Review Question - QID 218220

You are hoping to utilize a screening test that ensures that only those with rheumatoid arthritis are given the experimental drug. As a result, you want a confirmatory test with the highest possible specificity (Answer 5), which is the probability that a test result will be negative in patients without the disease.

Biomedical tests can be described through a wide range of statistical concepts. Depending on the purpose of the test (screening vs. confirmation), maximization of either specificity, sensitivity, or a combination of both is paramount. Highly sensitive tests are very likely to be positive in patients with the disease being tested for. As a result, highly sensitive tests are excellent first-line screening tests, as they are unlikely to have many false-negative results. Highly specific tests are very likely to be negative in healthy individuals. As a result, highly specific tests are excellent confirmatory tests, as they ensure that healthy people are not accidentally diagnosed (false positive).

Szabo provides an excellent review of epidemiology for the orthopedic surgeon. He defines specificity as the proportion of individuals in a tested population who do not have a given disease and are identified as not having it. Furthermore, he notes that sensitivity and specificity are often mutually exclusive, and one is generally increased at the expense of another.

The work of Szabo was further updated by that of Kocher and Zurakowski, who provided a refreshed current concepts review. The authors provide suggestions on how to best choose diagnostic tests on the grounds of sensitivity, specificity, positive predictive value, and negative predictive value. They note that in cases when the consequences of a false-negative diagnosis outweigh the consequences of a false-positive diagnosis of a condition, a more sensitive diagnostic tool should be chosen.

Illustration A represents a contingency table, from which a variety of statistical values can be calculated. TP = true positive. TN = true negative. FP = false positive. FN = false negative.

Incorrect Answers:
Answer 1: Negative predictive value represents the probability that a patient with a negative test actually has no disease. It is based on the prevalence of the disease and can be calculated through TN/(TN + FN).
Answer 2: Positive predictive value represents the probability that a patient with a positive test actually has the disease, and it too is dependent on the prevalence of the disease. It can be calculated through TP/(TP + FP)
Answer 3: Precision represents how close or disparate two measurements are to one another, and it represents the random error of measurement.
Answer 4: Sensitivity represents the probability that the test will be positive in a patient with the disease. Highly sensitive tests are excellent screening, not confirmatory, tests.