Tonight’s episode is dropping on Thursday 30 March 2017. I should be flying back to Canberra from Melbourne all things going well.
In my day job, especially on Fridays, I get asked about the reliability and accuracy of diagnostic tests. Part of my work involves serology or immunoassay, the interpretation of results and giving advice to referring medical practitioners on how to interpret the results they have in front of them. I also use this knowledge when writing advice for policy development in my role as an adviser.
Diagnostic tests are never perfect. False positive and false negative results occur. How much of a problem these false results may cause depends on the clinical context in which a test is used. This underlines the importance of the clinical or medical context in laboratory medicine, especially for pathologists whose job is to bridge the gap between the patient and the test tube. One of the most frustrating things for pathologists and medical laboratory scientists is the absence of relevant clinical information on referral forms.
In pathology, truth can be defined as the presence or absence of a disease. The aim of the test is to determine the presence or absence of the disease being investigated. Unlike school tests, which are pass or fail, diagnostic tests are positive, negative, equivocal or indeterminate. In the context of serology, however, we use the terms reactive, nonreactive, equivocal and indeterminate. We may also use the terms detected or not detected in relation to the presence or absence of an antibody or antigen. I mention the passing and failing because I’ve read online some comments from patients who misunderstand the pathology results they’ve been given and use the terms pass and fail. I also emphasise the importance of not referring to positive or negative test results in the context of serology and molecular microbiology. It is far better to use the terms reactive or detected. The terms positive or negative can incorrectly be interpreted to infer the presence or absence of a disease. It’s not uncommon for a reactive serology result to be false as I’ll explain later. It’s also common when doing a panel of tests for similarly related microorganisms or very similar analytes for cross-reactions to occur. I’ve come across patients and sadly medical practitioners who through ignorance (and poor training on the part of the medical practitioners) assume that because more than one test is reactive, the result means the person has more than one infection. This is often a fallacious assumption.
I feel sorry for patients who leave a medical consultation under the impression they have multiple infections caused by multiple species of bacteria that exist in the same genus because their doctor didn’t understand the result and the reporting pathologist didn’t explain the result carefully enough on the result report.
Sensitivity and specificity
Sensitivity and specificity are characteristics of the test, while predictive values depend on the disease prevalence in the population being tested.
Often sensitivity and specificity of a test are inversely related.
Sensitivity = ability of a test to detect a true positive. Sensitivity = True positive/[True positive + False negative]
Specificity = ability of a test to exclude a true negative. Specificity = True negative/[True negative + False positive]
Predictive values are of importance when a positive result does not automatically mean the presence of disease. Unlike sensitivity and specificity, the predictive value varies with the prevalence of the disease within the population. Even with a highly specific test, if the disease is uncommon among those tested, a large proportion of the positive results will be false positives and the positive predictive value will be low.
Positive predictive value = proportion of positive test that is true positives and represents the presence of disease. PPV = True positive/[True positives + False positives]
Negative predictive value = proportion of negative test that is true negatives and represents the absence of disease. NPV = True negative/[True negatives + False negatives]
If the test is applied when the proportion of people who truly have the disease is high then the PPV improves.
Conversely, a very sensitive test (even one which is very specific) will have many false positives if the prevalence of the disease is low.
Sensitivity and specificity are intrinsic attributes of the test being evaluated (given similar patient and specimen characteristics) and are independent of the prevalence of disease in the population being tested.
Positive and negative predictive values are highly dependent on the population prevalence of the disease.
So, what does this mean?
It means no test is perfect. It means the referring medical practitioner and the pathologist need to be aware of contextual factors like disease prevalence and other factors that influence pretest probability. Knowing that no test is perfect and that there are other variables that influence a result including interpretation and disease prevalence, every result should be considered very carefully for what it means for the individual patient.
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