RLO Transcript: PPV/NPV
This is a transcript of the Reusable Learning Object (RLO) entitled "Positive and Negative Predictive Value or Diagnostic Tests", online at:

http://www.nottingham.ac.uk/nursing/sonet/rlos/ebp/ppv_npv/

Introduction
Diagnosis is an essential step in medical care.  Usually this involves ‘diagnostic tests’ of some description.  A health check is also a group of diagnostic tests. Decisions about medical treatment are made on the basis of test results. 

An ideal, or truly accurate, test will always give a positive result with disease, and a negative result without disease.  This is not the case for all tests.  In practice this means that not all positive test results will represent disease. This is described by the Positive Predictive Value (PPV). Equally, not all negative results will represent no disease. This is described by the Negative Predictive Value (NPV).

Positive predictive value is defined as the proportion of positive tests in people with disease. 

Negative predictive value is defined as the proportion of negative tests in people without disease 

Tests

In a group of 100 people, 60 have disease, 40 do not have disease. Our ideal test applied to all these people will give 60 positive results in those who have disease and 40 negative results in those without disease.  

In practice tests are not perfect, so not all diseased patients will be picked up by the test. In this case the test gives a positive result in 48 out of the 60 who have disease, therefore 48 are true positive. However, if the test is negative for all cases without disease, then all the positive tests show disease.

False results

For most tests a negative result is not always obtained when there is no disease. Some patients will have a positive result, even if no disease is present. These are therefore, false positives, as the test is incorrect.  

Equally for most tests a positive result is not always obtained when there is disease. Some cases have a negative result, even if disease is present. These are therefore, false negatives, as the test is incorrect.  

Positive test results are a sum of the true positive and false positive results.

Negative test results are a sum of the true negative and false negative results.

Prediction Table
Data is usually presented as a table. Disease present or absent must be determined by some means and this is usually called the ‘gold’ or ‘reference’ standard. Positive Predictive Value answers the question about how likely a positive test is to represent disease.

Putting the numbers into our table gives a PPV of 48/60 = 80% 

The NPV is 28/40 = 70%

This data is obtained from studies where the number of people with disease is known. In practice we don’t know who has disease or who hasn’t (otherwise we would not need to do a test!).
Using PPV and NPV
What does a Positive Predictive Value of 85% mean? For every 100 positive tests in patients from a similar population how many would be true positives? 

A positive test result usually means that a patient will have treatment or a further test. In an asthma clinic a positive test is followed by corticosteroid treatment. If there are 20 positive tests and a PPV of 75%, how many patients would be given corticosteroids unnecessarily?

The NPV for the same test in the same clinic is 90%. For every 20 patients who test negative, how many will be false negative?

Glossary
•	Positive predictive value: the proportion of positive tests in people with disease 
•	Negative predictive value: the proportion of negative tests in people without disease 
•	False positive: a positive test result in a patient without disease
•	False negative: a negative test result in a patient with disease
•	Reference standard: the presence or absence of disease determined by approved methods 
•	Prevalence: the amount of a particular disease in a population at a given point in time