Grundlage für ein genaues Bild des Infektionsgeschehens sowie für
die Maßnahmen zur Eindämmung der Pandemie ist die
möglichst sichere Identifizierung Corona-Infizierter. Zum Nachweis einer
Infektion mit SARS-CoV-2 werden vorwiegend 3 Testverfahren genutzt: der
PCR-Test, der Antigen-Test und der Antikörpertest. Dieser Beitrag gibt
einen Überblick über die unterschiedlichen Ziele, Grundbegriffe,
Kennwerte und Probleme dieser diagnostischen Tests.
Abstract
Introduction Many diagnostic tests are currently being performed around
the world to detect SARS-CoV-2 infection. Positive and negative test results are
not one hundred percent accurate, but have far-reaching consequences. There are
false positives (test positive, uninfected) and false negatives (test negative,
infected). A positive/negative result does not necessarily mean that the
test subject is actually infected/non-infected. This article has two
objectives: 1. to explain the most important characteristics of diagnostic tests
with binary outcome 2. to point out problems and phenomena of interpretation of
diagnostic tests, on the basis of different scenarios.
Method Presentation of the basic concepts of the quality of a diagnostic
test (sensitivity, specificity) and pre-test probability (prevalence of test
group). Calculation (including formulas) of further important quantities.
Results In the basic scenario, sensitivity is 100%, specificity
98.8%, and pre-test probability of 1.0% (10 infected persons per
1,000 tested). For 1,000 diagnostic tests, the statistical mean is 22 positive
cases, 10 of which are true-positive. The positive predictive probability is
45.7%. The prevalence calculated from this (22/1,000 tests)
overestimates the actual prevalence (10/1,000 tests) by a factor of 2.2.
All cases with a negative test outcome are true negative. The prevalence has a
strong influence on the positive and negative predictive value. This phenomenon
occurs even with otherwise very good test values of sensitivity and specificity.
At a prevalence of only 5 infected persons per 10,000 (0.05%), the
positive predictive probability drops to 4.0%. Lower specificity
amplifies this effect, especially with small numbers of infected persons.
Conclusion If the sensitivity or specificity is below 100%,
diagnostic tests are always error-prone. If the prevalence of infected persons
is low, a large number of false positive results are to be expected –
even if the test is of good quality with a high sensitivity and especially a
high specificity. This is accompanied by low positive predictive values,
i. e. positive tested persons are not infected. A false positive test
result in the first test can be clarified by carrying out a second test.
Schlüsselwörter
diagnostischer Test - Prävalenz - Sensitivität - Spezifität - Vorhersagewert - SARS-CoV-2-Test
Key words
diagnostic test - prevalence - sensitivity - specificity - predictive value - SARS-CoV-2 test
