The determination of the post-test likelihood for coronary disease using Bayes Theorem.
This report describes a method of using Bayes Theorem for determining the post-test likelihood for coronary disease from the exercise test. The pre-test likelihood is determined from the age, sex, and type of chest pain. Discriminant analysis of the amount of S-T depression, maximum pressure rate product and sex of the patient is then used to determine the sensitivity and specificity of the patient's exercise test for coronary artery disease. The post-test likelihood for coronary artery disease can then be calculated using Bayes Theorem. The discriminant function was estimated from a training set of 174 patients. This was then applied to 113 new patients who had both exercise tests and coronary arteriograms. In this new set, 47 patients had a post-test likelihood of 90% or greater for coronary disease. Only one of these patients had normal coronary arteriograms, a predictive accuracy of 98%. Of the 25 patients with a post-test likelihood of 10% or less for coronary disease, four had multivessel disease and four had single vessel disease. The predictive accuracy for the absence of coronary disease was 68%. The predictive accuracy for the exclusion of multivessel disease was 84%. Eight of 10 patients with left main disease had a post-test likelihood for coronary disease of greater than 90%.
D000367:Age Factors; D001499:Bayes Theorem; D017023:Coronary Angiography; D003327:Coronary Disease / Q000175:diagnosis*; D005080:Exercise Test; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D010146:Pain / Q000503:physiopathology; D011336:Probability; D012189:Retrospective Studies; D012737:Sex Factors; D013909:Thorax / Q000503:physiopathology