Cardiology Board Review statistics
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Editor-In-Chief: C. Michael Gibson, M.S., M.D. [1]
Bayes Theorem
- The pretest probability of disease affects utility of the test
- Low pretest probability of disease: Negative result virtually excludes disease. Positive test not that helpful, triggers more tests.
- High pretest probability: Negative test not helpful.
- Intermediate Range Probability of Disease: Tests are most useful in identifying patients with the disease.
- Likelihood ratio (LR) = sensitivity / 1 - specificity
- Pretest probability X LR = usefulness of the test
Incidence
New cases per year or over a period of time
Prevalence
How many people have the disease at a snapshot in time
False Positive
Don't have the disease, but test indicates they do
False Negative
Have the disease, but tests says they don't
Positive Predictive Value
Proportion of people with disease who have a positive test. If you have a low prevalence of disease, this may be low. If you have a prevalent disease, this will often be positive.
Negative Predictive Value
Proportion of people with a negative test don't have the disease. If the prevalence of disease is low, this is helpful (atypical CP and a negative stress test rules out the disease).
Metaanalysis
- NNT, number needed to treat, number need to treat to save one life, or number of lives saved in treating a thousand patients