The actual gold standard test may be too unpleasant for the patient, too impractical or too expensive to be used widely as a screening test. Assessment of test performance is usually presented in a two by two table 3.
The disease status as assessed through the Gold Standard is conventionally put in the top row and the screening test result in the first column. Sensitivity is defined as the ability of the test to detect all those with disease in the screened population.
This is expressed as the proportion of those with disease correctly identified by a positive screening test result. Specificity is defined as the ability of the test to identify correctly those free of disease in the screened population.
This is expressed as the proportion of those without disease correctly identified by a negative screening test result. The positive predictive value PPV describes the probability of having the disease given a positive screening test result in the screened population. This is expressed as the proportion of those with disease among all screening test positives.
The negative predictive value NPV describes the probability of not having the disease given a negative screening test result in the screened population. This is expressed as the proportion of those without disease among all screening test negatives.
Sensitivity and specificity are independent of prevalence of disease , i. PPV and NPV give information on how well a screening test will perform in a given population with known prevalence. Knowledge of expected disease prevalence in the target population is necessary when a screening activity is introduced to mitigate the potential harms and costs see ethical, economic, social, legal aspects.
With a sensitivity of Of the million people, 10, would be infected with HIV. Looking at those numbers the test appears very good because it detected 9, out of 10, HIV infected people. But there is another side to the test. Of the 1 million people in this population, , are not infected. In this example, two columns indicate the actual condition of the subjects, diseased or non-diseased. The rows indicate the results of the test, positive or negative.
Cell A contains true positives, subjects with the disease and positive test results. Cell D subjects do not have the disease and the test agrees. A good test will have minimal numbers in cells B and C. Cell B identifies individuals without disease but for whom the test indicates 'disease'. These are false positives. Cell C has the false negatives. Sensitivity and specificity are characteristics of the test.
The population does not affect the results. Therefore, a negative result would mean one of two things. Firstly, that amylase is present but in such small quantities that it is undetectable by the test unlikely because this test picks up small changes. Secondly, that amylase is not present at all more likely. This example works because the disease pancreatitis has a trait amylase that is almost always present and the test looks for that trait.
If the trait is not present, the disease is unlikely to be present and can be ruled out. The specificity of a test is the proportion of people who test negative among all those who actually do not have that disease. A specific test helps rule a disease in when positive e. If a disease UTI has a trait nitrites in urine that is rare in other diseases, a test for that trait can be thought of as being highly specific because the trait is specific to that disease.
However, a positive result would not mean they definitely have a UTI because a highly specific test does not factor in how common the disease is prevalence. Positive predictive value PPV and negative predictive value NPV are directly related to prevalence and allow you to clinically say how likely it is a patient has a specific disease.
The positive predictive value is the probability that following a positive test result , that individual will truly have that specific disease. The negative predictive value is the probability that following a negative test result , that individual will truly not have that specific disease.
For any given test i. Therefore, as prevalence decreases , the NPV increases because there will be more true negatives for every false negative. This is because a false negative would mean that a person actually has the disease, which is unlikely because the disease is rare low prevalence.
The examples given should allow you to see how and why these vary as different factors change. Clinicians are usually confronted with test results that are either positive or negative.
So the question is, what does a positive or negative test result tell you? This is where the predictive value comes in. Let's consider an example. We have a population of individuals: are diseased, are not.
Out of the , correctly tested positive. Of the non-diseased, are correctly tested negative. So, we have true positives and true negatives. Accordingly, we have 80 false positives and 40 false negatives. Figure 1. An example to illustrate predictive value.
Out of individuals, are diseased and are not. So, there are 40 false negatives and 80 false positives. Well, positive predictive value, or PPV, is the percentage of truly diseased people out of those who test positive. Figure 2.
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