## Definition of Sensitivity **Key Point:** Sensitivity is the ability of a test to correctly identify those WITH the disease. It answers the question: "If someone has the disease, what is the probability the test will be positive?" ### Formula $$\text{Sensitivity} = \frac{TP}{TP + FN}$$ Where: - TP = True Positives (diseased individuals who test positive) - FN = False Negatives (diseased individuals who test negative) ### Clinical Interpretation A sensitivity of 95% means that if 100 people with tuberculosis are tested, 95 will test positive and 5 will be missed (false negatives). **High-Yield:** Sensitivity is a **disease-centric** metric — it focuses on the diseased population and the test's ability to catch them. A test with high sensitivity is good for **ruling out disease** (high sensitivity = low false negative rate = safe screening test). ### Mnemonic **SNOUT** — **S**ensitivity rules **OUT** disease. If sensitivity is high and the test is negative, you can confidently exclude the disease. ### Comparison with Other Metrics | Metric | Formula | Interpretation | Population Focus | |--------|---------|-----------------|------------------| | **Sensitivity** | TP / (TP + FN) | Probability test is **+** given disease is present | **Diseased** | | **Specificity** | TN / (TN + FP) | Probability test is **−** given disease is absent | **Non-diseased** | | **PPV** | TP / (TP + FP) | Probability disease is present given test is **+** | **Test result** | | **NPV** | TN / (TN + FN) | Probability disease is absent given test is **−** | **Test result** | **Clinical Pearl:** In screening programs for serious but treatable diseases (e.g., TB, cancer), high sensitivity is prioritized to avoid missing cases, even if specificity is lower (leading to more false positives that can be confirmed with a second test).
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