Clinical usefulness of fully automated chemiluminescent immunoassay for quantitative antibody measurements in COVID-19 patients

Abstract

Since December 2019, we have been in the battlefield with a new threat to the humanity, known as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes coronavirus disease 2019 (COVID-19), characterized by viral pneumonia. It may be asymptomatic or cause various symptoms, ranging from flu-like symptoms to acute respiratory distress syndrome and eventually death. At present, the only reliable test for COVID-19 diagnosis is quantitative reverse transcriptase-polymerase chain reaction. Assessing the immune response against SARS-CoV-2 could increase the detection sensitivity of infected population. Hereby, we report the performances of a fully automated chemiluminescent immunoassay (CLIA) on 276 serum samples. One hundred samples obtained from COVID-19 negative subjects (COVID-19 free) were analyzed to evaluate the diagnostic specificity of antibody (Ab) detection. Thereafter, 176 samples obtained from 125 patients with confirmed COVID-19 (COVID-19 patients) were selected to assess the diagnostic sensitivity of the CLIA. All samples were analyzed on MAGLUMI 800 platform. All COVID-19 free samples had Ab levels below the cutoff values. Hence, the diagnostic specificity was estimated at 100% (95% confidence interval [CI] = 96.3-100.0; positive predictive value = 100%). By the 18th day from the onset of symptoms, we reached an optimal diagnostic sensitivity (more than 95.0%) In fact, the diagnostic sensitivity increased over time and between 15 and 25 days after symptoms onset, reached 95.5% (95% CI = 84.9-99.2). The new automated CLIA analyzer appeared to be a robust and reliable method to measure specific Ab against COVID-19 at high throughput. Our data suggest that combining Ab and nucleic acid detection could increase diagnostic sensitivity.

Keywords: COVID-19; Euroimmun; MAGLUMI; SARS-CoV-2; immunoassay; serology.

Figure 1

A, Receiver operating characteristic (ROC) curve for IgG levels obtained from 176 samples at different stages of infection. Area under the ROC curve (AUC) was estimated = 0.846 (CI = 0.801‐0.891), P < .0001. The latter AUC suggests excellent discriminatory power of the test between the COVID‐19 patients and the COVID‐19 free groups. B, ROC curve for IgM levels obtained from 176 samples at different stages of infection. AUC was estimated = 0.831 (CI = 0.782‐0.880), P < .0001. The latter AUC suggests excellent discriminatory power of the test between the COVID‐19 patients and the COVID‐19 free groups. C, ROC curve for IgG levels obtained from 44 samples between 15 and 25 days after symptoms onset. AUC was estimated = 0.963 (CI = 0.916‐1.000), P < .0001. The latter AUC suggests outstanding discriminatory power of the test between the COVID‐19 patients and the COVID‐19 free groups. D, ROC curve for IgM levels obtained from 44 samples between 15 and 25 days after symptoms onset. AUC was estimated = 0.970 (CI = 0.931‐1.000), P < .0001. The latter AUC suggests outstanding discriminatory power of the test between the COVID‐19 patients and the COVID‐19 free groups. CI, confidence interval; COVID‐19, coronavirus disease 2019; IgG, immunoglobulin G