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Difficulty: EasyDiagnostic and Screening Test Performance

A 22-year-old man with no significant past medical history presents to the clinic with mild, intermittent knee pain that occurs only after running. A 55-year-old woman with a strong family history of rheumatoid arthritis presents with bilateral hand pain, joint swelling, and morning stiffness lasting over an hour. The physician orders a rheumatoid factor assay for both patients, and both tests return a positive result. The physician informs the patients that the positive test in the 55-year-old woman is much more likely to represent true rheumatoid arthritis than the positive test in the 22-year-old man. Which of the following principles of test performance best explains the physician's reasoning?

  1. The positive predictive value of a test increases when the pre-test probability of the disease is higher.Answer
  2. B
    The sensitivity of a diagnostic test is higher in patient populations with multiple clinical risk factors.
  3. C
    The negative predictive value of a test increases proportionally with the prevalence of the disease.
  4. D
    The specificity of a diagnostic test increases when the pre-test probability of the disease is higher.
  5. E
    The positive likelihood ratio of a test increases as the prevalence of the disease in the population increases.

Answer

The positive predictive value of a test increases when the pre-test probability of the disease is higher.
The correct statement accurately reflects Bayes' theorem applied to clinical medicine. The positive predictive value (PPV) is defined as the probability that a patient actually has the disease given a positive test result (PPV=TruePositivesTruePositives+FalsePositivesPPV = \frac{True Positives}{True Positives + False Positives}). When a test is applied to a population with a high pre-test probability (like the 55-year-old woman with classic symptoms), the number of true positives is much higher relative to false positives, resulting in a high PPV. Conversely, applying the exact same test to a patient with a low pre-test probability yields a high proportion of false positives, resulting in a low PPV.

Step-by-Step Solution

1
Analyze the clinical scenarios to establish the pre-test probability for each patient.
The 22-year-old man has a very low pre-test probability (low prevalence scenario), while the 55-year-old woman has a high pre-test probability (high prevalence scenario) for rheumatoid arthritis.
Establishing the pre-test probability is essential for determining how test characteristics apply to individual patients.
2
Recall how disease prevalence (pre-test probability) affects diagnostic test characteristics.
Intrinsic test properties (sensitivity, specificity, likelihood ratios) do not change. Extrinsic properties (positive and negative predictive values) change significantly based on prevalence.
Differentiating between fixed intrinsic test properties and variable predictive values is the core concept being tested.
3
Determine the relationship between pre-test probability and positive predictive value (PPV).
As pre-test probability increases, the ratio of true positives to false positives increases, meaning the PPV increases. Therefore, the positive test in the high-risk woman is more likely to be a true positive.
This directly explains the physician's interpretation of the identical test results in two different patients.

Key Concept

The positive predictive value (PPV) of a diagnostic test is directly proportional to the pre-test probability (prevalence) of the disease in the tested population, whereas sensitivity and specificity remain constant.
Estimated Time:50s
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Topics

Diagnostic and Screening Test Performance

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