The Bayesian approach to disease diagnosis in the emergency department is facilitated by the use of likelihood ratios (LRs) to evaluate diagnostic tests. The use of dichotomous, interval, and joint LRs for single and multiple tests is reviewed, and comparison is made to regression modeling.

The clinical motivation for a single statistic to describe the average change in the odds of disease associated with the use of a particular test or series of tests is described. This new extension of the LR concept is termed the average absolute LR (AALR).

Illustrative examples include the use of elevated electrocardiogram ST segment and troponin to diagnose acute myocardial infarction, and serum d-dimer and computed tomographic angiography to diagnose pulmonary embolism. Finally, a detailed example with original data demonstrating the use of the AALR to compare QRS duration, QRS axis, and the 2 tests combined to diagnose ventricular tachycardia in patients with stable sustained regular wide QRS tachycardia is provided. Application of both tests together to patients with wide QRS complex tachycardia changes the odds of ventricular tachycardia, on average, by a factor of 3.5 (95% confidence interval, 2.4-6.2). Challenges are described, and methods are provided to estimate the 95% confidence interval of the LR and AALR using bootstrapping techniques. The AALR is a test statistic that may be helpful for clinicians and researchers in evaluating and comparing diagnostic testing approaches.