JACC. Cardiovascular interventions, Volume 18, Issue 23, 1 1 2025, Pages 2833-2845 Anatomical vs Physiological Lesion Characteristics in Prediction of Acute Coronary Syndrome. Yang S, Chung JW, Park SH, Zhang J, Lee K, Hwang D, Lee KS, Na SH, Doh JH, Nam CW, Kim TH, Shin ES, Chun EJ, Choi SY, Kim HK, Hong YJ, Park HJ, Kim SY, Husic M, Lambrechtsen J, Jensen JM, Nørgaard BL, Andreini D, Maurovich-Horvat P, Merkely B, Penicka M, de Bruyne B, Ihdayhid A, Ko B, Tzimas G, Leipsic J, Sanz J, Rabbat MG, Katchi F, Shah M, Tanaka N, Nakazato R, Asano T, Terashima M, Takashima H, Amano T, Sobue Y, Matsuo H, Otake H, Kubo T, Takahata M, Akasaka T, Kido T, Mochizuki T, Yokoi H, Okonogi T, Kawasaki T, Nakao K, Sakamoto T, Yonetsu T, Kakuta T, Yamauchi Y, Taylor CA, Bax JJ, Shaw LJ, Stone PH, Narula J, Koo BK
Background
Acute coronary syndrome (ACS) arises from a complex interplay among luminal narrowing, plaque morphology, and hemodynamic environment.
Objectives
The authors aimed to compare the effectiveness of anatomy- and physiology-based ACS risk assessment.
Methods
In this international, multicenter, internal case-control study, 351 ACS patients who underwent coronary computed tomography angiography (CCTA) 1 month to 3 years before the event were analyzed. Lesions were classified as culprit or nonculprit based on invasive coronary angiography at the time of ACS. Core lab CCTA analyses assessed lesion-specific characteristics: stenosis severity, adverse plaque characteristics (APC) (low-attenuation plaque, positive remodeling, spotty calcification, napkin-ring sign), plaque burden at minimum lumen area, and changes in CCTA-derived fractional flow reserve (ΔFFRCT). Diagnostic performance in identifying culprit lesions was compared.
Results
Among 2,451 lesions, 363 (14.8%) became ACS culprits, with a median interval of 375 [95.0-644.5] days. All anatomical and simulated physiological characteristics were independently associated with culprit lesions (all P < 0.001). In identifying ACS culprit lesions, plaque burden ≥70% showed the highest sensitivity of 90.6% (87.2%-93.2%) and ΔFFRCT ≥0.10 had the highest specificity of 88.3% (86.9%-89.6%) %. Predictability was similar between ΔFFRCT and the combined degree of stenosis, the number of APCs, and plaque burden (area under the curve 0.805 [0.782-0.829] vs 0.802 [0.777-0.826]; P = 0.748), with additive discrimination towards each other.
Conclusions
Luminal narrowing, plaque quality and quantity, and local hemodynamics were independent predictors of ACS, offering specificity in physiology and sensitivity in anatomy. A comprehensive assessment of them further refined the risk prediction for future ACS. (Exploring the Mechanism of Plaque Rupture in Acute Coronary Syndrome Using Coronary CT Angiography and Computational Fluid Dynamics II [EMERALD II]; NCT03591328).
