Predictive value

Diagnostic tests must supplement rather than be used as a substitute for clinical skills.

Careful clinical assessment followed by discretionary testing is cost-effective, efficient and leads to improved patient outcomes.

A major factor in improving the positive predictive value of a test is to limit the use of the test to those patients who are likely to have the disease in question.

The proportion of true positives to the total number of positives in the population is the positive predictive value of the test.

This represents the diagnostic value of a positive result for the test.

The relationship between the positive predictive value and the prevalence of the disease in the community is represented on the graph below.

Thus the higher the disease prevalence the greater the probability that a positive result will be 'correct'.

For example, if a test with high sensitivity and specificity is used to detect a disease with a prevalence of 1%, the positive predictive value of the test is 16%. That is there is only a one in six chance that the test result is correct.

The predictive value of a test can be improved by selecting a population in which the disease has a higher prevalence. For example hepatitis C virus testing in 'at risk' groups.

The positive predictive value of a test can be signficantly improved by limiting the test to those patients who, on clinical assessment, are likely to have the disease in question. That is to practise discretionary testing.

The negative predictive value of a test, with a sensitivity and specificity of 95%, used for a disease with a 1% prevalence is 99%. That is there is only a 1% chance of a false negative result. Thus the test, applied to the general population, can efficiently exclude the diagnosis but is extremely inefficient in confirming the diagnosis.

As previously noted its efficiency as a diagnostic test can be markedly increased by using it in a discretionary fashion in high risk groups or in patients with clinical features suggesting the disease in question.

This improved diagnostic efficiency does not significantly reduce its negative predictive value.

It should be noted that few tests achieve the specificity and sensitivity of the test used in this example.

Pathology tests guide clinical decision making and the clinician should have some understanding of the factors which influence the reliability of a test for such decisions to be valid.

The clinician has an important part to play in the avoidance, or control, of many of the preanalytical variables.

The clinician also needs to have an understanding of the sensitivity and specificity of tests and of their positive predictive value.

Profile testing, or 'screening', is an expensive process which, even with tests of high sensitivity and specificity, has a limited positive predictive value. Highly accurate test results may be entirely meaningless or misleading when used in this fashion.

False positive results lead to unnecessary and expensive follow-up testing and patient anxiety.

False negative results place the patient at risk.

Diagnostic tests must supplement, rather than be used as a substitute for, clinical skills.

Careful clinical assessment followed by discretionary testing is cost-effective, efficient and leads to improved patient outcomes.

Last Updated on Tuesday, 02 February 2010 15:57