Abstract 20945: Improved Risk Prediction Using a Comprehensive Cta Risk Score That Incorporates All Aspects of Coronary Atherosclerosis Compared With Standard Cta Classification

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Background: Risk assessment with coronary CTA is traditionally done with maximal stenosis, but this approach does not use the prognostic value of plaque extent, location and composition. This study assessed whether a comprehensive CTA risk model incorporating all aspects of coronary atherosclerosis provides better risk stratification than the commonly used CTA classification of no CAD, non-obstructive and obstructive CAD.

Methods: Among 2255 patients with suspected or known CAD the predictive value of the CTA risk score (range from 0-42) was compared with the standard CTA classification. Three risk groups were created based on their best discriminatory performance: 0-5; 6-20; >20.

Results: During a mean follow up of 3.6 ± 2.8 years 156 events (death or MI) occurred. Compared with the standard CTA classification, a stronger association with events was observed for the CTA risk score: adjusted HR for 6-20 was: 2.69 (95% CI: 1.55-4.68, P<0.001) and 5.53 (95% CI: 2.82-10.82, P<0.001) for a score > 20 By comparison, adjusted HR for non-obstructive CAD were 1.19 (95% CI: 0.61-2.29, P=0.613) and for obstructive CAD 2.52 (95% CI: 1.33-4.78, P=0.005), Figure 1. Adding the score to a model with clinical variables and the standard CTA classification increased the c-statistic from 0.756 to 0.781 (P<0.001). Furthermore net reclassification analysis confirmed that the novel CTA risk score was able to correctly reclassify a significant proportion of patients compared with the standard CTA classification: categorical NRI 12% (7.4% for events, 4.6% for non-events) using three predicted risk categories 0-5%, 5-15% and >15%

Conclusion: A CTA risk model incorporating all aspects of CAD provides better discrimination and reclassification of events compared with the standard CTA scoring model including normal, non-obstructive and obstructive CAD. The proposed model allows more precise risk estimation which may further guide patient treatment.

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