Analysis of Probability as an Aid in the Clinical Diagnosis of Coronary-Artery Disease

TLDR

The diagnosis of coronary‑artery disease has become increasingly complex, requiring integration of imperfect test results into a probability assessment. The study reviewed literature to estimate pretest likelihood of disease by age, sex, and symptoms, and the sensitivity and specificity of four diagnostic tests. Using Bayes’ theorem, test results were analyzed to compute post‑test probabilities. This probabilistic approach pools physician experience and clinical descriptors to produce reproducible, meaningful probabilities of angiographic coronary‑artery disease, aiding but not replacing clinical judgment and informing cost‑effectiveness decisions.

Abstract

The diagnosis of coronary-artery disease has become increasingly complex. Many different results, obtained from tests with substantial imperfections, must be integrated into a diagnostic conclusion about the probability of disease in a given patient. To approach this problem in a practical manner, we reviewed the literature to estimate the pretest likelihood of disease (defined by age, sex and symptoms) and the sensitivity and specificity of four diagnostic tests: stress electrocardiography, cardiokymography, thallium scintigraphy and cardiac fluoroscopy. With this information, test results can be analyzed by use of Bayes' theorem of conditional probability. This approach has several advantages. It pools the diagnostic experience of many physicians ans integrates fundamental pretest clinical descriptors with many varying test results to summarize reproducibly and meaningfully the probability of angiographic coronary-artery disease. This approach also aids, but does not replace, the physician's judgment and may assit in decisions on cost effectiveness of tests.