Chapter adapted and updated, with permission, from Nicoll D et al. Guide to Diagnostic Tests, 7th ed. McGraw-Hill, 2017.
The usefulness of a test in a particular clinical situation depends not only on the test's characteristics (eg, sensitivity and specificity, which are not predictive measures) but also on the probability that the patient has the disease before the test result is known (pretest probability). Before ordering a diagnostic test in clinical practice, theoretical considerations on positive and negative predictive values need to be taken into account. The results of a useful test substantially change the probability that the patient has the disease (posttest probability). Figure e2–4 shows how posttest probability can be calculated from the known sensitivity and specificity of the test and the estimated pretest probability of disease (or disease prevalence), based on Bayes theorem.
The estimated pretest probability of disease, based on prevalence data, informed opinion, or consensus guidelines (eg, disease prediction score systems), has a profound effect on the posttest probability of disease. As demonstrated in Table e2–5, when a test with 90% sensitivity and specificity is used, the posttest probability can vary from 8% to 99% depending on the pretest probability of disease. Furthermore, as the pretest probability of disease decreases, it becomes more likely that a positive test result represents a false positive.
++ Table Graphic Jump Location Table e2–5.Influence of pretest probability on the posttest probability of disease when a test with 90% sensitivity and 90% specificity is used. ||Download (.pdf) Table e2–5. Influence of pretest probability on the posttest probability of disease when a test with 90% sensitivity and 90% specificity is used.
|Pretest Probability ||Posttest Probability |
|0.01 ||0.08 |
|0.50 ||0.90 |
|0.99 ||0.999 |
As an example, suppose the clinician wishes to calculate the posttest probability of prostate cancer using the PSA test and a cutoff value of 4 ng/mL (4 mcg/L). Using the data shown in Figure e2–5, sensitivity is 90% and specificity is 60%. The clinician estimates the pretest probability of disease given all the evidence and then calculates the posttest probability using the approach shown in Figure e2–4. The pretest probability that an otherwise healthy 50-year-old man has prostate cancer is equal to the prevalence of prostate cancer in that age group (probability = 10%), and the posttest probability after a positive test is only 20%. Even though the test is positive, there is still an 80% chance that the patient does not have prostate cancer (Figure e2–6A). If the clinician finds a prostate nodule on rectal examination, the pretest probability of prostate cancer rises to 50% and the posttest probability using the same test is 69% (Figure e2–6B). Finally, if the clinician estimates the pretest probability to be 98% based on a prostate nodule, bone pain, and lytic lesions on spine radiographs, the posttest probability using PSA is 99% (Figure e2–6C). This example illustrates that pretest probability has a profound effect on posttest probability and that tests provide more information when the diagnosis is truly uncertain (pretest probability about 50%) than when the diagnosis is either unlikely (eg, pretest probability is less than 5%) or nearly certain (eg, pretest probability is greater than 95%).
Effect of pretest probability and test sensitivity and specificity on the posttest probability of disease. (See text for explanation.) (Reproduced, with permission, from Nicoll D et al. Guide to Diagnostic Tests, 7th ed. McGraw-Hill, 2017.)
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