Can CT Coronary Angiography Predict Future ACS?
Abstract & Commentary
By Andrew J. Boyle, MBBS, PhD, Assistant Professor of Medicine, Interventional Cardiology, University of California, San Francisco. Dr. Boyle reports no financial relationships relevant to this field of study.
Source: Versteylen MO, et al. Additive value of semiautomated quantification of coronary artery disease using cardiac computed tomographic angiography to predict future acute coronary syndrome. J Am Coll Cardiol 2013;61:2296-2305.
Computed tomography (CT) has evolved significantly in recent years, and is now able to accurately assess coronary arteries for the presence of atherosclerosis in carefully selected patients. For example, in patients presenting with acute chest pain to the emergency department, coronary CT angiography (CCTA) has a very high negative predictive value for acute coronary syndromes (ACS). CCTA reports usually contain a coronary calcium score, the degree of luminal stenosis, and a qualitative evaluation of plaque morphology. In addition to these measures, both plaque burden and plaque characteristics that are not routinely reported have been associated with future ACS in some studies, albeit inconsistently. Whether a more systematic approach to assessing plaque volume and geometry leads to better prediction of events has not been demonstrated in ambulant patients with stable coronary artery disease (CAD). Accordingly, Versteylen and colleagues performed a retrospective cohort study of patients with stable chest pain undergoing CCTA at two high-volume centers in the Netherlands. A total of 1650 patients underwent 64-slice CCTA and were followed up for ACS for 26 ± 10 months.
Twenty-five patients subsequently developed ACS. These were compared to 101 random controls (selected from 993 patients with CAD but without coronary event during follow-up), and their coronary arteries were evaluated using conventional CCTA reading (calcium score, luminal stenosis, morphology), and were then independently quantified using semi-automated software to derive plaque volume, burden area [defined as (plaque area)/(vessel area) × 100%], non-calcified percentage, attenuation, and vessel remodeling. Clinical risk profile was calculated with Framingham risk score (FRS). There were no significant differences in conventional CCTA parameters between controls and patients who subsequently developed ACS. However, the semiautomated plaque quantification showed that compared to controls, ACS patients had higher total plaque volume (94 mm3 vs 29 mm3) and non-calcified plaque volume (28 mm3 vs 4 mm3, P ≤ 0.001 for both). In addition, per-plaque maximal volume (56 mm3 vs 24 mm3), noncalcified percentage (62% vs 26%), and plaque burden (57% vs 36%) in ACS patients were significantly higher (P < 0.01 for all). A receiver-operating characteristic (ROC) model predicting for ACS incorporating FRS plus conventional CCTA reading had an area under the curve of 0.64; when the ROC also incorporated semiautomated plaque quantification, the area under the curve improved to 0.79 (P < 0.05). The authors conclude that semi-automated plaque quantification identified several parameters predictive for ACS and provided incremental prognostic value over clinical risk profile and conventional CT reading. The application of this tool may improve risk stratification in patients undergoing CCTA.
This study adds to the growing body of literature that CCTA not only can non-invasively define coronary anatomy, but also has powerful predictive ability for future ACS events. Several studies have shown the utility of CCTA in patients with acute chest pain, and this study extends these findings into patients with stable CAD. It is perhaps intuitive that higher plaque burden is predictive of future plaque rupture events. The early identification of such patients may allow intensification of lipid-lowering and antiplatelet medications in order to prevent such events. An important limitation to this study is the retrospective nature of its observations. The information gleaned from this study should be hypothesis-generating and will not alone be enough to change practice. However, this study sets the stage for future clinical trials using CCTA to guide therapy in patients with stable CAD. As we search for more personalized and more cost-effective medicine, we look for novel strategies to improve risk stratification and early disease identification to more accurately direct therapy. The use of these novel CCTA parameters may help predict future events, but how this should alter management remains to be tested in future clinical trials.