KEY CONCEPTS

KEY CONCEPTS

• Selecting the appropriate diagnostic procedure depends, in part, on the clinician's index of suspicion.

• The threshold model for testing contains two decision points: when the index of suspicion is high enough to order a diagnostic procedure, and when the index of suspicion is so high that results from a procedure will not influence subsequent actions.

• Two components of evaluating diagnostic procedures are sensitivity and specificity.

• Sensitivity is a procedure's ability to detect a disease if one is present.

• Specificity is a procedure's ability to give a negative result if no disease is present.

• Two errors are possible: false-positive results occur when the procedure is positive but no disease is present; false-negative results occur when the procedure is negative but a disease is present.

• Sensitivity and specificity must be combined with the clinician's index of suspicion to properly interpret a procedure.

• The 2 × 2 table method provides a simple way to use sensitivity and specificity to determine how to interpret the diagnostic procedure after it is done.

• After sensitivity and specificity are applied to the clinician's index of suspicion, the probability of a disease based on a positive test and the probability of no disease with a negative test can be found. They are the predictive values of a positive and negative test, respectively.

• A likelihood ratio is the ratio of true-positives to false-positives; it is used with the prior odds of a disease (instead of the prior probability) to determine the odds after the test is done.

• A decision tree may be used to find predictive values.

• Bayes’ theorem gives the probability of one outcome, given that another outcome has occurred. It is another way to calculate predictive values.

• A sensitive test is best to rule out a disease; a specific test is used to rule in a disease.

• ROC (receiver operating characteristic) curves are used for diagnostic procedures that give a numerical result, rather than simply being positive and negative.

• Decision analysis, often using decision trees, is an optimal way to model approaches to diagnosis or management.

• Outcomes for the decision analysis may be costs, quality-of-life adjusted survival, or subjective utilities measuring how the patient values different outcomes.

• The optimal decision from a decision tree may be analyzed to learn how sensitive the decision is to various assumptions regarding probabilities, costs, and so on.

• Decision analysis can be used to compare two or more alternative approaches to diagnosis or management (or both).

• Decision analysis can be used to compare the timing for diagnostic testing.

• Journal articles should not publish predictive values without reminding readers that these values depend on the prevalence or index of suspicion.

PRESENTING PROBLEMS

Presenting Problem 1

A 57-year-old man presents with a history of low back pain. The pain is aching in quality, persists at rest, and is made worse by bending and lifting. The pain has been ...

Pop-up div Successfully Displayed

This div only appears when the trigger link is hovered over. Otherwise it is hidden from view.