In evaluating the diagnostic accuracy of a screening test, all of the following statements are correct EXCEPT:

Explanation

## Diagnostic Accuracy of Screening Tests — Sensitivity, Specificity, and Predictive Values **Key Point:** Predictive values are NOT independent of disease prevalence. This is a critical concept frequently tested in NEET PG. ### Definitions and Formulas | Parameter | Formula | Interpretation | |-----------|---------|----------------| | **Sensitivity (True Positive Rate)** | $\frac{TP}{TP+FN}$ | Proportion of diseased individuals correctly identified | | **Specificity (True Negative Rate)** | $\frac{TN}{TN+FP}$ | Proportion of non-diseased individuals correctly identified | | **Positive Predictive Value (PPV)** | $\frac{TP}{TP+FP}$ | Probability of disease given a positive test result | | **Negative Predictive Value (NPV)** | $\frac{TN}{TN+FN}$ | Probability of no disease given a negative test result | **High-Yield:** Sensitivity and specificity are **test properties** — they are fixed for a given test and do not change with prevalence. However, PPV and NPV are **population properties** — they depend on disease prevalence. ### The Prevalence Paradox ```mermaid flowchart TD A[Test with fixed Sensitivity & Specificity]:::outcome A --> B[High Prevalence Population]:::outcome A --> C[Low Prevalence Population]:::outcome B --> D[PPV increases<br/>NPV decreases]:::action C --> E[PPV decreases<br/>NPV increases]:::action D --> F[More true positives<br/>relative to false positives]:::outcome E --> G[More false positives<br/>relative to true positives]:::outcome ``` **Mnemonic:** **"SPIN and SNOUT"** - **SP**ecificity **IN** — High specificity rules IN disease (few false positives) - **SN**ensitivity **OUT** — High sensitivity rules OUT disease (few false negatives) ### Why Option 3 Is Wrong **Clinical Pearl:** Option 3 claims PPV is independent of prevalence — this is **FALSE**. PPV is directly affected by disease prevalence: - **High prevalence:** More true cases in the population → more TP relative to FP → **high PPV** - **Low prevalence:** Fewer true cases in the population → more FP relative to TP → **low PPV** **Example:** A screening test with 95% sensitivity and 95% specificity applied to two populations: | Scenario | Prevalence | TP | FP | PPV | |----------|-----------|----|----|-----| | High-risk population | 10% | 95 | 45 | 68% | | Low-risk population | 1% | 95 | 495 | 16% | Same test, same sensitivity/specificity, but PPV drops from 68% to 16% because prevalence is lower. ### Why Options 1, 2, and 4 Are Correct **Option 1:** Correct definition. Sensitivity = TP/(TP+FN) identifies the proportion of truly diseased people who test positive. **Option 2:** Correct definition. Specificity = TN/(TN+FP) identifies the proportion of truly non-diseased people who test negative. **Option 4:** Correct principle. Sensitivity and specificity are **inherent properties of the test** and do not change with the population being tested (assuming the test is applied consistently). **Warning:** A common trap is confusing PPV/NPV with sensitivity/specificity. Remember: Sensitivity/specificity describe the **test**; PPV/NPV describe the **clinical utility in a specific population**. [cite:Park 26e Ch 10]