Additive value of semiautomated quantification of coronary artery disease using cardiac computed tomographic angiography to predict future acute coronary syndrome

Objectives The purpose of this study was to investigate whether the use of a semiautomated plaque quantification algorithm (reporting volumetric and geometric plaque properties) provides additional prognostic value for the development of acute coronary syndromes (ACS) as compared with conventional reading from cardiac computed tomography angiography (CCTA). Background CCTA enables the visualization of coronary plaque characteristics, of which some have been shown to predict ACS. Methods A total of 1,650 patients underwent 64-slice CCTA and were followed up for ACS for a mean 26 ± 10 months. In 25 patients who had ACS and 101 random controls (selected from 993 patients with coronary artery disease but without coronary event), coronary artery disease was evaluated using conventional reading (calcium score, luminal stenosis, morphology), and then independently quantified using semiautomated software (plaque volume, burden area [plaque area divided by vessel area times 100%], noncalcified percentage, attenuation, remodeling). Clinical risk profile was calculated with Framingham risk score (FRS). Results There were no significant differences in conventional reading parameters between controls and patients who had ACS. Semiautomated plaque quantification showed that compared to controls, ACS patients had higher total plaque volume (median: 94 mm3vs. 29 mm3) and total noncalcified volume (28 mm3vs. 4 mm3, p ≤ 0.001 for both). In addition, per-plaque maximal volume (median: 56 mm3vs. 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 model predicting for ACS incorporating FRS and conventional CCTA reading had an area under the curve of 0.64; a second model also incorporating semiautomated plaque quantification had an area under the curve of 0.79 (p < 0.05). Conclusions The semiautomated plaque quantification algorithm 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.

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