Optimal use of literature knowledge to improve the Bayesian diagnosis of coronary artery disease

Bayes' theorem with the independence assumption is applied to a test sample of 141 subjects, using two sets of test sensitivities and specificities. The first set is derived by averaging over literature reports on the accuracy of the exercise electrocardiogram, exercise thallium scintigraphy, and carciac fluoroscopy. The second set of indices is derived by applying multivariate regression to the technical, population, and methodologic attributes obtained from the same literature by the use of meta-analysis. The meta-analytically corrected sensitivities and specificities resulted in significant improvement in the discriminatory power of the Bayes model. (Area under ROC curve increased, p = less than 0.01). However, the corrected model was not as accurate as a data-derived logistic regression model of the same test variables. Meta-analysis may be useful for modest improvement in the accuracy of literature-derived Bayesian models for predicting disease probabilities.