What is the definition of negative predictive value (NPV) of a diagnostic test?

Negative Predictive Value of a Diagnostic Test

The negative predictive value (NPV) of a test is the proportion of patients with a negative test who do not have the disease, which corresponds to option (e). 1

Definition and Calculation of NPV

• Negative predictive value (NPV) is defined by the American College of Medical Genetics as the likelihood that a patient with a negative test result really does not have the disease 1
• NPV is calculated using the formula: NPV = Number of True Negatives / (Number of True Negatives + Number of False Negatives) 1
• In statistical terms, NPV reflects the proportion of subjects with a negative test result who truly do not have the outcome of interest 2

Comparison with Other Diagnostic Test Parameters

• Sensitivity is the percentage of people with the disease who are detected by the test (true positives divided by the sum of true positives and false negatives) 1
• Specificity is the percentage of people without the disease who are correctly labeled by the test as not having the disease (true negatives divided by the sum of true negatives and false positives) 1
• Positive predictive value (PPV) is the likelihood that a patient with a positive test result actually has the disease (true positives divided by the sum of true positives and false positives) 1

Clinical Importance of NPV

• NPV provides information about the disease rather than the test itself 1
• A high NPV means that when a test result is negative, the patient is very likely to be free of the disease 1
• NPV is particularly important when screening for serious conditions where missing a diagnosis (false negative) could have significant consequences 1
• When selecting a test for ruling out a disease, clinicians should prioritize tests with high NPV 1

Factors Affecting NPV

• Disease prevalence significantly affects NPV, with NPV varying 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 1
• Unlike sensitivity and specificity which are generally considered stable for a given test, NPV inherently varies with pre-test probability (e.g., changes in population disease prevalence) 2

Example of NPV Calculation

• For a population with 1% disease prevalence, using a test with 80% sensitivity and 99% specificity, the NPV is >99% 1
• This demonstrates how in low-prevalence populations, NPV is often high even when sensitivity is moderate 1