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

OBJECTIVES: We explored the anatomical, plaque, and hemodynamic characteristics of high-risk non-obstructive coronary lesions that caused acute coronary syndrome (ACS). METHODS: 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. RESULTS: 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). CONCLUSION: 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. KEY POINTS: • 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.

Identification of High-Risk Plaques Destined to Cause Acute Coronary Syndrome Using Coronary Computed Tomographic Angiography and Computational Fluid Dynamics.

OBJECTIVES: The authors investigated the utility of noninvasive hemodynamic assessment in the identification of high-risk plaques that caused subsequent acute coronary syndrome (ACS). BACKGROUND: ACS is a critical event that impacts the prognosis of patients with coronary artery disease. However, the role of hemodynamic factors in the development of ACS is not well-known. METHODS: Seventy-two patients with clearly documented ACS and available coronary computed tomographic angiography (CTA) acquired between 1 month and 2 years before the development of ACS were included. In 66 culprit and 150 nonculprit lesions as a case-control design, the presence of adverse plaque characteristics (APC) was assessed and hemodynamic parameters (fractional flow reserve derived by coronary computed tomographic angiography [FFRCT], change in FFRCT across the lesion [△FFRCT], wall shear stress [WSS], and axial plaque stress) were analyzed using computational fluid dynamics. The best cut-off values for FFRCT, △FFRCT, WSS, and axial plaque stress were used to define the presence of adverse hemodynamic characteristics (AHC). The incremental discriminant and reclassification abilities for ACS prediction were compared among 3 models (model 1: percent diameter stenosis [%DS] and lesion length, model 2: model 1 + APC, and model 3: model 2 + AHC). RESULTS: The culprit lesions showed higher %DS (55.5 ± 15.4% vs. 43.1 ± 15.0%; p < 0.001) and higher prevalence of APC (80.3% vs. 42.0%; p < 0.001) than nonculprit lesions. Regarding hemodynamic parameters, culprit lesions showed lower FFRCT and higher △FFRCT, WSS, and axial plaque stress than nonculprit lesions (all p values <0.01). Among the 3 models, model 3, which included hemodynamic parameters, showed the highest c-index, and better discrimination (concordance statistic [c-index] 0.789 vs. 0.747; p = 0.014) and reclassification abilities (category-free net reclassification index 0.287; p = 0.047; relative integrated discrimination improvement 0.368; p < 0.001) than model 2. Lesions with both APC and AHC showed significantly higher risk of the culprit for subsequent ACS than those with no APC/AHC (hazard ratio: 11.75; 95% confidence interval: 2.85 to 48.51; p = 0.001) and with either APC or AHC (hazard ratio: 3.22; 95% confidence interval: 1.86 to 5.55; p < 0.001). CONCLUSIONS: Noninvasive hemodynamic assessment enhanced the identification of high-risk plaques that subsequently caused ACS. The integration of noninvasive hemodynamic assessments may improve the identification of culprit lesions for future ACS. (Exploring the Mechanism of Plaque Rupture in Acute Coronary Syndrome Using Coronary CT Angiography and Computational Fluid Dynamic [EMERALD]; NCT02374775).