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Elsevier, Journal of the American College of Cardiology, 22(61), p. 2296-2305, 2013

DOI: 10.1016/j.jacc.2013.02.065

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Additive Value of Semiautomated Quantification of Coronary Artery Disease Using Cardiac Computed Tomographic Angiography to Predict Future Acute Coronary Syndrome

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This paper is available in a repository.

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Abstract

OBJECTIVES: To investigate whether the use of a semi-automated plaque quantification algorithm (reporting volumetric and geometric plaque properties) provides additional prognostic value for the development of acute coronary syndrome (ACS) as compared with conventional reading from cardiac computed tomographic 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 developed ACS and 101 random controls (selected from 993 patients with CAD, but without coronary event), coronary artery disease was evaluated using conventional reading (calcium score, luminal stenosis, morphology), and then independently quantified using semi-automated software (plaque volume, burden [plaque area/vessel area * 100%], area, non-calcified 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 developed ACS. Semi-automated plaque quantification showed that compared to controls, ACS patients had higher total plaque volume (median 94 vs. 29 mm(3)) and total non-calcified volume (28 vs. 4 mm(3), p≤0.001 for both). In addition, per plaque maximal volume (median 56 vs. 24 mm(3)), non-calcified percentage (62 vs. 26%) and plaque burden (57 vs. 36%) in ACS patients were significantly higher (p<0.01 for all An receiver operating characteristic (ROC)-model predicting for ACS incorporating FRS and conventional CCTA-reading had an area under the curve (AUC) of 0.64, a second model also incorporating semi-automated plaque quantification had an AUC of 0.79, p<0.05. CONCLUSION: The semi-automated 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.