Interobserver Reliability in the Assessment of Coronary Stenoses by Multidetector Computed Tomography

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

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.

Interobserver variability among expert readers quantifying plaque volume and plaque characteristics on coronary CT angiography: a CLARIFY trial sub-study

BACKGROUND

The difference between expert level (L3) reader and artificial intelligence (AI) performance for quantifying coronary plaque and plaque components is unknown.

OBJECTIVE

This study evaluates the interobserver variability among expert readers for quantifying the volume of coronary plaque and plaque components on coronary computed tomographic angiography (CCTA) using an artificial intelligence enabled quantitative CCTA analysis software as a reference (AI-QCT).

METHODS

This study uses CCTA imaging obtained from 232 patients enrolled in the CLARIFY (CT EvaLuation by ARtificial Intelligence For Atherosclerosis, Stenosis and Vascular MorphologY) study. Readers quantified overall plaque volume and the % breakdown of noncalcified plaque (NCP) and calcified plaque (CP) on a per vessel basis. Readers categorized high risk plaque (HRP) based on the presence of low-attenuation-noncalcified plaque (LA-NCP) and positive remodeling (PR; ≥1.10). All CCTAs were analyzed by an FDA-cleared software service that performs AI-driven plaque characterization and quantification (AI-QCT) for comparison to L3 readers. Reader generated analyses were compared among readers and to AI-QCT generated analyses.

RESULTS

When evaluating plaque volume on a per vessel basis, expert readers achieved moderate to high interobserver consistency with an intra-class correlation coefficient of 0.78 for a single reader score and 0.91 for mean scores. There was a moderate trend between readers 1, 2, and 3 and AI with spearman coefficients of 0.70, 0.68 and 0.74, respectively. There was high discordance between readers and AI plaque component analyses. When quantifying %NCP v. %CP, readers 1, 2, and 3 achieved a weighted kappa coefficient of 0.23, 0.34 and 0.24, respectively, compared to AI with a spearman coefficient of 0.38, 0.51, and 0.60, respectively. The intra-class correlation coefficient among readers for plaque composition assessment was 0.68. With respect to HRP, readers 1, 2, and 3 achieved a weighted kappa coefficient of 0.22, 0.26, and 0.17, respectively, and a spearman coefficient of 0.36, 0.35, and 0.44, respectively.

CONCLUSION

Expert readers performed moderately well quantifying total plaque volumes with high consistency. However, there was both significant interobserver variability and high discordance with AI-QCT when quantifying plaque composition.