Predictive Values and Disease Prevalence

Overview

This lecture explains positive predictive value (PPV) and negative predictive value (NPV), how to calculate them, and the impact of disease prevalence on their interpretation.

Positive and Negative Predictive Values

• Positive predictive value (PPV) is the probability that a person has the disease given a positive test result.
• Negative predictive value (NPV) is the probability that a person does not have the disease given a negative test result.
• PPV and NPV depend on the prevalence (commonness) of the disease in the tested population.

Calculating Prevalence

• Prevalence = (number of people with the disease) / (total number of people tested).
• Example: If 250 people have the disease out of 1,000 tested, prevalence is 250/1,000 = 25%.

Calculating PPV and NPV

• PPV = true positives / (true positives + false positives).
• Using numbers: 220 true positives, 75 false positives; PPV = 220/(220+75) = 0.75 (75%).
• NPV = true negatives / (true negatives + false negatives).
• Using numbers: 675 true negatives, 30 false negatives; NPV = 675/(675+30) = 0.96 (96%).

Interpreting Predictive Values

• A positive test increases disease probability from the prevalence to PPV (e.g., 25% to 75%).
• A negative test increases chance of not having the disease from prevalence of healthy individuals to NPV (e.g., 75% to 96%).
• Predictive values from studies are only valid if your patient population has the same disease prevalence as in the study.

Limitations and Next Steps

• PPV and NPV are less useful when disease prevalence in your setting is different from that in the study.
• Likelihood ratios are recommended as a better alternative in most cases.

Key Terms & Definitions

• Positive Predictive Value (PPV) — Proportion of true disease cases among those who tested positive.
• Negative Predictive Value (NPV) — Proportion of true non-disease cases among those who tested negative.
• Prevalence — Proportion of the population who have the disease.
• True Positives — Patients who have the disease and tested positive.
• False Positives — Patients who do not have the disease but tested positive.
• True Negatives — Patients who do not have the disease and tested negative.
• False Negatives — Patients who have the disease but tested negative.

Action Items / Next Steps

• Review the concepts of sensitivity and specificity if not already familiar.
• Learn about likelihood ratios as a more practical tool for clinical decision-making.