From the Guidelines
Positive predictive value (PPV) should be prioritized in high-prevalence populations, while negative predictive value (NPV) should be prioritized in low-prevalence populations to minimize false positives and false negatives. When interpreting diagnostic test results, it is essential to consider the disease prevalence in the population being tested, as PPV and NPV are significantly influenced by this factor 1. In populations with high disease prevalence, PPV increases, making positive test results more reliable, while NPV decreases, making negative test results less reliable. Conversely, in low-prevalence settings, PPV decreases, leading to more false positives, while NPV increases, making negative test results more reliable 1.
Key Considerations
- PPV represents the probability that a person with a positive test result actually has the disease
- NPV indicates the probability that a person with a negative test result truly does not have the disease
- Disease prevalence significantly influences PPV and NPV, with high-prevalence populations having higher PPV and lower NPV, and low-prevalence populations having lower PPV and higher NPV
- Clinicians should consider disease prevalence when interpreting test results and may need to confirm positive screening results with more specific diagnostic tests, especially in low-prevalence settings 1
Implications for Clinical Practice
- In low-prevalence populations, a two-step approach with a second test can help overcome the problem of low PPV, as seen in the diagnosis of Clostridium difficile-infection (CDI) 1
- Screening tests can be used to exclude disease in low-prevalence populations, but positive results should be confirmed with more specific diagnostic tests
- Clinicians should be aware of the potential for false positives and false negatives and take disease prevalence into account when interpreting test results to minimize unnecessary treatment and ensure timely diagnosis and treatment 1
From the Research
Definition of Positive and Negative Predictive Value
- Positive predictive value (PPV) is the chance that a member of a relevant population who tests positive has been ill 2
- Negative predictive value (NPV) is the chance that someone who tests negative has not been ill 2
Relationship with Sensitivity and Specificity
- Knowledge of sensitivity and specificity enables one to predict the test result given a person's illness status, but these predictions are not directly relevant to patient care 2
- PPV and NPV can be derived if one knows the prevalence of the disease, the population rate of illness, and the sensitivity and specificity of the test 2
Factors Affecting Predictive Values
- Prevalence of the disease: uncertainty about the prevalence of some diseases, such as COVID-19, can affect the accuracy of PPV and NPV 2
- Sensitivity and specificity of the test: the accuracy of the test can impact the predictive values 2
- Population being tested: the characteristics of the population being tested can influence the predictive values 3, 4, 5, 6
Clinical Applications
- Rapid strep testing: studies have shown that rapid strep testing can be reliable in certain settings, such as in patients with recent streptococcal pharyngitis 3
- Diagnosis of streptococcal pharyngitis: clinical decision rules and rapid antigen testing can be used to assess the risk of group A beta-hemolytic streptococcal infection 5
- Antibiotic prescribing: a clinical score approach can be used to improve the pattern of antibiotic prescribing and reduce unnecessary prescriptions 6