Chance of Infection Given a Negative Test at Different Levels of Pretest Probabilities.

Given the need to know how well diagnostic tests rule out infection, it’s important to review assessment of test accuracy by the Food and Drug Administration (FDA) and clinical researchers, as well as interpretation of test results in a pandemic.

Analytic specificity indicates the likelihood that the test will be negative for material containing pathogens other than the target virus.

Clinical sensitivity is the proportion of positive index tests in patients who in fact have the disease in question.

Under the EUAs, the FDA does allow companies to demonstrate clinical test performance by establishing the new test’s agreement with an authorized reverse-transcriptase–polymerase-chain-reaction (RT-PCR) test in known positive material from symptomatic people or contrived specimens.

Use of either known positive or contrived samples may lead to overestimates of test sensitivity, since swabs may miss infected material in practice.1.

Designing a reference standard for measuring the sensitivity of SARS-CoV-2 tests in asymptomatic people is an unsolved problem that needs urgent attention to increase confidence in test results for contact-tracing or screening purposes.

Assessment of clinical sensitivity in asymptomatic people had not been reported for any commercial test as of June 1, 2020.

In a preprint systematic review of five studies (not including the Yang and Zhao studies), involving 957 patients (“under suspicion of Covid-19” or with “confirmed cases”), false negatives ranged from 2 to 29%.4 However, the certainty of the evidence was considered very low because of the heterogeneity of sensitivity estimates among the studies, lack of blinding to index-test results in establishing diagnoses, and failure to report key RT-PCR characteristics.4 Taken as a whole, the evidence, while limited, raises concern about frequent false negative RT-PCR results.

For a negative test, there are two key inputs: pretest probability — an estimate, before testing, of the person’s chance of being infected — and test sensitivity.

Ideally, clinical sensitivity and specificity of each test would be measured in various clinically relevant real-life situations (e.g., varied specimen sources, timing, and illness severity).

Assume that an RT-PCR test was perfectly specific (always negative in people not infected with SARS-CoV-2) and that the pretest probability for someone who, say, was feeling sick after close contact with someone with Covid-19 was 20%.

If the test sensitivity were 95% (95% of infected people test positive), the post-test probability of infection with a negative test would be 1%, which might be low enough to consider someone uninfected and may provide them assurance in visiting high-risk relatives.

At this sensitivity level, with a pretest probability of 50%, the post-test probability with a negative test would be 23% — far too high to safely assume someone is uninfected.

The blue line represents a test with sensitivity of 70% and specificity of 95%.

The green line represents a test with sensitivity of 90% and specificity of 95%.

Arrow A indicates that with the lower-sensitivity test, this threshold cannot be reached if the pretest probability exceeds about 15%.

Arrow B indicates that for the higher-sensitivity test, the threshold can be reached up to a pretest probability of about 33%.

The graph shows how the post-test probability of infection varies with the pretest probability for tests with low (70%) and high (95%) sensitivity.

With a negative result on the low-sensitivity test, the threshold is exceeded when the pretest probability exceeds 15%, but with a high-sensitivity test, one can have a pretest probability of up to 33% and still, assuming the 5% threshold, be considered safe to be in contact with others.

If the pretest probability gets too high (above 50%, for example), testing loses its value because negative results cannot lower the probability of infection enough to reach the threshold.

Second, the FDA should ensure that manufacturers provide details of tests’ clinical sensitivity and specificity at the time of market authorization; tests without such information will have less relevance to patient care.

It will also be important to develop methods (e.g., prediction rules) for estimating the pretest probability of infection (for asymptomatic and symptomatic people) to allow calculation of post-test probabilities after positive or negative results.

Fourth, negative results even on a highly sensitive test cannot rule out infection if the pretest probability is high, so clinicians should not trust unexpected negative results (i.e., assume a negative result is a “false negative” in a person with typical symptoms and known exposure).

The blue line represents a test with sensitivity of 70% and specificity of 95%

The green line represents a test with sensitivity of 90% and specificity of 95%

Arrow A indicates that with the lower-sensitivity test, this threshold cannot be reached if the pretest probability exceeds about 15%

Arrow B indicates that for the higher-sensitivity test, the threshold can be reached up to a pretest probability of about 33%