From the Guidelines
When a diagnostic test with 99% sensitivity is applied to the general population, the sensitivity remains unchanged at 99%, but the positive predictive value (PPV) will decrease and the negative predictive value (NPV) will increase due to the lower disease prevalence in the general population. This is because sensitivity is an intrinsic property of the test that measures its ability to correctly identify people with the disease (true positive rate), and it does not change with disease prevalence 1. However, the PPV and NPV are influenced by the disease prevalence, with PPV decreasing and NPV increasing in low-prevalence settings, as shown by Bayes' theorem 1.
Key Points to Consider
- The sensitivity of a diagnostic test remains constant regardless of the population being tested, as it is a measure of the test's ability to detect true positives 1.
- The PPV will decrease in a low-prevalence population because even highly specific tests will generate more false positives relative to true positives in such settings 1.
- The NPV will improve in a low-prevalence population because the likelihood that a negative result truly represents absence of disease increases with lower disease prevalence 1.
- Understanding these relationships is crucial for interpreting test results appropriately in different clinical contexts and population screening scenarios, as highlighted by recent guidance on evaluating the clinical performance of tests for SARS-CoV-2 infection 1.
Clinical Implications
- In clinical practice, a negative result from a highly sensitive test can be used to rule out disease with a high degree of confidence, especially in low-prevalence settings.
- However, a positive result from the same test may require confirmation with a more specific test to rule out false positives, particularly in low-prevalence populations.
- Clinicians should consider the pre-test probability of disease (based on factors like symptoms, exposure history, and epidemiological context) when interpreting test results, as this influences the predictive values of the test 1.
From the Research
Effect of Disease Prevalence on Diagnostic Test Metrics
- The sensitivity and specificity of a diagnostic test are important measures of its diagnostic accuracy, but they are not directly useful for estimating the probability of disease in an individual patient 2.
- Positive predictive value (PPV) and negative predictive value (NPV) can be used to estimate the probability of disease, but these values vary according to disease prevalence 2, 3.
- When a diagnostic test with high sensitivity (e.g., 99%) is applied to a population, the PPV and NPV will depend on the disease prevalence in that population 3.
- If the disease prevalence is low, even a test with high sensitivity and specificity can yield a large number of false positive results, which can lead to incorrect conclusions about the presence of disease 3.
Impact of Negative Test Result on Predictive Values
- A negative test result from a diagnostic test with high sensitivity (e.g., 99%) can provide strong evidence against the presence of disease, but the NPV will still depend on the disease prevalence in the population 2, 4.
- The NPV will be higher in populations with lower disease prevalence, and lower in populations with higher disease prevalence 2, 5.
- The sensitivity and specificity of a test can vary with disease prevalence, which can affect the accuracy of the test in different populations 5.
Considerations for Applying Diagnostic Tests to General Population
- When applying a diagnostic test to a general population, it is essential to consider the disease prevalence in that population to accurately interpret the test results 2, 3.
- The predictive values of a diagnostic test (PPV and NPV) should not be assumed to be the same across different populations with different disease prevalence 2, 5.
- Clinicians should use disease prevalence as a guide when selecting studies that most closely match their situation to ensure accurate interpretation of diagnostic test results 5.