Diagnostic sensitivity and specificity
as well as positive and negative predictive values
are important metrics that clinical lab professionals use
to evaluate the performance of laboratory tests,
including those for the novel coronavirus.
But what do they mean?
Well the diagnostic sensitivity of a test
tells us how good the test is
at identifying the people who have the disease or condition;
in other words, it’s the true positive rate.
Diagnostic specificity, on the other hand,
indicates how good the test is at excluding the people
without the disease or condition;
or, it’s the true negative rate.
Let me give you an example.
Suppose you’re using a test
to detect antibodies to the coronavirus
and assume that this test is both
95% sensitive and 95% specific.
If you perform that test on 100 people with antibodies,
you would expect to get 95 true positive test results
and 5 false negative test results.
Likewise, if you perform the test on
100 people without antibodies,
you would expect to get 95 true negative test results
and 5 false positive results.
However, diagnostic sensitivity and specificity
define the performance of a test
in terms of the patients being tested.
In actual practice, however,
the test result is all that’s known,
so we want to know how good the test is
at predicting the presence or absence
of the condition or the disease.
In other words,
what proportion of patients with a
positive test result actually have the disease.
And to answer that question
we need to know the predictive values.
The nice thing about predictive values
is that they transform the results of a test
into the probability
that a person does or does not have
the disease in question.
But they are critically dependent
upon the prevalence of the disease
in the tested population.
Here’s what I mean.
Let’s use that antibody test
with 95% sensitivity and specificity
to test a population of say 10,000 people
in which the prevalence of antibodies is 5%.
So that would mean 500 out of those 10,000 people
actually have antibodies.
And of those 500
there will be 475 true positives
and 25 false negatives.
Now, among the 9,500 people
that truly don’t have antibodies,
there will be 9,025 true negatives
and 475 false positives.
And note that the number of false positives
is the same as the number of true positives.
And so the positive predictive value of the test
is just 50%. You may as well flip a coin.
Now the positive predictive value improves
as the prevalence increases which should make sense.
When more people with antibodies are tested,
positive results are more likely to be true.
So, using that same test on a population
with a prevalence of 20%
would give us a positive predictive value of 83%.
Another way to improve the positive predictive value
is to use a test with higher diagnostic specificity
because there will be fewer false positives.
When used in a population
where antibody prevalence is, again, 5%,
an antibody test with 95% sensitivity
and 99% specificity
will yield a positive predictive value of 83%
because of the test’s improved specificity.
Of course, the two major types of COVID-19 tests—
PCR tests and antibody tests—
have different strengths and weaknesses
with respect to sensitivity and specificity.
A scenario like the one I just mentioned—
where nearly half of the positive results
turned out to be false—
can occur with antibody tests,
also known as serological tests,
if they are used to test individuals
that are unlikely to have been exposed to the virus.
When performing antibody tests,
false positive results can be minimized
by using a test with high specificity
and by reserving this type of testing
for populations that have an elevated likelihood
of past exposure to the virus.
On the flipside,
PCR tests for the virus
tend to be highly sensitive and specific
but their sensitivity depends
on many preanalytical variables
such as sample type
and the timing of sample collection
relative to day of infection.
So for example,
testing a person too soon following infection
will likely yield a negative result
and tests performed
after symptoms of COVID-19 have resolved
can actually still be positive
due to sustained shedding of inactive viral particles.
There is no lab test
that is 100% sensitive and specific
which is why it’s important to interpret test results
in conjunction with other information
about the person being tested.
Lab tests have real value
but only when used and interpreted correctly.