title: Application of probability analysis in the diagnosis of coronary artery disease.
abstract: The accuracy and applicability of probability analysis to the diagnosis of coronary artery disease is still an open question. Although earlier criticisms are well taken, much of the resistance to the application of probability analysis is based on tradition, rather than logic. Probabilistic algorithms, like any new technology, must be researched and developed and then withstand the test of time. They should not be dismissed simply because they are not traditional. On the other hand, probability analysis in the diagnosis of coronary artery disease must not be accepted just because it is attractive or because it appears to simplify clinical decisions. Application of probabilistic approaches should depend on their accuracy. There is evidence that results of tests and clinical data are not statistically independent. There is also evidence that sensitivities and specificities derived from pooled literature cannot be appropriately applied to just any patient in a particular institution. This is due to variability in the population of patients, a lack of standardization of testing methods, and methodologic problems in reporting results of sensitivities and specificities. In a large institution, where probabilistic formulae can be derived with some degree of confidence, probability analysis has an application today. Discriminant functions will be more accurate than Bayesian formulas, but whatever method one chooses, one must be certain that the parameters used are appropriate. Where the institution is not large enough to generate such a data base, there is presently no accurate approach to the estimation of the probability of coronary disease.
authors.affiliation: Veterans Administration Medical Center, Long Beach, Calif.
authors.forename: R
authors.initials: R
authors.lastname: Detrano
authors.forename: G
authors.initials: G
authors.lastname: Marcondos
authors.forename: V F
authors.initials: VF
authors.lastname: Froelicher
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D003327:Coronary Disease / Q000175:diagnosis*; D006801:Humans; D007231:Infant, Newborn; D011336:Probability