Positive Predictive Value of a Test
The positive predictive value of a test is the proportion of persons with a positive test who have the disease (answer choice c).1
Definition and Calculation of Positive Predictive Value
- Positive predictive value (PPV) is defined as the proportion of true positive outcomes among all positive tests in a study population 1
- PPV is calculated using the formula: Number of True Positives / (Number of True Positives + Number of False Positives) 2
- PPV represents the likelihood that a patient with a positive test result actually has the disease 2
- Unlike sensitivity and specificity, which are test characteristics, PPV is a measure that reflects the probability of disease given a test result 2
Comparison with Other Test Performance Metrics
- Sensitivity is the proportion of patients with the disease who have a positive test (answer choice a) 2
- Specificity is the proportion of persons without the disease who have a negative test (answer choice b) 2
- Negative predictive value is the proportion of persons with a negative test who do not have the disease (answer choice d) 2
Factors Affecting Positive Predictive Value
- PPV depends greatly on the prevalence (or incidence) of the disease in the population being tested 1, 3
- Even a test with nearly perfect specificity will have a poor PPV when the population prevalence of the disorder is low 1
- For screening tests, the PPV is less dependent on test sensitivity than on its specificity 1
- In populations with low disease prevalence, PPV tends to be lower, even with highly specific tests 3
Clinical Examples Demonstrating PPV
- In a population with 1% disease prevalence, using a test with 80% sensitivity and 99% specificity, the PPV would be only 45% 1
- This means 55% of people who test positive do not have the disease and could be unnecessarily treated or quarantined 1
- In contrast, with a 10% disease prevalence using the same test, the PPV increases to 90% 1
- For ECG screening in athletes, where cardiovascular abnormalities have low prevalence, the PPV is poor despite good specificity 1
Clinical Implications
- When interpreting positive test results, clinicians must consider the prevalence of disease in their specific population 3
- In low-prevalence settings, positive test results should be interpreted with caution due to lower PPV 3
- To overcome problems of low PPV in low-prevalence settings, two-step testing approaches are sometimes recommended 1
- Understanding PPV is critical for avoiding unnecessary treatments, further testing, and patient anxiety from false positive results 4