Screening tests are used to detect the presence of disease in apparently healthy or at-risk individuals. For example, an ELISA test for HIV indicates whether a patient is positive or negative for the disease.
The accuracy of a screening test is assessed by comparing its results to a gold standard diagnostic test. For HIV, the Western blot serves as the gold standard.
True vs. False Results
Test results are described based on correlation with actual disease status:
| Term | Definition | Interpretation |
|---|---|---|
| True Positive (TP) | The test is positive, the patient is actually sick | The positive result is correct |
| False Positive (FP) | The test is positive, the patient is actually healthy | The positive result is incorrect |
| True Negative (TN) | The test is negative, the patient is healthy | Negative result is correct |
| False Negative (FN) | The test is negative, the patient is sick | Negative result is incorrect |
2 × 2 Table of Screening Results
| Disease Status | Screening Positive | Screening Negative | Totals |
|---|---|---|---|
| Present | TP | FN | TP + FN |
| Absent | FP | TN | FP + TN |
| Totals | TP + FP | FN + TN | TP + FP + FN + TN |
Activity:
Measures of Test Performance
| Measure | Formula | Interpretation |
|---|---|---|
| Sensitivity | TP ÷ (TP + FN) | A probability test is positive when the disease is present |
| Specificity | TN ÷ (TN + FP) | The probability test is negative when the disease is absent |
| Positive Predictive Value (PPV) | TP ÷ (TP + FP) | The probability disease being present when the test is positive |
| Negative Predictive Value (NPV) | TN ÷ (TN + FN) | The probability disease being absent when the test is negative |
| Accuracy | (TP + TN) ÷ (TP + TN + FP + FN) | Overall proportion of correct results |
Key Points
- Screening tests are most useful in the early detection of disease before symptoms appear.
- True/False classification helps calculate test performance metrics.
- The choice of the gold standard is critical to evaluate test accuracy.
Learning Objective
Medical students should be able to define true positive, false positive, true negative, and false negative, construct a 2 × 2 table, and calculate sensitivity, specificity, predictive values, and accuracy to evaluate the performance of screening tests.
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