SR-AHEC residents: Evidence-Based Medicine

Using medical evidence to effectively guide medical practice is an important skill for all physicians to learn. The purpose of this article is to understand how to ask and evaluate questions of diagnosis, and then apply this knowledge to the new diagnostic test of CT colonography to demonstrate its applicability. Sackett and colleagues have developed a step-wise approach to answering questions of diagnosis: Step1: Define a clinical question and its four components: Patient, intervention, comparison and outcome. Step 2: Find the evidence that will help answer the question. PubMed Clinical Queries is an efficient database to accomplish this step. Step 3: Assess whether this evidence is valid and important. A quick review of the methods and results section will help to answer these two questions. Step 4: Apply the evidence to the patient. This step includes: assessing whether the test can be used; determining if it will help the patient; finding whether the study patients are similar to the patient in question; determining a pretest probability; and deciding if the test will change one's management of the patient. A relatively new diagnostic test, CT colonography, is explored as a scenario in which the steps presented by Sackett et al.1 can be helpful. A patient who is interested in completing a CT colonography instead of a colonoscopy is the basis of the discussion. Because a CT colonography does not detect polyps of less than 10 mm accurately, many patient are not likely to prefer this test over a colonoscopy. Evidence-based medicine is an effective strategy for finding, evaluating, and critically appraising diagnostic tests, treatment and application. This skill will help physicians interpret and explain the medical information patients read or hear about.

The use of laboratory biomarkers in clinical practice is rapidly increasing. Laboratory tests are, however, rarely evaluated adequately before implementation, and the utility of many tests is essentially unclear. An important reason for this knowledge gap is that a comprehensive and generally accepted methodological framework supporting evaluation studies is essentially lacking. Researchers, clinicians, and decision-makers are often not aware of the methodological tools available and face problems with the appraisal of a test's utility. With the present article, I aim to summarize current concepts and methodological tools and propose a framework for a phased approach that could be used in future evaluation projects. Future research will refine this suggested framework by identifying problems in current evaluation projects, specifying methodological criteria for all phases, as well as developing advanced methodological tools.

The usefulness of diagnostic tests, that is their ability to detect a person with disease or exclude a person without disease, is usually described by terms such as sensitivity, specificity, positive predictive value and negative predictive value. In this article, the first of the series, a simple, practical explanation of these concepts is provided and their use and misuse discussed. It is explained that while sensitivity and specificity are important measures of the diagnostic accuracy of a test, they are of no practical use when it comes to helping the clinician estimate the probability of disease in individual patients. Predictive values may be used to estimate probability of disease but both positive predictive value and negative predictive value vary according to disease prevalence. It would therefore be wrong for predictive values determined for one population to be applied to another population with a different prevalence of disease.
Conclusion: Sensitivity and specificity are important measures of the diagnostic accuracy of a test but cannot be used to estimate the probability of disease in an individual patient. Positive and negative predictive values provide estimates of probability of disease but both parameters vary according to disease prevalence.

The sensitivity and specificity of a test cannot be used to estimate probability of disease in individual patients. They can, however, be combined into a single measure called the likelihood ratio which is, clinically, more useful than sensitivity or specificity. Likelihood ratios provide a summary of how many times more (or less) likely patients with a disease are to have a particular result than patients without the disease. Using the principles of the Bayes theorem, likelihood ratios can be used in conjunction with pre-test probability of disease to estimate an individual's post-test probability of disease, that is his or her chance of having disease once the result of a test is known. The Fagan's nomogram is a graphical tool which, in routine clinical practice, allows one to combine the likelihood ratio of a test with a patient's pre-test probability of disease to estimate post-test probability.
Conclusion: Likelihood ratios summarize information about a diagnostic test by combining sensitivity and specificity. The Fagan's nomogram is a useful and convenient graphical tool that allows likelihood ratios to be used in conjunction with a patient's pre-test probability of disease to estimate the post-test probability of disease.