The Role of Coronary Computed Tomography Angiography in the Diagnosis, Risk Stratification, and Management of Patients with Diabetes and Chest Pain

Abstract

Coronary artery disease (CAD) affects over 200 million individuals globally, accounting for approximately 9 million deaths annually. Patients living with diabetes mellitus exhibit an up to fourfold increased risk of developing CAD compared to individuals without diabetes. Furthermore, CAD is responsible for 40 to 80 percent of the observed mortality rates among patients with type 2 diabetes. Patients with diabetes typically present with non-specific clinical complaints in the setting of myocardial ischemia, and as such, it is critical to select appropriate diagnostic tests to identify those at risk for major adverse cardiac events (MACEs) and for determining optimal management strategies. Studies indicate that patients with diabetes often exhibit more advanced atherosclerosis, a higher calcified plaque burden, and smaller epicardial vessels. The diagnostic performance of coronary computed tomographic angiography (CCTA) in identifying significant stenosis is well-established, and as such, CCTA has been incorporated into current clinical guidelines. However, the predictive accuracy of obstructive CAD in patients with diabetes has been less extensively characterized. CCTA provides detailed insights into coronary anatomy, plaque burden, epicardial vessel stenosis, high-risk plaque features, and other features associated with a higher incidence of MACEs. Recent evidence supports the efficacy of CCTA in diagnosing CAD and improving patient outcomes, leading to its recommendation as a primary diagnostic tool for stable angina and risk stratification. However, its specific benefits in patients with diabetes require further elucidation. This review examines several key aspects of the utility of CCTA in patients with diabetes: (i) the diagnostic accuracy of CCTA in detecting obstructive CAD, (ii) the effect of CCTA as a first-line test for individualized risk stratification for cardiovascular outcomes, (iii) its role in guiding therapeutic management, and (iv) future perspectives in risk stratification and the role of artificial intelligence.

Keywords: coronary artery disease; coronary computed tomography angiography; diabetes mellitus.

Fig. 1.

Management of patients with diabetes mellitus and chest pain using CCTA as the initial diagnostic test. DM, diabetes mellitus; CAD, coronary artery disease; LM, left main; LAD, left anterior descending artery; SGLT-2i, sodium–glucose transport protein 2 inhibitor; GLP1-RA, glucagon-like peptide-1 receptor agonist; PCSK9 inhibitor, proprotein convertase subtilisin/kexin type 9 inhibitor; CCTA, coronary computed tomography angiography; CABG, coronary artery bypass grafting; PCI, percutaneous coronary intervention. Figure created using BioRender.com.

Fig. 2.

CCTA biomarkers for personalized risk assessment in patients with diabetes mellitus. MACE, major adverse cardiovascular event; CCTA, coronary computed tomography angiography. Figure created using BioRender.com.

Fig. 3.

Artificial intelligence applications in CCTA analysis. MACE, major adverse cardiovascular event; CCTA, coronary computed tomography angiography; CAD-RADS-score, coronary artery disease-reporting and data system score. Figure created using BioRender.com.