CONCEPT OF SENSITIVITY/SPECIFICITY TO DIAGNOSTIC TESTING 5
Sensitivity depicts the ability of a test to determine accurately theindividuals that have an illness from all persons without theillness. It refers to the proportion of correctly diseasedindividuals from a screened population that is determined to bediseased by a screening test, meaning they have higher scores (Linnetet al, 2012). Sensitivity demonstrates the probability that the testwill properly aid in the diagnosis of a case, or the possibility thata specific case is possible to identify via the test.
Specificity refers to the capability of a test is determiningaccurately the people that lack an illness from all persons free ofthe illness. It regards to the percentage of individuals without theillness that indicate low scores on the screening test, or thepossibility of a test at accurately determining a non-diseasedindividual (Linnet et al, 2012). A specific test picks just theillness under diagnosis, thus comprises of a narrow focus explainingthe phrase specific.
Sensitivity and specificity are statistical calculations of thepresentation of a binary categorization test. In diagnostic testing,the concepts help in the selection, as well as interpreting of testresults. Sensitivity/specificity values avail important informationapplied to interpret the outcomes of diagnostic tests. Sensitivity isa representation of the capability of the test in recognizing acondition when present (Leeflang et al, 2013). A highly sensitivetest comprises rather minimal incorrect negative outcomes.
High-test sensitivity, hence, indicates to the negative test resultvalue. When sensitivity is high, the negative result is useful inruling out the condition. High sensitivity depicts that a test may beemployed for eliminating, a situation when negative, but fails totackle the positive value test. Specificity demonstrates thecapability to employ a test in realizing when a condition is notpresent. A highly specific test comprises minimal incorrect positiveresults, and hence communicates to the assessment of a positive test(Naeger et al, 2013). Few tests have a high sensitivity as well asspecificity.
Knowhow on the sensitivity/specificity of a test is important inassisting clinicians improve clinical decisions. The knowledgeassists them in weighing the relative positive or negative valueresults (Moynihan, Henry &Moons, 2014). The values derived from the tests avail helpfulinformation by inferring the possibility that a result is right,owing to the outcome of a reference standard. The values may beemployed as independent approximations of the helpfulness of positiveand negative test effects (Moynihan,Henry & Moons, 2014).
For instance, to help in diagnosis, a physician would select a testcomprising satisfactory test traits, which issensitivity/specificity. Sensitivity being the capability of thediagnostic test at categorizing individuals with the illness aspositive, through high fasting glucose when diagnosing diabetesmellitus, or low ferritin when diagnosing a lack of iron. Sensitivitydefines the possibility an individual from the target disorder testspositive, while specificity is the capability of a diagnostic test inclassifying those that lack the illness as negative, which isabove/below cutoff in reference to the test.
In another illustration, supposing there is a population of 1000persons, 100 have an illness, while 900 have no illness. A screeningtest is employed in determining the 100 individuals that have theillness. The screening results are:
Results after screening
True population traits/ Disease
True population traits/ No Illness
To determine sensitivity and specificity:
Sensitivity is 80/100, which equals 80%, while specificity is800/900, which equals 89%.
Leeflang, M. M., Rutjes, A. W.,Reitsma, J. B., Hooft, L., & Bossuyt, P. M. (2013). Variation of a test’s sensitivity and specificity with disease prevalence.Canadian MedicalAssociation Journal, 185(11),E537-E544.
Linnet, K., Bossuyt, P. M., Moons,K. G., & Reitsma, J. B. (2012). Quantifying the accuracy of a diagnostic test or marker. Clinicalchemistry, 58(9),1292-1301.
Moynihan, R., Henry, D., &Moons, K. G. (2014). Using evidence to combat over diagnosis and overtreatment: evaluating treatments, tests, and disease definitionsin the time of too much. PLoSmedicine, 11(7),e1001655.
Naeger, D. M., Kohi, M. P., Webb,E. M., Phelps, A., Ordovas, K. G., & Newman, T. B. (2013). Correctly using sensitivity, specificity, and predictive values inclinical practice: how to avoid three common pitfalls. AmericanJournal of Roentgenology, 200(6),W566-W570.