who were symptomatic for <7 days. Automated antigen dosing was trialed for the screening of asymptomatic inpatients. Results: Our diagnostic algorithm proposed to test recently symptomatic patients using rapid antigen tests, asymptomatic patients using automated tests, and patients requiring immediate admission using molecular point-of-care tests. Accordingly, the conventional reverse transcription PCR was kept as a second line tool. In this setting, antigen rapid tests yielded an overall sensitivity of 83.3% (not significantly different between the four assays) while the use of automated antigen dosing would have spared 93.5% of asymptomatic inpatient screening PCRs. Conclusion: Using tests not considered the "gold standard" for COVID-19 diagnosis on well-defined target populations allowed for the optimization of their intrinsic performances, widening the scale of our testing arsenal while sparing molecular resources for more seriously ill patients.