Association of Hemodynamic Disease Severity and Distribution With Risk of Future Acute Coronary Syndrome

Background: Although physiological assessment has been used in decision-making for revascularization, its role in predicting the future risk of acute coronary syndrome (ACS) remains underexplored. Objectives: This study aims to investigate the independent and combined prognostic significance of hemodynamic disease severity and distribution in identifying ACS culprit vessels, in conjunction with lumen and plaque characteristics. Methods: The EMERALD-II study is an international, multicenter, internal case-control study enrolling 351 patients with ACS who underwent coronary computed tomography angiography (CTA) 1 month to 3 years before the event. Culprit and nonculprit vessels were identified by matching invasive coronary angiography with coronary CTA findings. High-risk plaque (HRP) characteristics, including minimum lumen area <4 mm², plaque burden ≥70%, low-attenuation plaque, positive remodeling, spotty calcification, and napkin-ring sign, were assessed by a core laboratory, with HRP defined as ≥3 HRP characteristics. From coronary CTA, the authors derived both the hemodynamic severity of the disease (fractional flow reserve derived from computed tomography [FFR_CT]) and its spatial distribution (diffuse vs focal), as assessed by the pullback pressure gradient derived from coronary CTA (PPG_CT). Vessels were categorized into 4 hemodynamic disease patterns: nonischemic (FFR_CT >0.80), hemodynamic diffuse (FFR_CT ≤0.80 and PPG_CT ≤0.50), mixed (FFR_CT ≤0.80 and 0.50 < PPG_CT ≤0.60), and focal disease (FFR_CT ≤0.80 and PPG_CT >0.60). Results: Among 873 vessels, the mean FFR_CT was 0.74 ± 0.17 and the mean PPG_CT was 0.54 ± 0.14. Both lower FFR_CT and higher PPG_CT were independently associated with higher ACS risk (OR per 0.1 increase in FFR_CT: 0.71 [95% CI: 0.65-0.77]; P < 0.001; OR per 0.1 increase in PPG: 1.22 [95% CI: 1.09-1.37]; P < 0.001). Among the 4 subgroups of hemodynamic disease pattern, hemodynamic focal disease showed the highest risk of ACS (relative risk [RR]: 2.02 [95% CI: 1.74-2.36]; P < 0.001), myocardial infarction (RR: 1.75 [95% CI: 1.43-2.14]; P < 0.001), and unstable angina (RR: 2.54 [95% CI: 2.00-3.22]; P < 0.001). It remained a predictor for ACS in nonobstructive lesions (OR: 3.56 [95% CI: 1.43-8.84]), obstructive lesions (OR: 3.16 [95% CI: 1.96-5.07]), non-HRP (OR: 6.69 [95% CI: 3.59-12.5]), and HRP (OR: 2.98 [95% CI: 1.83-4.87]). Although the maximal lesion-level ΔFFR_CT (differences in FFR_CT across the lesion) demonstrated superior model performance compared with models incorporating FFR_CT and PPG_CT, higher PPG_CT was additionally associated with increased ACS risk, particularly among vessels with maximal ΔFFR_CT ≥0.10. Conclusions: Hemodynamic disease distribution, as measured by PPG_CT, complements FFR_CT in predicting ACS risk. The integration of hemodynamic disease patterns provides additional prognostic value beyond lumen and plaque characteristics, with hemodynamic focal disease emerging as an independent predictor and a potential therapeutic target for ACS prevention.