**1. Introduction**

Cardiovascular diseases (CVDs) remain a leading cause of death across the world [1]. Ischemic heart disease (IHD) is one of the largest contributors to these deaths both globally and in the United States of America [2] and contributes to years of productivity loss due to complications from disease sequelae. These include non-fatal myocardial infarction, stable angina pectoris and symptomatic ischemic cardiomyopathy. Although the number of deaths resulting from fatal MI has been decreasing, the number of quality years lost from IHD complications has been increasing [3]. The decrease in mortality is largely due to interventions for the management of acute coronary syndromes (ACS) and early percutaneous coronary intervention (PCI) for ST-segment elevation MI [4, 5].

There are a variety of scoring systems/tools which have been used to predict (with varying degrees of success), which patients are likely to have obstructive coronary artery disease as a cause of their chest pain [6].

The most frequently used clinical decision making tool to decide the likelihood of CAD on the basis of patient characteristics, is the Diamond-Forrester classification [7]. It is derived from the application of Bayesian principles [8] and has formed the backbone of many of the guideline statements for the management of patients with suspected stable ischemic heart disease [9, 10].

Due to the limitations of the history and physical examination in determining the likelihood of disease, clinicians have utilized various testing modalities

#### *Differential Diagnosis of Chest Pain*

to further increase certainty. Evaluation of chest pain has been no different. The number of available testing strategies has increased over the last few decades, and the technologies underlying these tests are constantly being refined. Despite the growing number of options, many clinicians remain unsure how to utilize these modalities [5, 11]. The increasing utilization of these tests often leads to: (i) further investigation, (ii) physician and patient uncertainty/anxiety [12], (iii) harm and (iv) increase in health care costs.

We aim to cover the following in our chapter review:


Bayes' theorem has been previously applied in many clinical scenarios, including the evaluation of chest pain [13–16]. This chapter will neither be burdened with complex statistical formulas nor difficult to follow calculations. Rather, it will provide a practical approach to decision making and dealing with diagnostic uncertainty in patients with stable chest pain. Though many of the concepts expressed here are not original to the authors, we hope that this review will provide a comprehensive approach to testing—considering patient outcomes and resource utilization. The almost three century old principles of the Bayesian approach to decision making are just as relevant today with the growing technological advancements in the new era of precision medicine.
