The Application of Conditional Probability to Harmonize Nuclear Cardiology Test Results

Both exercise single photon emission computed tomography (SPECT) imaging and myocardial perfusion imaging with positron emission tomography produce multiple outcome variables. These include the stress electrocardiogram (ECG), visual perfusion assessment and quantitative myocardial blood flow. Bayes' analysis using conditional probability allows the distillation of multiple test results into a single probability of disease for individual patients. This paper examines the application of conditional probability analysis to two noninvasive modalities that generate multiple outcome results: exercise ECG combined with SPECT imaging and vasodilator RB-82 positron emission tomography perfusion imaging combined with quantitative measure of absolute myocardial blood flow. In this manner, a single probability of disease incorporating all the available data is generated for an individual patient.