Definition of Sensitivity
The sensitivity of a test is the proportion of patients with the disease who have a positive test, making answer (a) correct. 1
Understanding Sensitivity
Sensitivity is defined as the percentage of people with the disease who are detected by the test 1. It is calculated mathematically as the number of true-positive (TP) results divided by the sum of true-positive and false-negative (FN) results 1.
In practical terms:
- Sensitivity tells you how good a test is at identifying people who actually have the disease 1
- A highly sensitive test will catch most cases of disease (few false negatives) 1
- Sensitivity is influenced by disease severity, effort level, and medications such as anti-ischemic drugs 1
Why the Other Options Are Incorrect
- Option (b) describes specificity, not sensitivity. Specificity is the percentage of people without disease who have a negative test 1
- Option (c) describes positive predictive value (PPV), which is the likelihood that a patient with a positive test actually has the disease 1
- Option (d) describes negative predictive value (NPV), which is the likelihood that a patient with a negative test does not have the disease 1
Key Clinical Distinction
Sensitivity and specificity provide information about the test itself, while predictive values (PPV and NPV) provide information about the disease probability in individual patients 1. This is a critical distinction because:
- Sensitivity and specificity remain relatively constant characteristics of the test 1
- Predictive values vary with disease prevalence in the population being tested 1
- Sensitivity increases when testing populations with more severe disease (e.g., triple-vessel coronary disease versus single-vessel disease) 1
Important Relationship
Sensitivity and specificity are inversely related 1. When you adjust the diagnostic cut point to maximize sensitivity (catching more disease cases), specificity decreases (more false positives), and vice versa 1. This trade-off is visualized using receiver operating characteristic (ROC) curves 1.