Negative predictive value

Negative predictive value (NPV) is one of the 4 basic diagnostic test metrics in addition to sensitivity, specificity and positive predictive value. Negative predictive value is a test/investigationmeasure of how often someone who tests negative for disease does not have disease and is defined ascalculated by dividing the proportionnumber of patients withtrue negatives (TN) by the number of people who tested negative results being truly disease free, i.e. true negatives and false negatives (FN):

• TN/(TN + FN)

The formula shows that a high NPV is achieved by maximising true negatives and minimising false negatives.

Negative predictive value = true negatives detected / totalcan be expressed as a conditional probability:

• P(Disease negative results

  (where "total|Test negative results" = true negative + false negative)

Bayes' theorem

One

• NPV = [(specificity) x (1 - p)] / [specificity x (1 - p) + (1 - sensitivity) x (p)]

  Practical points

  • unlike

  Unlike sensitivity and specificity, NPV is highly dependentdepends to some extent on disease prevalence. When prevalence increases, NPV decreases because the prevalenceproportion of true negatives to false negatives decreases. For example, if the entire population of interest had disease, all of the diseasenegatives would be false negatives, there would be no true negatives, resulting in a NPV of 0%. When prevalence decreases, NPV increases because the proportion of true negatives to false negatives increases. For example, if no one in the target population