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M09.01.06 Measurement of screening test
Test performance metrics such as sensitivity and specificity provide valuable insights into the reliability of diagnostic and screening tests. They are particularly useful when gold-standard tests are unavailable or impractical due to high costs or complexity. This structured explanation covers the principles, calculations, and applications of these measures.
Understanding Sensitivity and Specificity
Sensitivity
Definition: Sensitivity is the probability that a test correctly identifies individuals with the disease. It is also referred to as the “true positive rate.”
Formula
Sensitivity=True Positives (TP)/(True Positives (TP) + False Negatives (FN))
Key Points
- Sensitivity measures the test’s ability to detect the disease among those who truly have it.
- High sensitivity implies that a negative test result effectively rules out the disease.
- Mnemonic: SN-N-OUT – Sensitive test when Negative rules OUT disease.
Example
In the case of temporal arteritis (TA):
- ESR is elevated in all patients with TA (100% sensitivity).
- Therefore, a patient with suspected TA but a normal ESR is likely disease-free.
Specificity
Definition: Specificity is the probability that a test correctly identifies individuals without the disease. It is known as the “true negative rate.”
Formula
Specificity=True Negatives (TN)/(True Negatives (TN) + False Positives (FP))
Key Points
- Specificity measures the test’s ability to exclude the disease in disease-free individuals.
- High specificity indicates that a positive test result strongly suggests the presence of the disease.
- Mnemonic: SP-P-IN – Specific test when Positive rules IN disease.
Example
A CT angiogram has a specificity of approximately 97% for pulmonary embolism. Thus, a positive CT scan is highly indicative of the condition.
2. Practical Applications and Calculations
Table: Sensitivity and Specificity in a 2×2 Contingency Table
| Disease Present (D+) | Disease Absent (D-) | |
|---|---|---|
| Test Positive (T+) | True Positive (TP) | False Positive (FP) |
| Test Negative (T-) | False Negative (FN) | True Negative (TN) |
- Sensitivity uses data from the left column (Disease Present) of the 2×2 table.
- Specificity uses data from the right column (Disease Absent) of the 2×2 table.
Calculating Sensitivity and Specificity with Example Data
Suppose we have 200 patients with a disease, and the test identifies 160 as positive:
Sensitivity=160/200=0.80 or 80%
Points to Remember
- High Sensitivity: A negative result in a highly sensitive test rules out the disease.
- High Specificity: A positive result in a particular test confirms the disease.
- False Negative Rate: 1−Sensitivity
- False Positive Rate: 1−Specificity
Bibliography and Further Reading
- Gordis, L. Epidemiology. 5th ed. Philadelphia: Elsevier Saunders, 2014.
- Fletcher, R. H., Fletcher, S. W., & Fletcher, G. S. Clinical Epidemiology: The Essentials. 5th ed. Philadelphia: Wolters Kluwer Health, 2014.
- Pagano, M., & Gauvreau, K. Principles of Biostatistics. 2nd ed. Belmont, CA: Brooks/Cole, 2000.