PDF herunterladen
Gesundheitswesen 2023; 85(06): 578-594
DOI: 10.1055/a-1937-9516
CME-Fortbildung

Der diagnostische Test: Güte, Kennwerte und Interpretation Unter dem Eindruck der Corona-Pandemie und unterschiedlicher SARS-CoV-2-Tests

The Diagnostic Test: Goodness, Characteristics, and Interpretation: Under the Impact of the Corona Pandemic and Different SARS-CoV-2 Tests
Bernd Röhrig
› Weitere Informationen
Auch verfügbar auf
    Lizenzen und Reprints

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


Publikationsverlauf

Artikel online veröffentlicht:
27. Februar 2023

© 2023. Thieme. All rights reserved.

Georg Thieme Verlag
Rüdigerstraße 14, 70469 Stuttgart, Germany