Evaluation of Serological Tests for SARS-CoV-2: Implications for Serology Testing in a Low-Prevalence Setting. (6th August 2020) - Bond, Katherine; Nicholson, Suellen; Lim, Seok Ming; Karapanagiotidis, Theo; Williams, Eloise; Johnson, Douglas; Hoang, Tuyet; Sia, Cheryll; Purcell, Damian; Mordant, Francesca; Lewin, Sharon R; Catton, Mike; et al.
Abstract: Background: Robust serological assays are essential for long-term control of the COVID-19 pandemic. Many recently released point-of-care (PoCT) serological assays have been distributed with little premarket validation. Methods: Performance characteristics for 5 PoCT lateral flow devices approved for use in Australia were compared to a commercial enzyme immunoassay (ELISA) and a recently described novel surrogate virus neutralization test (sVNT). Results: Sensitivities for PoCT ranged from 51.8% (95% confidence interval [CI], 43.1%–60.4%) to 67.9% (95% CI, 59.4%–75.6%), and specificities from 95.6% (95% CI, 89.2%–98.8%) to 100.0% (95% CI, 96.1%–100.0%). ELISA sensitivity for IgA or IgG detection was 67.9% (95% CI, 59.4%–75.6%), increasing to 93.8% (95% CI, 85.0%–98.3%) for samples >14 days post symptom onset. sVNT sensitivity was 60.9% (95% CI, 53.2%–68.4%), rising to 91.2% (95% CI, 81.8%–96.7%) for samples >14 days post symptom onset, with specificity 94.4% (95% CI, 89.2%–97.5%). Conclusions: Performance characteristics for COVID-19 serological assays were generally lower than those reported by manufacturers. Timing of specimen collection relative to onset of illness or infection is crucial in reporting of performance characteristics for COVID-19 serological assays. The optimal algorithm for implementing serological testing for COVID-19 remains to be determined, particularly in low-prevalence settings. Abstract : Performance characteristics for COVID-19 serological assays were generally lower than those reported by manufacturers. The optimal algorithm for implementing serological testing for COVID-19 remains to be determined, particularly in low-prevalence settings where positive predictive values are low.