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Deseive, S.; Straub, R.; Kupke, M.; Broersen, A.; Kitslaar, P. H.; Massberg, S.; Hadamitzky, M.; Hausleiter, J. (2018): Quantification of coronary low-attenuation plaque volume for long-term prediction of cardiac events and reclassification of patients. In: Journal of Cardiovascular Computed Tomography, Vol. 12, No. 2: pp. 118-124
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Background: To investigate the incremental prognostic value of low-attenuation plaque volume (LAPV) from coronary CT angiography datasets. Methods: Quantification of LAPV was performed using dedicated software equipped with an adaptive plaque tissue algorithm in 1577 patients with suspected CAD. A combination of death and acute coronary syndrome was defined as primary endpoint. To assess the incremental prognostic value of LAPV, parameters were added to a baseline model including clinical risk and obstructive coronary artery disease (CAD), a baseline model including clinical risk and calcium scoring (CACS) and a baseline model including clinical risk and segment involvement score (SIS). Results: Patients were followed for 5.5 years either by telephone contact, mail or clinical visits. The primary endpoint occurred in 30 patients. Quantified LAPV provided incremental prognostic information beyond clinical risk and obstructive CAD (c-index 0.701 vs. 0.767, p<.001), clinical risk and CACS (c-index 0.722 vs. 0.771, p<.01) and clinical risk and SIS (c-index 0.735 vs. 0.771, p<.01. A combined approach using quantified LAPV and clinical risk significantly improved the stratification of patients into different risk categories compared to clinical risk alone (categorical net reclassification index 0.69 with 95% CI 0.27 and 0.96, p<.001). The combined approach classified 846 (53.6%) patients as low risk (annual event rate 0.04%), 439 (27.8%) patients as intermediate risk (annual event rate 0.5%) and 292 (18.5%) patients as high risk (annual event rate 0.99%). Conclusion: Quantification of LAPV provides incremental prognostic information beyond established CT risk patterns and permits improved stratification of patients into different risk categories.