Limitations of Chest Pain Categorization Models to Predict Coronary Artery Disease

Abstract

We aimed to evaluate how chest pain categorization, currently used to assess the pretest probability of coronary artery disease (CAD), predicts the actual presence of CAD in a population of patients with stable symptoms. We studied 475 consecutive patients enrolled in the Evaluation of Integrated Cardiac Imaging for the Detection and Characterization of Ischemic Heart Disease study based on possible symptoms of CAD. Chest pain or discomfort was categorized as typical angina, atypical angina, or as nonanginal according to the guidelines. Exertional dyspnea and fatigue suspected to be angina equivalents were classified as atypical angina. Patients with a probability of CAD <20 or >90% based on age, gender, and symptoms were excluded. The end points of this substudy were significant CAD (defined by invasive coronary angiography as >50% reduction in lumen diameter in the left main stem or >70% stenosis in a major coronary vessel or 30% to 70% stenosis with fractional flow reserve ≤0.8), inducible myocardial ischemia at noninvasive stress imaging, and their association. Patients' symptoms had limited ability to predict the presence of significant CAD, global chi-square being 5.0. The inclusion of age increased global chi-square to 18.7 and gender increased it further to 51.1. Using inducible myocardial ischemia or the association of CAD with inducible ischemia as end points, the ability to predict these end points was again better for patient demographics than for patient symptoms. Thus, the ability of current models based on symptoms, age, and gender to predict the presence of CAD is mainly based on patient demographics as opposed to symptoms.