Negative Predictive Value: Definition and Clinical Meaning
Negative predictive value (NPV) is the probability that a patient with a negative test result truly does not have the disease being tested for. 1
Mathematical Definition
- NPV is calculated as: True Negatives / (True Negatives + False Negatives) 1
- This formula represents the proportion of all negative test results that are correct (true negatives) versus incorrect (false negatives) 1
What NPV Tells Clinicians
- A high NPV means that when a test result is negative, the patient is very likely to be free of the disease 1
- NPV provides information about the disease rather than the test itself 1
- NPV answers the clinical question: "If the test is negative, what is the probability that the patient does not have the disease?" 2
Critical Dependence on Disease Prevalence
- NPV varies dramatically with the prevalence of disease in the population being tested 1
- In populations with low disease prevalence, NPV tends to be higher 1
- When prevalence is very high (>80%), the impact on NPV becomes more significant and NPV decreases 1
- For example, in a population with 1% disease prevalence, using a test with 80% sensitivity and 99% specificity, the NPV is >99% 1
Clinical Applications
- When selecting a test for ruling out a disease, clinicians should prioritize tests with high NPV 1
- NPV is particularly important when screening for serious conditions where missing a diagnosis (false negative) could have significant consequences 1
- For screening purposes in low-prevalence populations, NPV is often high even when sensitivity is moderate 1
- A test with high NPV reassures providers that when the test is negative, the patient probably does not have the disease, supporting treatment stewardship to do no harm 3