special subjects
Understanding Medical Tests and Test Results
Test Characteristics
Some clinical variables have only 2 possible results (eg, alive/dead, pregnant/not pregnant); such variables are termed categorical and dichotomous. Other categorical results may have many discrete values (eg, blood type, Glasgow Coma Scale) and are termed nominal or ordinal. Nominal variables such as blood type have no particular order. Ordinal variables such as the Glasgow Coma Scale have discrete values that are arranged in a particular order. Other clinical variables, including many typical diagnostic tests, are continuous and have an infinite number of possible results (eg, white blood cell count, blood glucose level). Many clinicians select a cutoff point that can cause a continuous variable to be treated as a dichotomous variable (eg, patients with a fasting blood glucose level>126 mg/dL [7.0 mmol/L] are considered to have diabetes). Other continuous diagnostic tests have diagnostic utility when they have multiple cutoff points or when ranges of results have different diagnostic value. When test results can be defined as positive or negative, all possible outcomes can be recorded in a simple 2×2 table (see table Distribution of Hypothetical Test Results ) from which important discriminatory test characteristics, including sensitivity, specificity, positive and negative predictive value, and likelihood ratio (LR), can be calculated (see table Sensitivity and specificity are typically considered characteristics of the test itself, independent of the patient population. Sensitivityis the likelihood that patients with the disease will have a positive test result (true-positive rate) Thus, a test that is positive in 8 of 10 patients with a disease has a sensitivity of 0.8 (also expressed as 80%). Sensitivity represents how well a test detects the disease; a test with low sensitivity does not identify many patients with disease, and a test with high sensitivity is useful to exclude a diagnosis when results are negative. Sensitivity is the complement of the false-negative rate (ie, the false-negative rate plus the sensitivity=100%). Specificityis the likelihood that patients without the disease will have a negative test result in patients without disease (true-negative rate) Thus, a test that is negative in 9 of 10 patients without disease has a specificity of 0.9 (or 90%). Specificity represents how well a test correctly identifies patients with disease because tests with high specificity have a low false-positive rate. A test with low specificity diagnoses many patients without disease as having disease. It is the complement of the false-positive rate. Predictive valuesdescribe test behavior in a given population of patients not knowing if they have the disease. For a given test, predictive values vary depending on the prevalence of disease in the patient population tested. Positive predictive value (PPV)is the proportion of patients with a positive test that actually have disease Thus, if 9 of 10 positive test results are correct (true positive), the PPV is 90%. Because all positive test results have some number of true positives and some false positives, the PPV describes how likely it is that a positive test result in a given patient population represents a true positive. Negative predictive value (NPV)is the proportion of patients with a negative test result that are actually disease free Thus, if 8 of 10 negative test results are correct (true negative), the NPV is 80%. Because not all negative test results are true negatives, some patients with a negative test result actually have disease. The NPV describes how likely it is that a negative test result in a given patient population represents a true negative. Unlike sensitivity and specificity, which do not apply to specific patient probabilities, the LR allows clinicians to interpret test results in a specific patient provided there is a known (albeit often estimated) pre-test probability of disease. The LR describes the change in pre-test probability of disease when the test result is known and answers the question How much has the post-test probability changed from the pre-test probability now that the test result is known? Many clinical tests are dichotomous; they are either above the cutoff point (positive) or below the cutoff point (negative) and there are only 2 possible results. Other tests give results that are continuous or occur over a range where multiple cutoff points are selected. The actual post-test probability depends on the magnitude of the LR (which depends on test operating characteristics) and the pre-test probability estimation of disease. When the test being done is dichotomous and the result is either positive or negative, the sensitivity and specificity can be used to calculate positive LR (LR+) or negative LR (LR-). LR+:The ratio of the likelihood of a positive test result occurring in patients with disease (true positive) to the likelihood of a positive test result in patients without disease (false positive) LR-:The ratio of the likelihood of a negative test result in patients with disease (false negative) to the likelihood of a negative test result in patients without disease (true negative) When the result is continuous or has multiple cutoff points, the ROC curve, not sensitivity and specificity, is used to calculate an LR that is no longer described as LR+or LR-. Because the LR is a ratio of mutually exclusive events rather than a proportion of a total, it represents Probability and odds rather than probability. For a given test, the LR is different for positive and negative results. For example, given a positive test result, an LR of 2.0 indicates the odds are 2:1 (true positives:false positives) that a positive test result represents a patient with disease. Of 3 positive tests, 2 would occur in patients with disease (true positive) and 1 would occur in a patient without disease (false positive). Because true positives and false positives are components of sensitivity and specificity calculations, the LR+can also be calculated as sensitivity/(1−specificity). The greater the LR+, the more information a positive test result provides; a positive result on a test with an LR+>10 is considered strong evidence in favor of a diagnosis. In other words, the pre-test probability estimation moves strongly toward 100% when a positive test has a high LR+. For a negative test result, an LR-of 0.25 indicates that the odds are 1:4 (false negatives:true negatives) that a negative test result represents a patient with disease. Of 5 negative test results, 1 would occur in a patient with disease (false negative) and 4 would occur in patients without disease (true negative). The LR-can also be calculated as (1−sensitivity)/specificity. The smaller the LR-, the more information a negative test result provides; a negative result on a test with an LR<0.1 is considered strong evidence against a diagnosis. In other words, the pre-test probability estimation moves strongly toward 0% probability when a negative test has a low LR-. Test results with LRs of 1.0 carry no information and cannot affect the post-test probability of disease. LRs are convenient for comparing tests and are also used in Bayes Theorem to interpret test results. Just as sensitivity and specificity change as cutoff points change, so do LRs. As a hypothetical example, a high cutoff for white blood cell (WBC) count (eg, 20,000/ 