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IMPORTANT CONCEPTS

DIAGNOSIS

When evaluating a new diagnostic test, researchers compare the results of the new test to the diagnotic results produced by the recognized "gold standard" in the area. For example, a new test for diagnosing ectoptic pregnancy would be compared to transvaginal ultrasonography.

	Disease Present	Disease Not Present
Test Positive	900	100
Test Negative	200	800

Positive Predictive Value
The Positive Predictive Value of a test is the number of times the disease was present according to the gold standard as a percentage of the number of times the new test came back positive. In the above example, the Positive Predictive Value would be 900/(900 +100) or 90%.

Negative Predictive Value
The Negative Predictive Value of a test is the number of times the disease is absent according to the gold standard as a percentage of the number of times the new test comes back negative. In the above example, the Negative Predictive Value would be 800/(800+200)) or 80%.

Prevalance
The prevalance of a disease is the percentage of people with a particular disease within a given population. In the above example, the prevalance would be 900/(900+100+200+800) or 45%.

Positive and Negative Predictive Values are highly influenced by the prevalance of a disease. So researchers began to look for qualities that would not be so easily influenced.

Sensitivity
The Sensitivity of a test is the number of times a test came back positive as a percentage of the times the disease was present according to the gold standard. In the above example, the Sensitivity would be (900/900+200) or 81%.

Specificity
The Specificity of a test is the number of times a test comes back negative as a percentage of the times the disease was negative according to the gold standard. In the above example, the Specificity would be (800/800+100) or 88%.

While Sensitivity and Specificity are useful in that they are unaffected by prevalance, they are qualities of the test and not patient-related the way we would like evidence-based practice to be. So researchers came up with another quality that would satisfy both demands of being patient-centered and minimally affected by prevalance.

Pre- and Post-Test Probability
First, it is important to note that no test tells us absolutely whether a disease is present (This is not, strictly speaking. true. A pathologist performing an autopsy knows exactly what disease killed a patient, but by then it's too late). A diagnostic test simply tells us something about the probability that a disease is present. Evidence-based practice encourages the practitioner to assign a value to the probability that a disease is present, based on the practitioners's clinical judgment. This number is likely to be related to the prevalance of the disease in a particular population. As the prevalance of ectoptic pregnancies in men over 50 is 0%, that would be the pre-test probability. The results of a test raise or lower this number to a post-test probability that the condition is present. If the TVU for the 50-year old man comes back positive, the post-test probability is 100% that you need a new sonograph.

Likelihood Ratios
The positive and negative likelihood ratios of a test gauge the test's ability to bring the pre-test probability and post-test probability into allignment.

The likelihood ratio for the positive test measures the test's ability to raise the pre-test probability to a level high enough that a practioner feels comfortable choosing one therapy over another. It is computed with the following equation: Sensitivity/(1-Specificity) [where specificity and sensitivity are expressed as decimals rather than percentages].

The likelihood ratio for the negative test measures the test's ability to lower the pre-test probability to a level low enough that a practitioner feels comfortable in choosing not to apply the therapy for the suspected disease. It is computed with the following equation: (1-Sensitivity)/Specificity [where sensitivity and specificity are expressed as decimals rather than percentages].

The Likelihood Nomagram
The Likelihood Nomagram developed by T.J. Fagan (Fagan TJ. Letter: Nomogram for Bayes theorem. N Engl J Med 293. 257(1975)) is the tool used to make use of likelihood ratios. Find your pre-test probability on the left-hand column. Draw a line through the likelihood ratio of the test as reported in the study and find the post-test probability that the condition is present. As can be seen from the nomagram, a likelihood ratio of 1 is not at all effective. On the whole, a positive likelihood ratio (LR+) of greater than 10 and a negative likelihood ratio (LR-) of less than 0.1 provide the greatest results.

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