Positive Predictive Value and Negative Predictive Value Study Outcomes Mini-Presentation Created by Thomas Hahn, MD Edited by John Kloke, PhD

Objectives Following this presentation, the learner will be able to: Understand the concepts and calculations for positive predictive value and negative predictive Apply PPV and NPV to patient care Communicate PPV and NPV to patients

UNDERSTAND the CONCEPT

PPV and NPV Positive Predictive Value: Probability that subjects with a positive test have the disease. Negative Predictive Value: Probability that subjects with a negative test do not have the disease.

Positive Predictive Value Positive Predictive Value: Proportion of positive test results that are true positives PPV = True Positives x 100% All Positives

Negative Predictive Value Negative Predictive Value: Proportion of negative test results that are true negatives NPV = True Negatives x 100% All Negatives

You need to know these equations for boards! 2x2 Table You need to know these equations for boards! Disease Present No Disease a (true positive) b (false positive, Type 1 error) c (false negative, Type 2 error) d (true negative) Positive Test PPV = a/(a+b) Negative Test NPV = d/(d+c) Sensitivity and specificity focus on the DISEASE: total number of people with or without the disease makes up the denominator Positive and negative predictive values focus on the TEST: total number of people with positive or negative tests makes up the denominator Sensitivity = a/(a+c) Specificity = d/(d+b) PPV = true positives x100% NPV = true negatives x100% (true positives + false positives) (true negatives + false negatives)

PPV/NPV Characteristics Predictive value is determined by the sensitivity and specificity of a test Sensitivity (low false negatives)  NPV Specificity (low false positives)  PPV Predictive value is determined by the prevalence of a disease in the population Prevalence  PPV, NPV Prevalence  PPV, NPV As prevalence of a disease in a population increases, PPV increases and NPV decreases As prevalence of a disease in a population decreases, PPV decreases and NPV increases Bayes’ theorem connects PPV/NPV with Sn/Sp

Example of Effect of Prevalence on PPV/NPV Hypothetically test 10 people for Lyme disease in Lyme, Connecticut (high prevalence) and Boise, Idaho (low prevalence) Lyme, Connecticut 9/10 positive tests. High likelihood that the tests are truly positive (Prevalence  PPV, NPV) Boise, Idaho 9/10 negative tests. High likelihood that the tests are truly negative (Prevalence  PPV, NPV)

Predictive Value and Low Disease Prevalence How can you increase predictive value of a test when there is a low preclinical disease prevalence? Example: Breast cancer Prevalence is low in the population (about 1% in 2012) To increase PPV, target screening to those at highest risk of developing the disease: women > 50 years old Prevalence data based on about 3 million cases of breast cancer in 2012, when US population was around 314 million.

PPV/NPV vs Sensitivity/Specificity Sensitivity and specificity Focus on the disease Important to epidemiologists and clinicians Positive and negative predictive values Focus on the test Important to patients The denominator in sens/spec is the total number of people w/ and w/out disease The denominator in PPV/NPV is total number of positive and negative tests

2x2 Table Disease Present No Disease a (true positive) b (false positive, Type 1 error) c (false negative, Type 2 error) d (true negative) Positive Test PPV = true positives all positives Negative Test NPV = true negatives all negatives Sensitivity and specificity focus on the DISEASE: total number of people with or without the disease makes up the denominator Positive and negative predictive values focus on the TEST: total number of people with positive or negative tests makes up the denominator Sensitivity = true positives Specificity = true negatives all with disease all without disease

APPLY the OUTCOME

Using Predictive Value A PPV helps to answer the question: “If the patient’s test is positive, what are the chances that the patient has the disease?” Predictive values may make more sense to the patient than sensitivity/specificity Explains how many people with a positive test are truly positive vs how many people with the disease will test positive.

Application Example The negative predictive value of a screening test for Lyme disease is 50%? What does this mean? This is not a good test for ruling out Lyme disease. 50% of people with a negative test will actually have Lyme disease!!!

COMMUNICATE the DATA

Communicating to Patients A patient presents with a tick bite, and you decide to test for Lyme disease using a new serum test. The patient asks you, “How good is this test?” Knowing that the PPV is 85%, how do you answer this question? “If your test is positive, there is an 85% chance that you have the disease.” (test is 85% accurate) “Out of all the positive tests, 85% will be correct.” “If 100 people have a positive test, 85 of those will have the disease and 15 will not have the disease.”

Practice Question A study evaluating a two-tier screening test for Lyme disease in a low prevalence area tested 4723 patients for Lyme disease. Of the patients who had positive tests, 12 had Lyme disease and 58 did not have Lyme disease. What is the positive predictive value of the test in this low prevalence area? A. 17% B. 20% C. 1% D. 15% Disease No Disease Positive Test 12 58 Negative Test 4653 Lantos, P.M. et al. (2015). Poor positive predictive value of lyme disease serologic testing in an area of low disease incidence. Clinical Infectious Diseases, 584. http://www.ncbi.nlm.nih.gov/pubmed/26195017

Practice Question A study evaluating a two-tier screening test for Lyme disease in a low prevalence area tested 4723 patients for Lyme disease. Of the patients who had positive tests, 12 had Lyme disease and 58 did not have Lyme disease. What is the positive predictive value of the test in this low prevalence area? A. 17% (12/70 = PPV) B. 20% (12/58) C. 1% (70/4653) D. 15% http://cid.oxfordjournals.org/content/53/6/541.long#T4 Disease No Disease Positive Test 12 58 Negative Test 4653

The worst predictive value?

References AAFP Family Medicine Board Review Course http://www.ncbi.nlm.nih.gov/pubmed/26195017