Diagnostic and screening tests - sensitivity

Definition

Sensitivity is the proportion of people who truly have the disease who test positive - a highly sensitive test has few false negatives, making it useful for ruling a diagnosis out when negative (mnemonic: SnNout - a highly Sensitive test, when Negative, rules out)
A fixed property of a given test (does not change with disease prevalence in the tested population), making it directly comparable across different clinical settings and populations, unlike predictive values

Sensitivity reflects the test's ability to correctly identify true positives among all people who actually have the condition - a highly sensitive test used as a screening tool minimises the risk of missing genuine disease, an important property when the cost of a missed diagnosis (false negative) is high relative to the cost of a false alarm requiring further work-up

Calculated as true positives divided by (true positives plus false negatives) - requires a reference standard (confirmed disease status) against which the test is compared to determine this ratio in a validation study
Should always be interpreted alongside specificity, since a test's sensitivity and specificity together (not either alone) fully characterise its diagnostic performance

Highly sensitive tests are preferentially used for initial screening in conditions where missing a case has serious consequences, accepting a higher false-positive rate as an acceptable trade-off given subsequent confirmatory testing will filter out false positives
A negative result on a highly sensitive test provides strong reassurance against the diagnosis being screened for, given the low false-negative rate

SnNout mnemonic (Sensitive test, Negative result, rules OUT disease), sensitivity as a fixed test property (unlike predictive values), screening test design favouring high sensitivity, trade-off with specificity in test design
A highly sensitive test, when negative, effectively rules out the disease being tested for (SnNout) - this is the key practical application of sensitivity, explaining why screening tests (where missing a true case carries high cost) are specifically designed and selected to maximise sensitivity, even if this means accepting more false positives requiring confirmatory follow-up testing

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