The role of antibody tests for COVID-19 in primary care

CoVimmune COVID-19 Immunity Calculator: Web Application Development and Validation Study

Background

This study illustrates the development of a simple web-based application, which demonstrates the relationship between serum anti-SARS-CoV-2 S1/receptor-binding domain immunoglobulin G (IgG) and anti-SARS-CoV-2 neutralizing antibody (nAb) half-maximal inhibitory concentration (IC50) titers in a vaccinated US adult population and compares them to prior data on nAb titers at different time points after vaccination.

Objective

The objective of this study is to create an easily accessible calculator that uses the results of commercially available anti-SARS-CoV-2 serum IgG to approximate the underlying ability to neutralize SARS-CoV-2.

Methods

Our web-based application leveraged two previously published datasets. One dataset demonstrated a robust correlation between nAb and serum IgG. The other dataset measured nAb titers at specific time periods over a year-long interval following a messenger RNA vaccination primary series and booster vaccine dose. Clinical factors that were statistically significant on a forward linear regression model examining the prediction of nAb from serum IgG were incorporated in the application tool.

Results

By combining the datasets described above, we developed a publicly available web-based application that allows users to enter a serum IgG value and determine their estimated nAb titer. The application contextualizes the estimated nAb titer with the theoretical distance from the corresponding vaccine-mediated antibody protection. Using the clinical variables that had a significant impact on how well IgG values predict nAb titers, this application allows for a patient-centered, nAb titer prediction.

Conclusions

This application offers an example of how we might bring the advances made in scientific research on protective antibodies post-SARS-CoV-2 vaccination into the clinical sphere with practical tools.

Correlation of a commercial platform's results with post-vaccination SARS-CoV-2 neutralizing antibody response and clinical host factors

INTRODUCTION

The objective of this study was to describe the correlation between the commercially available assay for anti-S1/RBD IgG and protective serum neutralizing antibodies (nAb) against SARS-CoV-2 in an adult population after SARS-CoV-2 vaccination, and determine if clinical variables impact this correlation.

METHODS

We measured IgG anti-S1/RBD using the IgG-II CMIA assay and nAb IC50 values against SARS-CoV-2 WA-1 in sera serially collected post-mRNA vaccination in veterans and healthcare workers of the Veterans Affairs Connecticut Healthcare System (VACHS) between December 2020 and January 2022. The correlation between IgG and IC50 was measured using Pearson correlation. Clinical variables (age, sex, race, ethnicity, prior COVID infection defined by RT-PCR, history of malignancy, estimated glomerular filtration rate (GFR calculated using CKD-EPI equation) were collected by manual chart review. The impact of these clinical variables on the IgG-nAb correlation was analyzed first with univariable regression. Variables with a significance of p < 0.15 were analyzed with forward stepwise regression analysis.

RESULTS

From 127 sera samples in 100 unique subjects (age 20-93 years; mean 63.83; SD 15.63; 29% female; 67% White), we found a robust correlation between IgG anti-S1/RBD and nAb IC50 (R2 = 0.83, R2adj = 0.70, p < 0.0001). Race, ethnicity, and a history of malignancy were not significant on univariable analysis. GFR (p < 0.05) and prior COVID infection (p < 0.001) had a significant impact on the correlation between IgG anti-S1/RBD and nAb IC50. Age (p = 0.06) and sex (p = 0.07) trended towards significance on univariable analysis, but were not significant on multivariable regression.

CONCLUSIONS

There was a strong correlation between IgG anti-S1/RBD and nAb IC50 after SARS-CoV-2 vaccination. Clinical comorbidities, such as prior COVID infection and renal function, impacted this correlation. These results may assist the prediction of post-vaccination immune protection in clinical settings using cost-effective commercial platforms.

Diagnostic accuracy and feasibility of a rapid SARS-CoV-2 antigen test in general practice – a prospective multicenter validation and implementation study

Background

PCR testing is considered the gold standard for SARS-CoV-2 diagnosis but its results are earliest available hours to days after testing. Rapid antigen tests represent a diagnostic tool enabling testing at the point of care. Rapid antigen tests have mostly been validated by the manufacturer or in controlled laboratory settings only. External validation at the point of care, particularly in general practice where the test is frequently used, is needed. Furthermore, it is unclear how well point of care tests are accepted by the practice staff.

Methods

In this prospective multicenter validation study in primary care, general practitioners included adult individuals presenting with symptoms suggesting COVID-19. Each patient was tested by the general practitioner, first with a nasopharyngeal swab for the point of care test (Roche SARS-CoV-2 Rapid Antigen Test) and then with a second swab for PCR testing. Using the RT-PCR result as a reference, we calculated specificity, sensitivity, positive predictive value and negative predictive value, with their 95% confidence intervals. General practitioners and medical assistants completed a survey to assess feasibility and usefulness of the point of care tests.

Results

In 40 practices in Würzburg, Germany, 1518 patients were recruited between 12/2020 and 06/2021. The point of care test achieved a sensitivity of 78.3% and a specificity of 99.5% compared to RT-PCR. With a prevalence of 9.5%, the positive predictive value was 93.9% and the negative predictive value was 97.8%. General practitioners rated the point of care test as a helpful tool to support diagnostics in patients with signs and symptoms suggestive for infection, particularly in situations where decision on further care is needed at short notice.

Conclusion

The point of care test used in this study showed a sensitivity below the manufacturer’s specification (Sensitivity 96.25%) in the practice but high values for specificity and high positive predictive value and negative predictive value. Although widely accepted in the practice, measures for further patient management require a sensitive interpretation of the point of care test results.