U01.06.003 Evaluation of diagnostic tests

Understanding the performance of diagnostic tests is crucial in medicine. Here are key points to remember about sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV), along with illustrative calculation examples:

Fixed vs. Variable Values:

Sensitivity and Specificity: These are inherent properties of a test and remain relatively constant regardless of the population being tested.
PPV and NPV: These values depend on the prevalence (how common) of the disease in the population being tested.

Interpreting Results:

Sensitivity (True Positive Rate):
- Proportion of people with the disease who test positive.
- A high sensitivity (approaching 100%) is desirable to rule out disease (low false-negative rate).
- SN-N-OUT: A highly sensitive test helps rule OUT disease with a negative result.
- Useful for screening tests where identifying all true positives is crucial.

Example Calculation:

Imagine a test for a rare disease with a sensitivity of 95%. This means that out of 100 people with the disease, 95 will test positive, and 5 will test negative (false negatives).

Sensitivity = TP / (TP + FN) = 95%
Specificity (True Negative Rate):
- Proportion of people without the disease who test negative.
- A high specificity (approaching 100%) is desirable to rule in disease (low false-positive rate).
- SP-P-IN: A highly specific test helps rule IN disease with a positive result.
- Useful for confirmation tests after a positive screening test.

Example Calculation:

Let’s say the same test has a specificity of 90%. This means that out of 100 people without the disease, 90 will test negative, and 10 will test positive (false positives).

Specificity = TN / (TN + FP) = 90%

Predictive Values:

Positive Predictive Value (PPV):
- Probability of having the disease given a positive test result.
- Increases with higher disease prevalence.

Example Calculation:

Consider a disease with a prevalence of 1% in the population. If someone tests positive using the test from the examples above (sensitivity 95%, specificity 90%), what’s the PPV?

We can’t directly calculate PPV from the information provided, but we can understand how disease prevalence affects it. Since the disease is rare (1%), even a positive test result might not always indicate the presence of the disease (due to false positives).

Negative Predictive Value (NPV):
- Probability of not having the disease given a negative test result.
- Increases with lower disease prevalence.

Cutoff Values:

Cutoff values determine a positive vs. negative test result.
A trade-off exists between sensitivity and specificity:
- Lowering the cutoff value increases sensitivity (better at ruling out disease) but decreases specificity (more false positives).
- Raising the cutoff value increases specificity (better at ruling in disease) but decreases sensitivity (more false negatives).

Choosing the Right Test:

Consider the intended use (screening vs. confirmation) and disease prevalence when selecting a diagnostic test.

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U01.06.003 Evaluation of diagnostic tests

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