A 60-year-old man comes to the office to discuss screening for Alzheimer disease. He recen… — USMLE Step 2 CK

A $60$ -year-old man comes to the office to discuss screening for Alzheimer disease. He recently read about a new plasma biomarker test that was approved for clinical use. The physician explains that this test was evaluated in a specialized memory disorders clinic where the prevalence of Alzheimer disease is $30\%$ . In that setting, the test had a sensitivity of $95\%$ and a specificity of $90\%$ . The physician is considering using this test for asymptomatic patients in the primary care setting, where the prevalence of Alzheimer disease is estimated to be $2\%$ . Which of the following best describes the expected change in the test's positive predictive value (PPV) and negative predictive value (NPV) when applied in this general practice setting compared to the specialized clinic?

PPV will decrease, and NPV will increaseAnswer
B
PPV will increase, and NPV will decrease
C
Both PPV and NPV will remain unchanged
D
PPV will decrease, and NPV will remain unchanged
E
PPV will remain unchanged, and NPV will increase

Answer

PPV will decrease, and NPV will increase

Positive predictive value (PPV) and negative predictive value (NPV) are highly dependent on the prevalence of the disease in the tested population. Sensitivity and specificity are intrinsic properties of the test and generally remain constant. When a test is moved from a high-prevalence setting (such as a specialized memory disorders clinic) to a low-prevalence setting (such as general primary care), the absolute number of disease-free individuals in the tested population increases significantly. Because the test has a fixed specificity (

90\%

), this larger disease-free group generates a proportionally larger absolute number of false-positive results. Since

PPV = \frac{TP}{TP + FP}

, the increase in false positives drives the PPV down. Conversely, the lower number of diseased individuals results in a smaller absolute number of false-negative results. Since

NPV = \frac{TN}{TN + FN}

, the decrease in false negatives drives the NPV closer to

100\%

. Therefore, moving to a low-prevalence setting decreases PPV and increases NPV.

Step-by-Step Solution

Identify the relationship between disease prevalence and intrinsic test characteristics.

Sensitivity and specificity are inherent to the test and remain unchanged when moving between populations.

These values depend only on the test's biological properties, not the population tested.

Analyze the change in disease prevalence between the two settings.

The prevalence decreases drastically from

30\%

in the specialty clinic to

2\%

in the primary care setting.

This establishes the mathematical direction for the predictive values.

Determine the effect of decreased prevalence on PPV and NPV.

A lower prevalence increases the absolute number of healthy individuals, leading to more false positives and a lower PPV. It also means fewer diseased individuals, leading to fewer false negatives and a higher NPV.

PPV = \frac{TP}{TP + FP}

and

NPV = \frac{TN}{TN + FN}

Key Concept

Positive predictive value (PPV) and negative predictive value (NPV) are highly dependent on disease prevalence. As prevalence decreases, PPV decreases and NPV increases.

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