CDC Clarifies Coronavirus Antibody Testing Specificity
During the current coronavirus pandemic, maximizing lab test specificity is very important, says the US Centers for Disease Control and Prevention (CDC) in its newly published ‘Interim Guidelines for COVID-19 Antibody Testing.’
On May 23, 2020, the CDC said ‘positive predictive value in a serologic algorithm is preferred in most instances since the overall prevalence of SARS-CoV-2 antibodies is likely low in most states, except New York.
For example, in a local population where the coronavirus prevalence is 5%, a test with 90% sensitivity and 95% specificity will yield a positive predictive value of 49%.
In other words, less than half of those testing positive will truly have antibodies.
Alternatively, the CDC says the same test in a population with an antibody prevalence exceeding 52%, will yield a positive predictive greater than 95%, meaning that less than one in 20 people testing positive will have a false-positive test result.
The CDC offers 3 strategies that can be used to improve positive predictive value: Choosing a test with very high specificity, perhaps 99.5% or greater, will yield a high positive predictive value in populations tested with prevalence >5%.
Another strategy is to focus testing on persons with a high pre-test probability of having SARS-CoV-2 antibodies, such as persons with a history of COVID-19 like illness.
A third approach is to employ an orthogonal testing algorithm in which persons who initially test positive are tested with a second test. Effective orthogonal algorithms are generally based on testing a patient sample with two tests, each with unique design characteristics.
Additionally, test algorithms can be designed to maximize overall specificity while retaining maximum sensitivity.
For example, in the example above with a population prevalence of 5%, a positive predictive value of 95% can be achieved if samples initially positive are tested with a second different orthogonal assay that also has 90% sensitivity and 95% specificity.
At present, the immunologic correlates of immunity from SARS-CoV-2 infection are not well defined.
Representatives from the CDC, FDA, and other organizations are working with members of academia and the medical community to determine whether positive serologic tests are indicative of protective immunity against SARS-CoV-2.
This work includes assessing the level of antibodies required for protection from reinfection, the duration of that protection, and the factors associated with the development of a protective antibody response.
The kinetics of antibody response, the longevity of antibodies, the ability of antibodies to protect from repeat infection, the protective titer of neutralizing antibody, and the correlation of binding antibody titers to neutralization ability are yet to be determined.
Although animal challenge studies demonstrate protection in the short run, demonstration of long-term protection in humans will require future study.
Hence, pending additional data, the presence of antibodies cannot be equated with an individual’s immunity from SARS-CoV-2 infection.
Some tests may exhibit cross-reactivity with other coronaviruses, such as those that cause the common cold.
This could result in false-positive test results.
Some persons may not develop detectable antibodies after coronavirus infection. In others, it is possible that antibody levels could wane over time to undetectable levels.
As an example, IgM and IgG antibodies are not present early in infection.
Thus, serologic test results do not indicate with certainty the presence or absence of current or previous infection with SARS-CoV-2 coronavirus, says the CDC.
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