Interpreting diagnostic test results (Proceedings) - Veterinary Healthcare
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Interpreting diagnostic test results (Proceedings)


CVC IN KANSAS CITY PROCEEDINGS


The innate sensitivity and specificity of a diagnostic test affects its validity, i.e. how well a test reflects the true disease status of an animal. An assay is considered 'validated' if it consistently produces test results that identify animals as being positive or negative for the presence of a substance (e.g. antibody, antigen, organism) in the sample (e.g. serum) being tested and, by inference, accurately predicts the infection status of the tested animals with a predetermined degree of statistical certainty (confidence). Inferences based on these test results can be made about the infection status of the animals. The process of validating an assay is the responsibility of researchers and diagnosticians. The initial development and optimization of an assay, by a researcher, may require further characterization of the performance of the assay by laboratory diagnosticians before field use. Keep in mind that the specific criteria required for assay validation of an infectious disease are elusive and that the process leading to a validated assay is not standardized. Factors that influence the capacity of the test result to accurately infer the infection status of the host are diagnostic sensitivity (Sn), diagnostic specificity (Sp), and prevalence of the disease in the population targeted by the assay.

Diagnostic Sn and diagnostic Sp of a test are calculated relative to test results obtained from reference animal populations of known infection/exposure status to a specific disease agent. Diagnostic Sn describes the proportion of animals with disease that have a positive test result. Diagnostic Sp describes the proportion of animals free of a disease that have a negative test. The degree to which the reference animals represent all of the host and environmental variables in the population targeted by the assay has a major impact on the accuracy of test result interpretation (accuracy is discussed in greater detail below) and applicability of this test in this population.

The capacity of a positive or negative test result to accurately predict the infection status of the animal is a key objective of assay validation. This capacity is not only dependent upon a highly precise and accurate assay and carefully derived estimates of Sn and Sp, but is also strongly influenced by the prevalence of the infection in the targeted population. Without a current estimate of the disease prevalence in that population, the interpretation of a positive or negative test result will be compromised.

When diagnostic tests are dichotomized into 'positive' or 'negative', a cut-off point is required. Cut-off points have been determined in several ways. Visual inspection of the frequency distributions of the test results for infected and uninfected animals has been used. The frequency distributions for infected and uninfected animals typically indicate an overlapping region of assay results (the perfect test with no overlap, yielding 100% diagnostic Sn and 100% diagnostic Sp, rarely—if ever—exists). The cut-off is placed at the intersection of the two distributions. This method has the advantage of being simple and flexible, and requires no statistical calculations or assumptions about the normality of the two distributions. Another way to determine a cut-off point is to arbitrarily place it two or three standard deviations greater than the mean of the test values of the unaffected individuals. This approach fails to consider the frequency of disease and the distribution of test results in diseased individuals, and ignores the impact of false-positive and false-negative errors. Another approach is one that identifies 95% of individuals with disease as being test-positive. This ignores the distribution of test results in unaffected individuals, the prevalence of the disease, and all consequences except those that are due to false-negative error. Alternatively, the value that minimizes the total number or total cost of misdiagnoses can be selected. The optimum cut-off point also depends on the frequency distribution of the test variable in the healthy and diseased population, which may be complicated. A cut-off point can also be established by a modified receiver-operator characteristics (ROC) analysis.


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