Relevance of anatomical, plaque, and hemodynamic characteristics of non-obstructive coronary lesions in the prediction of risk for acute coronary syndrome.

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We explored the anatomical, plaque, and hemodynamic characteristics of high-risk non-obstructive coronary lesions that caused acute coronary syndrome (ACS). From the EMERALD study which included ACS patients with available coronary CT angiography (CCTA) before the ACS, non-obstructive lesions (percent diameter stenosis < 50%) were selected. CCTA images were analyzed for lesion characteristics by independent CCTA and computational fluid dynamics core laboratories. The relative importance of each characteristic was assessed by information gain. Of the 132 lesions, 24 were the culprit for ACS. The culprit lesions showed a larger change in FFRCT across the lesion (ΔFFRCT) than non-culprit lesions (0.08 ± 0.07 vs 0.05 ± 0.05, p = 0.012). ΔFFRCT showed the highest information gain (0.051, 95% confidence interval [CI] 0.050-0.052), followed by low-attenuation plaque (0.028, 95% CI 0.027-0.029) and plaque volume (0.023, 95% CI 0.022-0.024). Lesions with higher ΔFFRCT or low-attenuation plaque showed an increased risk of ACS (hazard ratio [HR] 3.25, 95% CI 1.31-8.04, p = 0.010 for ΔFFRCT; HR 2.60, 95% CI 1.36-4.95, p = 0.004 for low-attenuation plaque). The prediction model including ΔFFRCT, low-attenuation plaque and plaque volume showed the highest ability in ACS prediction (AUC 0.725, 95% CI 0.724-0.727). Non-obstructive lesions with higher ΔFFRCT or low-attenuation plaque showed a higher risk of ACS. The integration of anatomical, plaque, and hemodynamic characteristics can improve the noninvasive prediction of ACS risk in non-obstructive lesions. • Change in FFR CT across the lesion (ΔFFR CT ) was the most important predictor of ACS risk in non-obstructive lesions. • Non-obstructive lesions with higher ΔFFR CT or low-attenuation plaque were associated with a higher risk of ACS. • The integration of anatomical, plaque, and hemodynamic characteristics can improve the noninvasive prediction of ACS risk.