For example, Table 3 uses the same sensitivity, specificity and sample size as for the data in Table 1, but the prevalence (proportion of deaths) has been changed from 126/1391 = 9% to 600/1391 = 43%. However, although the PPV and NPV give a direct assessment of the usefulness of the test, they are affected by the prevalence of the disease. Sensitivity and specificity are characteristics of a test and are not affected by the prevalence of the disease. The 95% confidence interval for PPV is 10–15% and that for NPV is 92–96%. Therefore, 12% of patients in the sample whose test results were positive actually died and 94% whose test results were negative survived. Similarly, the negative predictive value (NPV) is the probability that a patient has a negative outcome given that they have a negative test result, in contrast to specificity, which is the probability that a patient has a negative test result given that they have a negative outcome.įor the data in Table 1 the PPV of the test using lactate level above 1.5 mmol/l as an indicator of mortality is 81/672 = 0.12, and the NPV is 674/719 = 0.94. This is in contrast to sensitivity, which is the probability that a patient has a positive test result given that they have a positive outcome. The positive predictive value (PPV) of a test is the probability that a patient has a positive outcome given that they have a positive test result. The decision to make use of a diagnostic test will also depend on whether a treatment exists should the result of the test be positive, the cost of such a treatment, and whether the treatment is detrimental in cases in which the result is a false positive. However, a test with high sensitivity may have low specificity and vice versa. A discriminating test would have sensitivity and specificity close to 100%. Generally, both the sensitivity and specificity of a test need to be known in order to assess its usefulness for a diagnosis. The 95% confidence interval for sensitivity is 56–73% and that for specificity is 51–56%. Because both of these measures are simple proportions, their confidence intervals can be calculated as described in Statistics review 8. Therefore, 64% of patients in this sample who died and 53% who survived were correctly identified by this test. The specificity is the proportion of patients for whom the outcome is negative that are correctly identified by the test.įor the data given in Table 1 the sensitivity of the test using lactate level above 1.5 mmol/l as an indicator of mortality is 81/126 = 0.64, and the specificity is 674/1265 = 0.53. The sensitivity of a diagnostic test is the proportion of patients for whom the outcome is positive that are correctly identified by the test.
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